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Texture discrimination with multidimensional distributions of signed gray level differences
... To represent the texture of an image, the classical CLBP frame work is used to combine LBP histogram different patterns into a signal histogram. As the values of the centre pixel the proposed patters are more robust to noise [2,20]. In this work, local binary pattern features are used to extract the gender information for classification of male and female using fingerprints. ...
... In local binary pattern the shape and texture of digital image features are extracted by dividing image into several regions. [2], [20]. The original LBP operator works with eight neighbour pixel, using centre pixel as threshold. ...
... Hence much informational of textural characteristics in original join distribution (eq.3) is preserved in joint difference distribution. [2]. The differences are influenced by sealing even though invariant gray scale shifts. ...
Gender classification from a fingerprint is an crucial step in forensic anthropology which plays a vital role for criminal identification which minimizes suspects searc list. A fingerprint biometric trait is one of the important traits working with good results in gender classification and identification. In this research work, local texture features are used for detection and classification of gender knowledge using fingerprints. As an experiment 400 real fingerprints were collected from different age groups of urban and rural people. An experimental observation is 95.88 % of classification rate is succeed using features of local binary pattern. The work has been analyzed and the results reported in this are found to be satisfactory and more competitive.
... After that, the key features included within the ROIs are retrieved by utilizing an enhanced version of the Sobel Directional Pattern (SDP), a methodology we are proposing here in this research to extract relevant features contained within skin images. When it comes to analyzing skin images, the Sobel Directional Pattern approach is preferable to the more conventional feature extraction strategy known as the ABCD rule [18]. As a last step, a stacked Restricted Boltzmann Machine, also known as a stacked RBM, is introduced as a solution for the classification of skin ROIs. ...
... Artificial intelligence (AI) and associated technologies are starting to be adopted by healthcare organizations as they become increasingly widespread in the industrial and medical sectors [20][21][22][23][24]. Studies [25][26][27][28][29][30][31][32][33] have proven that AI is as good as, or better than, human doctors when it comes to medical diagnosis. Recently, machine learning and deep learning algorithms [18] have been more accurate than radiologists in detecting malignant tumors. They are also aiding researchers in figuring out how to assemble study populations for costly clinical trials. ...
... The TPR for the benign group is 100%, whereas the TPR for melanoma is 94%. The confusion matrix and the categorization outcomes for the photos from the ISIC 2016 dataset are shown in Tables 4 and 5. Compared to LBP (Local Binary Pattern), CLDP (Color Local Directional Pattern) [18,[55][56][57][58] has the highest accuracy. The accuracy of 97.2 percent shows that, compared to GLCM [59], LBP more accurately captures the texture of skin cancer images [18]. ...
Melanoma, a kind of skin cancer that is very risky, is distinguished by uncontrolled cell multiplication. Melanoma detection is of the utmost significance in clinical practice because of the atypical border structure and the numerous types of tissue it can involve. The identification of mel-anoma is still a challenging process for color images, despite the fact that numerous approaches have been proposed in the research that has been done. In this research, we present a comprehensive system for the efficient and precise classification of skin lesions. The framework includes prepro-cessing, segmentation, feature extraction, and classification modules. Preprocessing with DullRazor eliminates skin-imaging hair artifacts. Next, Fully Connected Neural Network (FCNN) semantic segmentation extracts precise and obvious Regions of Interest (ROIs). We then extract relevant skin image features from ROIs using an enhanced Sobel Directional Pattern (SDP). For skin image analysis , Sobel Directional Pattern outperforms ABCD. Finally, a stacked Restricted Boltzmann Machine (RBM) classifies skin ROIs. Stacked RBMs accurately classify skin melanoma. The experiments have been conducted on five datasets: Pedro Hispano Hospital (PH2), International Skin Imaging Collaboration (ISIC 2016), ISIC 2017, Dermnet, and DermIS, and achieved an accuracy of 99.8%, 96.5%, 95.5%, 87.9%, and 97.6%, respectively. The results show that a stack of Restricted Boltzmann Machines is superior for categorizing skin cancer types using the proposed innovative SDP.
... To achieve the robust tracking of an object, appearance modeling is considered to be the back-bone as it is associated with most of the relevant information. Different region descriptors have been recommended by many researchers depending upon the types of challenges and applications [5,7,10,18,20,31]. The apposite searching of the object in the next frame with less computational load without being trapped in local minima is a challenging issue in the object tracking operation. ...
... In [10], the authors proposed an approach by combining the Gaussian Mixture Model (GMM) based object detection, a robust feature extraction method, and a Eigen vector based object counting technique. The texture information could be more efficiently expressed by combining the LBP with its strength, i.e., the local contrast measure (LCM) [20]. J. Ning et al. explored the robustness of object tracking by implementing the joint histogram of Ohta colors and the LBP texture feature [19]. ...
... The features should be strong enough for discriminating the foreground from the background. In this paper, an appearance model with joint histogram representation, based on both color and texture information of the target is considered [7,20]. Here, the texture information is represented with the combination of both Local Binary Pattern (LBP) and Local Contrast Measure (LCM). ...
In this paper, an Interactive Teaching Learning Based Optimization (In-TLBO) algorithm is proposed for tracking multiple objects with several challenges. The performance of the four other competitive approaches, such as the Mean Shift (MS), Particle Swarm Optimization (PSO), Sequential PSO (SPSO), and Adaptive Gaussian Particle Swarm Optimization (AGPSO) are investigated for comparison. The quantitative and qualitative analyses of these approaches have been performed to demonstrate their efficacy. The comparison of various performance measures includes the convergence rate, tracking accuracy, Mean Square Error (MSE) and coverage test. To assess the dominance of the proposed In-TLBO approach, Sign and Wilcoxon test are also performed. These two non-parametric tests reveal considerable advancement of the proposed Interactive TLBO (In-TLBO) over other four competitive approaches. In-TLBO shows significant improvement over the MS and PSO algorithms with a level of significance α = 0.05, and over SPSO, with a level of significance α = 0.1 by considering detection rate as winning parameter. The analyses of comparative results demonstrate that the proposed approach effectively tracks similar objects in the presence of many real time challenges.
... In this work, a U-Net architecture for the segmentation of textures is implemented and objectively compared against several popular traditional segmentation strategies. The traditional algorithms (co-occurrence matrices [5], watershed [51], local binary patterns (LBP) [52,53], filtering [54] and multiresolution sub-band filtering (MSBF) [8]) were selected as these have been previously published using the texture composites proposed by Randen [55] and thus an objective numerical comparison is possible. ...
... For this paper, we compared the results of the following texture segmentation algorithms: co-occurrence matrices [5], watershed [51], local binary patterns (LBP) [52,53], filtering [54] and multiresolution sub-band filtering (MSBF) [8] against a U-Net architecture [46]. ...
... Two advantages of LBP is that there is no need of quantising images and there is a certain immunity to low frequency artefacts. In a more recent paper, Ojala [53] presented another variation to the LBP by considering the sign of the difference of the grey-level differences histograms. Under the new consideration, LBP is a particular case of the new operator called p 8 . ...
This paper compares a series of traditional and deep learning methodologies for the segmentation of textures. Six well-known texture composites first published by Randen and Husøy were used to compare traditional segmentation techniques (co-occurrence, filtering, local binary patterns, watershed, multiresolution sub-band filtering) against a deep-learning approach based on the U-Net architecture. For the latter, the effects of depth of the network, number of epochs and different optimisation algorithms were investigated. Overall, the best results were provided by the deep-learning approach. However, the best results were distributed within the parameters, and many configurations provided results well below the traditional techniques.
... However, these techniques have been predominantly used in real-world texture analysis problems. LBP and its variants are proposed by Ojala et al. (2001). It has been used in different fields, which include texture classification, neonatal face expression recognition, stem cell classification, detection of cervical cancer etc. (Liao et al., 2007;Loris Nanni et al., 2013;Yimo Guo et al., 2012). ...
... LBP has been presented by many authors in many applications because of its computational efficiency, high discriminative power and less vulnerability to illumination changes. This method is first introduced by Ojala et al. (2001). They have considered statistics of grey level differences. ...
... There are many variants of LBP based on encodings, the shape of neighbourhood and transitions in binary code generated for each pixel. LBP variants which are based on transitions and rotation invariance include uniform LBP, rotation invariant LBP, rotation invariant uniform LBP (Ojala et al., 2001). The LBP variants which are based on the shape of the neighbourhood include elliptical binary pattern (EBP) and hyperbolic binary pattern (HBP), these variants are considered to detect anisotropic features . ...
Breast cancer is the second most prominent cancer diagnosed among women. Digital mammography is one of the effective imaging modalities used to detect breast cancer in early stages. Computer-aided detection systems help radiologists to detect and diagnose abnormalities earlier and faster in a mammogram. In this paper, a comprehensive study is carried out on different feature extraction methods for classification of abnormal areas in a mammogram. The prominent techniques used for feature extraction in this study are local binary pattern (LBP), rotation invariant local frequency (RILF) and segmented fractal texture analysis (SFTA). Features extracted from these techniques are then fed to a support vector machine (SVM) classifier for further classification via 10-fold cross-validation method. The evaluation is performed using image retrieval in medical applications (IRMA) database for feature extraction. Our statistical analysis shows that the RILF technique outperforms the LBP and SFTA techniques.
... To represent the texture of an image, the classical CLBP frame work is used to combine LBP histogram different patterns into a signal histogram. As the values of the centre pixel the proposed patters are more robust to noise [2,20]. In this work, local binary pattern features are used to extract the gender information for classification of male and female using fingerprints. ...
... In local binary pattern the shape and texture of digital image features are extracted by dividing image into several regions. [2], [20]. The original LBP operator works with eight neighbour pixel, using centre pixel as threshold. ...
... Hence much informational of textural characteristics in original join distribution (eq.3) is preserved in joint difference distribution. [2]. The differences are influenced by sealing even though invariant gray scale shifts. ...
... 13 Image level accuracy of the average of 10-fold test results for single features and hybrid approaches. ...
... 13 Plot of computation cost without clustering ...
... 13 shows the plot of computation cost without clustering. The most important time measure is during the testing phase because the training is only a onetime cost. ...
Informative Frame Filtering using Texture Analysis in Colonoscopy Videos. Doctor of Philosophy (Computer Science), December 2015, 74 pp., 12 tables, 34 figures, 52 numbered references. There are several types of disorders that affect our colon's ability to function properly such as colorectal cancer, ulcerative colitis, diverticulitis, irritable bowel syndrome and colonic polyps. Automatic detection of these diseases would inform the endoscopist of possible sub-optimal inspection during the colonoscopy procedure as well as save time during post-procedure evaluation. But existing systems only detects few of those disorders like colonic polyps. In this dissertation, we address the automatic detection of another important disorder called ulcerative colitis. We propose a novel texture feature extraction technique to detect the severity of ulcerative colitis in block, image, and video levels. We also enhance the current informative frame filtering methods by detecting water and bubble frames using our proposed technique. Our feature extraction algorithm based on accumulation of pixel value difference provides better accuracy at faster speed than the existing methods making it highly suitable for real-time systems. We also propose a hybrid approach in which our feature method is combined with existing feature method(s) to provide even better accuracy. We extend the block and image level detection method to video level severity score calculation and shot segmentation. Also, the proposed novel feature extraction method can detect water and bubble frames in colonoscopy videos with very high accuracy in significantly less processing time even when clustering is used to reduce the training size by 10 times. Copyright 2015 by Ashok Dahal ii ACKNOWLEDGEMENTS
... The filter responses are afterward associated to the nearest codeword for the convenience of histogram formation. The emergence of the local binary pattern (LBP) [11] [12] equalize the LBP derivation to the FB implementation whereby the LBP is interpreted as a filtering operator based on a set of local derivative filters. The tight connection between the LBP and the FB approaches allows the filter responses to be zero-thresholded and the LBP feature encoding is simulated to denote the extracted features in the SH representation. ...
... The non-linear hashing operator thresholds the filter responses, refer to in (11), with respect to 0 to assign a bit '1' to the positive coefficients, and a bit '0' otherwise. In what follows, the binarized filter responses are decimalized into the -bit integers, ranging from 0 to 2 1, to define as follows: ...
This paper devises a new means of filter diversification, dubbed multi-fold filter convolution (M-FFC), for face recognition. On the assumption that M-FFC receives single-scale Gabor filters of varying orientations as input, these filters are self-cross convolved by M-fold to instantiate a filter offspring set. The M-FFC flexibility also permits cross convolution amongst Gabor filters and other filter banks of profoundly dissimilar traits, e.g., principal component analysis (PCA) filters, and independent component analysis (ICA) filters. The 2-FFC of Gabor, PCA and ICA filters thus yields three offspring sets: (1) Gabor filters solely, (2) Gabor-PCA filters, and (3) Gabor-ICA filters, to render the learning-free and the learning-based 2-FFC descriptors. To facilitate a sensible Gabor filter selection for M-FFC, the 40 multi-scale, multi-orientation Gabor filters are condensed into 8 elementary filters. Aside from that, an average histogram pooling operator is employed to leverage the 2-FFC histogram features, prior to the final whitening PCA compression. The empirical results substantiate that the 2-FFC descriptors prevail over, or on par with, other face descriptors on both identification and verification tasks.
... The Local Binary Pattern (LBP) descriptor was originally developed by Ojala, Pietikainen and Harwood [48]. It describes the texture of an image and has been widely applied in diverse applications [48][49][50][51]. It assigns numeric label for the block of pixels of an image through a thresholding process that uses a 3x3 neighborhood of the center pixel value while treating the result obtained as a binary number. ...
... The LBP value is computed following the steps described in [48]. We chose the LBP descriptor because of its proficiency in appropriately describing the texture of an image [48][49][50][51][52]. In addition, it has a modest theoretical definition which is the foundation of its status as a computationally efficient image texture descriptor [53]. ...
Emotion is a complex state of human mind influenced by body physiological changes and interdependent external events thus making an automatic recognition of emotional state a challenging task. A number of recognition methods have been applied in recent years to recognize human emotion. The motivation for this study is therefore to discover a combination of emotion features and recognition method that will produce the best result in building an efficient emotion recognizer in an affective system. We introduced a shifted tanh normalization scheme to realize the inverse Fisher transformation applied to the DEAP physiological dataset and consequently performed series of experiments using the Radial Basis Function Artificial Neural Networks (RBFANN). In our experiments, we have compared the performances of digital image based feature extraction techniques such as the Histogram of Oriented Gradient (HOG), Local Binary Pattern (LBP) and the Histogram of Images (HIM). These feature extraction techniques were utilized to extract discriminatory features from the multimodal DEAP dataset of physiological signals. Experimental results obtained indicate that the best recognition accuracy was achieved with the EEG modality data using the HIM features extraction technique and classification done along the dominance emotion dimension. The result is very remarkable when compared with existing results in the literature including deep learning studies that have utilized the DEAP corpus and also applicable to diverse fields of engineering studies.
... Figure 5 shows the accuracy of the proposed method using MB-LBP and WLD on MORPH dataset. Here, we compare the accuracy of the proposed method with other existing classification method such as SIFT (Lowe 2004), Multi scale method based on morphology (Jalba et al. 2004), SD (Ojala et al. 2001), LBP (Ojala et al. 2002), scale-invariant classification (Urbach et al. 2007) and Gabor (Manjunath and Ma 1996). The Gabor filter (Manjunath and Ma 1996) approach is a traditional texture analysis method and it is utilized to analyze the global mean and standard deviation of the filter. ...
... The Gabor filter (Manjunath and Ma 1996) approach is a traditional texture analysis method and it is utilized to analyze the global mean and standard deviation of the filter. The SD (Ojala et al. 2001) approach is similar to the Gabor filter method. The fundamental idea of HFA ) method is to separate the aging variations from the person specific features for pursuing the robust age-invariant face features. ...
In our proposed research, we developed a discriminative model for addressing the challenge of face recognition with respect to age variation by utilizing periocular region. Initially, we represent each face by using a densely sampled local feature description technique, in which multi-block local binary pattern (MB-LBP) and weber local descriptor (WLD) are adopted as the local descriptors. In this approach, extract the feature of entire facial image by the aid of both local descriptors. Since the feature space of both the MBLBP and WLD is vast, we develop an advanced multi-feature discriminant analysis to process these two local feature spaces in a unified framework. By random sampling, the training set as well as the feature space, multiple linear discriminant analysis (LDA) based K-nearest neighbor (K-NN) classifiers are constructed and then combined to generate a robust decision via a fusion rule. In the proposed method, the MORPH and FG-NET datasets are utilized for the experimental evaluation. In the MORPH dataset, our proposed method is compared with existing methods such as SIFT, Multi scale method based on morphology, signed grey level difference (SD), LBP, scale-invariant classification, gabor, hidden factor analysis (HFA), modified HFA (MHFA) and learning discriminant face descriptors (LFD). The accuracy of face and periocular region for our method is 98.83% and 98.32%. The existing method such as sparse null linear discriminate analysis (SNLDA), direct LDA, principle component analysis + LDA, cross-age reference coding (CARC), HFA and sparse representation classification (SRC) are compared with the proposed method and obtained the recognition rate is 96.74% and 97.89%. In parameter evaluation we get following values: recall is 93.34% and 96.56%, precision is 95.49% and 94.76%, F-score is 94.40% and 92.17% for face and periocular region. In the FG-NET dataset, our proposed method is compared with existing methods such as SIFT, LBP, MLBP, MWLD and multi-feature discriminant analysis (MFDA). The average accuracy in face region and the periocular region is 65.32% and 68.92%. Our proposed method produce recognition rate as 98.11% and 97.87% for face region and periocular region when compared with SNLDA, DLDA, SRC, PCA + LDA and Coupled Auto-encoder Networks (CAN). In parameter evaluation we get following values: recall is 90.85% and 91.65%, precision is 92.35% and 94.56%, F-score is 91.59% and 90.84% for face and periocular region. Our Method has error rate as 1.7% and our error rate is less when contrasted with Agarwal et al. (21%), Wallis et al. (11.5%), Wang et al. (4.515%) and Leibe et al. (2.5%) techniques.
... Texture is an attribute of images that describes the variation and repetition of gray levels. Texture analysis extracts texture features through a texture descriptor, including the gray level co-occurrence matrix (GLCM) [12], [13], Gabor filter [14], local binary pattern (LBP) [15], [16], median binary pattern (MBP) [17], adaptive median binary pattern (AMBP) [18], [19]. Authors in [9] have proposed an automatic hyphae image detection method in 2016 based on LBP and support vector machine (SVM) [20] model that can separate abnormal images from normal images with 93.53% accuracy. ...
... LBP [15], [16], which was proposed by Ojala et al., is a general local texture descriptor. It is a simple but powerful texture analysis method. ...
Fungal keratitis is an inflammation of the cornea that results from infection by fungal organisms. It has a high rate of blindness, which makes the accurate diagnosis of fungal keratitis important. Confocal microscopy is an optical imaging technique that assists doctors in diagnosing fungal keratitis, and cornea images obtained by confocal microscopy can be used to detect hyphae. The current challenges are how to classify normal cornea images with nerves and abnormal cornea images with hyphae and how to detect the hyphae in a complicated background. To address this problem, this paper proposes a novel automatic hyphae detection method that assists doctors in making diagnoses. It includes two primary steps: texture classification of images and hyphae detection. In texture classification step, first, after image enhancement using a sub-regional contrast stretching algorithm, an adaptive robust binary pattern (ARBP), which is an improvement on the adaptive median binary pattern (AMBP), is proposed and adopted to extract texture features; and a support vector machine (SVM) model is used to classify the normal and abnormal images. In the hyphae detection step, binarization and a connected domain process are used to further enhance the targets, and a line segment detector (LSD) algorithm is adopted to detect the hyphae; then, the hyphal density is defined to quantitatively evaluate the infection severity. The contributions of this study include the improvement of the AMBP and the design of a novel framework. ARBP can extract effective texture features of images with relatively bright and small targets. The experimental results demonstrate the effectiveness of the proposed framework.
... It is one of the best performing texture descriptors and it has been widely used in multiple applications [36,37]. This operator was developed by Ojala et al. [38,39]. Many variants of LBP were developed, for example Heikkila et al. [40] proposed centersymmetric local binary pattern, then, Zhang et al. [41] developed a new approach replacing the neighbor pixels by the mean of the neighbors' blocks, and Wolf et al. [42] proposed novel patches based LBP where they explored the similarities between neighboring patches of pixels. ...
... However, the estimation of this distribution from image data is difficult. Ojala et al. [38] proposed to apply vector quantization given by the following formula: ...
Melanoma is one the most increasing cancers since past decades. For accurate detection and classification, discriminative features are required to distinguish between benign and malignant cases. In this study, the authors introduce a fusion of structural and textural features from two descriptors. The structural features are extracted from wavelet and curvelet transforms, whereas the textural features are extracted from different variants of local binary pattern operator. The proposed method is implemented on 200 images from PH² dermoscopy database including 160 non-melanoma and 40 melanoma images, where a rigorous statistical analysis for the database is performed. Using support vector machine (SVM) classifier with random sampling cross-validation method between the three cases of skin lesions given in the database, the validated results showed a very encouraging performance with a sensitivity of 78.93%, a specificity of 93.25% and an accuracy of 86.07%. The proposed approach outperforms the existing methods on the PH2 database.
... Thorough the experiment, the proposed method employs = = 16 yielding 48 feature dimensionality. The image retrieval performance is objectively measured in terms of Average Retrieval Rate (ARR) [1][2][3][4][5]. This experiment uses the Batik image dataset for measuring the proposed method effectiveness. ...
... This subsection reports an additional experiment to further investigate the proposed method performance compared to LBP scheme [4][5]. The image retrieval performance is measured in terms of APR for both methods under various Arnold transformation round = {0,1,5, … ,30} . ...
... Ojala et al. [21] originally proposed the standard LBP as a texture descriptor that measures neighborhood evaluation in texture analysis finds microtextons in a surrounding area. By comparing nearby pixels with the pixel in the center of them, a binary pattern is created [28]. The following is the definition of the LBP operator: ...
Biometric is the science of validating an individual’s identity while using behavioral and physiological characteristics. In unconstrained scenario, contactless palm-print recognition leads to better recognition accuracy of individuals. Most of the existing texture descriptors are fail to learn stable and discriminative features from palm-print images. The paper presents a multi-view feature learning method based on texture description for palm-print recognition. The multi-view features are simultaneously extracted by two complementary operators. We also learn how to use feature mapping to convert multi-view data into hash codes. Experiments are carried out on palm-print databases captured using a variety of devices and acquisition methods. We demonstrate that the proposed method has superior performance compared to the current methods.
... The result is forwarded to the corresponding output capsule. Authors [11] proposed a benchmark for facial manipulation detection. This publicly available benchmark is created using the most common fake creation tools: Face Swap, Face2Face, Deepfake and Neural Texture. ...
The emergence of deep fake videos in recent years has made image falsification a real danger. A person’s face and emotions are deep-faked in a video or speech and are substituted with a different face or voice employing deep learning to analyze speech or emotional content. Because of how clever these videos are frequently, Manipulation is challenging to spot. Social media are the most frequent and dangerous targets since they are weak outlets that are open to extortion or slander a human. In earlier times, it was not so easy to alter the videos, which required expertise in the domain and time. Nowadays, the generation of fake videos has become easier and with a high level of realism in the video. Deepfakes are forgeries and altered visual data that appear in still photos or video footage. Numerous automatic identification systems have been developed to solve this issue, however they are constrained to certain datasets and perform poorly when applied to different datasets. This study aims to develop an ensemble learning model utilizing a convolutional neural network (CNN) to handle deepfakes or Face2Face. We employed ensemble learning, a technique combining many classifiers to achieve higher prediction performance than a single classifier, boosting the model’s accuracy. The performance of the generated model is evaluated on Face Forensics. This work is about building a new powerful model for automatically identifying deep fake videos with the DeepFake-Detection-Challenges (DFDC) dataset. We test our model using the DFDC, one of the most difficult datasets and get an accuracy of 96%.
... So, it is better to review LBP computation process firstly. LBP is one of the most popular texture analysis operators that were introduced first time in [19]. It is a gray-scale invariant texture measure computed from the analysis of an N×N local neighborhood over a central pixel. ...
Human visual brain use three main component such as color, texture and shape to detect or identify environment and objects. Hence, texture analysis has been paid much attention by scientific researchers in last two decades. Texture features can be used in many different applications in commuter vision or machine learning problems. Since now, many different approaches have been proposed to classify textures. Most of them consider the classification accuracy as the main challenge that should be improved. In this article, a new approach is proposed based on combination of two efficient texture descriptor, co-occurrence matrix and local ternary patterns (LTP). First of all, basic local binary pattern and LTP are performed to extract local textural information. Next, a subset of statistical features is extracted from gray-level co-occurrence matrixes. Finally, concatenated features are used to train classifiers. The performance is evaluated on Brodatz benchmark dataset in terms of accuracy. Experimental results show that proposed approach provide higher classification rate in comparison with some state-of-the-art approaches.
... Other children or animals with The first line of treatment for this infection is medication, followed by homecare, which includes avoiding clothing that irritates the infected area, covering it with a dressing if you can't avoid it, washing the diseased area and clothes daily to help disinfect your environments, cleaning and drying your infected area daily. Constricting ringworm is more likely in malnourished children who have poor hygiene, live in a warm area, have contact with other children or animals with ringworm infection, or are immunocompromised due to sickness or medication [11]. ...
Fungal disease affects more than a billion people worldwide, resulting in different types of fungus diseases facing life-threatening infections. The outer layer of your body is called the integumentary system. Your skin, hair, nails, and glands are all part of it. These organs and tissues serve as your first line of defence against bacteria while protecting you from harm and the sun. The It serves as a barrier between the outside world and the regulated environment inside our bodies and a regulating effect. Heat, light, damage, and illness are all protected by it. Fungi-caused infections are found in almost every part of the natural world. When an invasive fungus takes over a body region and overwhelms the immune system, it causes fungal infections in people. Another primary goal of this study was to create a Convolutional Neural Network (CNN)-based technique for detecting and classifying various types of fungal diseases. There are numerous fungal illnesses, but only two have been identified and classified using the proposed Innovative Fungal Disease Diagnosis (IFDD) system of Candidiasis and Tinea Infections. This paper aims to detect infected skin issues and provide treatment recommendations based on proposed system findings. To identify and categorize fungal infections, deep machine learning techniques are utilized. A CNN architecture was created, and it produced a promising outcome to improve the proposed system accuracy. The collected findings demonstrated that CNN might be used to identify and classify numerous species of fungal spores early and estimate all conceivable fungus hazards. Our CNN-Based can detect fungal diseases through medical images; earmarked IFDD system has a predictive performance of 99.6% accuracy.
... In fact, LBP is an operator that provides a spatial description of the image texture. Ojala et al. [18], introduced a scientifically and computationally simple LBP-based technique that is robust with respect to changes in scale and rotation. In this pattern, first, the image texture is defined as a local neighborhood, with ( = 0.1 … .7) ...
... As such, cooccurrence matrix can be approximated using sum histogram, P I(x) + I(x ) and difference histogram, P I(x) − I(x ) . In practice, it has been shown that the difference histogram performs nearly as powerful as cooccurrence matrix in texture discrimination [46], [47]. As spectral subtraction is not physically defined, we consider instead the spectral difference ∆s (to differentiate from ∆s in J 1 ) induced by the difference between I(x) and I(x ). ...
Texture characterization from the metrological point of view is addressed in order to establish a physically relevant and directly interpretable feature. In this regard, a generic formulation is proposed to simultaneously capture the spectral and spatial complexity in hyperspectral images. The feature, named
relative spectral difference occurrence matrix
(RSDOM) is thus constructed in a multireference, multidirectional, and multiscale context. As validation, its performance is assessed in three versatile tasks. In texture classification on
HyTexiLa
, content-based image retrieval (CBIR) on
ICONES-HSI
, and land cover classification on
Salinas
, RSDOM registers 98.5% accuracy, 80.3% precision (for the top 10 retrieved images), and 96.0% accuracy (after post-processing) respectively, outcompeting GLCM, Gabor filter, LBP, SVM, CCF, CNN, and GCN. Analysis shows the advantage of RSDOM in terms of feature size (a mere 126, 30, and 20 scalars using GMM in order of the three tasks) as well as metrological validity in texture representation regardless of the spectral range, resolution, and number of bands.
... LBP is often used in the literature because it provides many advantages in textural image analysis. The advantages of LBP are as follows [7][8][9]. ...
Local descriptors are the most effective textural image recognition methods. Local descriptors generally consist of two phases. These are binary feature coding and histogram extraction phases, and they often use the signum function for the binary feature extraction. In this article, new fuzzy-based mathematical kernels are proposed for binary feature encoding in local descriptors. Fuzzy kernels consist of membership degree calculation and coding these membership degrees. In order to calculate membership degrees, four fuzzy sets are utilized. The proposed fuzzy kernels are considered as binary feature-extraction functions, and a novel textural image recognition architecture is created using these fuzzy kernels. These architecture phases are; (1) binary feature coding with fuzzy kernels, (2) calculating lower and upper images, (3) histogram extraction, (4) feature reduction with maximum pooling, (5) classification. In the classification phase, a quadratic kernel-based support vector machine (SVM) classifier is utilized. The presented fuzzy kernels are implemented on the Local Binary Pattern (LBP) and Local Graph Structure (LGS). 16 novel methods are presented using fuzzy kernels for each descriptor. In this article, LBP and LGS are used, and 32 novel fuzzy-based methods are proposed to improve recognition capability. 3 facial images and 3 textural image datasets are used to evaluate the methods' performance. The experimental results clearly illustrate that the fuzzy kernels based LBP and LGS methods have high facial and textural image recognition capability. Öz: Yerel tanımlayıcılar, en etkili dokusal görüntü tanıma yöntemleridir. Yerel tanımlayıcılar genellikle iki aşamadan oluşur. Bunlar ikili özellik kodlama ve histogram çıkarma aşamalarıdır ve genellikle ikili özellik çıkarımı için işaret işlevini kullanırlar. Bu makalede, yerel tanımlayıcılarda ikili özellik kodlaması için yeni bulanık tabanlı matematiksel çekirdekler önerilmiştir. Bulanık çekirdekler, üyelik derecesi hesaplaması ve bu üyelik derecelerinin kodlanmasından oluşur. Üyelik derecelerini hesaplamak için dört bulanık küme kullanılır. Önerilen bulanık çekirdekler, ikili özellik çıkarma işlevleri olarak kabul edilir ve bu bulanık çekirdekler kullanılarak yeni bir dokusal görüntü tanıma mimarisi oluşturulur. Bu mimari aşamalar; (1) bulanık çekirdekli ikili özellik kodlaması, (2) alt ve üst görüntülerin hesaplanması, (3) histogram çıkarma, (4) maksimum havuzlama ile özellik azaltma, (5) sınıflandırma. Sınıflandırma aşamasında, ikinci dereceden çekirdek tabanlı bir destek vektör makinesi (SVM) sınıflandırıcısı kullanılır. Sunulan bulanık çekirdekler, Yerel İkili Model (LBP) ve Yerel Grafik Yapısı (LGS) üzerinde uygulanmaktadır. Her tanımlayıcı için bulanık çekirdekler kullanılarak 16 yeni yöntem sunulmaktadır. Bu makalede, LBP ve LGS kullanılmış ve tanıma yeteneğini geliştirmek için 32 yeni bulanık tabanlı yöntem önerilmiştir. Yöntemlerin performansını değerlendirmek için 3 yüz görüntüsü ve 3 dokusal görüntü veri kümesi kullanılır. Deneysel sonuçlar, bulanık çekirdeklere dayalı LBP ve LGS yöntemlerinin yüksek yüz ve dokusal görüntü tanıma kapasitesine sahip olduğunu açıkça göstermektedir. Anahtar kelimeler: Bulanık kodlama, yerel ikili desen, yerel grafik yapısı, doku tanıma, yüz tanıma, biyometri.
... Though the traditional GLCM is a widely used method in the field of medical image TA, but its high dimensionality limits its application. In such case LBP based TA provides an alternate way [22] which has been found very efficient in texture classification. LBP operator encodes a local window pattern from an image patch, and its histogram is often treated as texture feature in classification problem. ...
... TA using LBP is first mentioned by Harwood et al. (1993), and introduced by Ojala (1996). LBP (Ojala et al., 2001) is a widely used TA method that has been found very efficient in discriminating textures of MR images (Oppedal et al., 2015). The evidence obtained from the literature suggests that the local texture information obtained from LBP-based TA can be utilized to detect disease related abnormalities that may not be perceptually visible. ...
Purpose
The study is conducted to identify the best corpus callosum (CC) sub-region that corresponds to highest callosal tissue alteration occurred due to Parkinsonism. In this regard the efficacy of local binary pattern (LBP) based texture analysis (TA) of CC is performed to quantify the changes in topographical distribution of callosal fiber connected to different regions of cortex. The extent of highest texture alteration in CC is used for differential diagnosis.
Materials and Methods
Study included subjects with Parkinson’s disease (PD) (n = 20), and atypical Parkinsonian disorders – multiple system atrophy (MSA) (n = 20), Progressive supranuclear palsy (PSP) (n = 20), and healthy controls (n = 20). For each subject, we have automated the ROI extraction within mid-sagittal CC, followed by LBP TA. Two-class support vector machine (SVM) classification for each disorder as against HC is performed using extracted LBP features like energy and entropy. Correct classification ratio (CCR) is computed as the fraction of correctly classified ROIs at each of the CC sub-regions based on well-known Witelson and Hofer schemes. Based on CCR values, the “Scatter Index (SI)” is proposed to capture how localized (closer to 0) or scattered (closer to 1) the textural changes are among the CC sub-regions, across all subjects per class. The CCR values are further utilized to classify the disease groups.
Results
Highest alteration of texture is observed in mid-body of CC. The consistency of this finding is quantified using SI for all subjects in a specific class that results more localized textural changes in PSP (15%) and MSA (25%), in comparison to PD (47%). Classification among disease groups results maximum classification accuracy of 90% in classifying PSP from PD-NC.
Conclusion
Our result demonstrates the efficacy of proposed methodology in analyzing tissue alteration in MRI of Parkinsonian disorders and thus has potential to become valuable tool in computer aided differential diagnosis.
... After described the ELBP P,R method, there is another extension of original LBP called Local Binary Pattern uniform [79] which defined by equation 3.7: ...
La technique de la biométrie est une mesure des certains caractéristiques pour l'identification ou l’authentification d'un individu. Cette technique est utilisée de plus en plus aujourd'hui pour établir la reconnaissance de la personne dans un grand nombre d’applications diverses. Bien que les techniques de reconnaissance biométrique promettent d’être très performantes. Le vieillissement du visage a un impact négatif sur les performances de reconnaissance et de vérification et d’authentification du visage. Dans notre travail, nous avons présenté une analyse approfondie pour l'estimation automatique de l'âge des visages. Nous avons discuté les différents opérateurs d’extraction des caractéristiques utilisés dans l’estimation de l'âge.
... The LBP is an image operator which transforms an image into integer labels then we converted this integer to an unique number used as primary key. These labels, most commonly the histogram, are then used for image analysis [15]. ...
In the recent years institutes and companies has multiple locations in different places and the number of employees is increasing, also the system of working became in multiple shifts, so it is necessary to built system work in real time to manage the attendance of employees remotely using internet and face recognition features. Our system is consist of microcontroller LPC ARM 2148 development board interfaced with OM13059 flexible Camera solution for face recognition, the LPC ARM 2148 is connected to wide area network or internet. In the main office of the company there is server contain the database designed by SQL server connected to PHP page for all employees identified by number which represent the picture processed by ARM7 using developed LBP algorithm to indicate each face after converting it to LBP by unique number ID. In our design the Camera will take picture and processed using ARM7 with LBP algorithm to look for the face detected by camera in designed board if it is match then extract numbers saved in internal EPROM of ARM7 send through Ethernet to server by internet using its public IP, the server will start transaction with the record of the employee faced. In this paper we used matlab to simulate the proposed algorithms to calculate the accuracy of our system then we get performance is 80% and then we get less delay time to accomplish attendance recording if we used 10 hardware designed boards for each branch of the company.
... The Local Binary Pattern (LBP) is an image texture element that was proposed by Ojala et al. (2001) and Ojala et al. (2002) in their research on image processing. The LBP technique has been adopted by prior studies such as (Li and Staunton, 2008) to achieve a consistent detector of image texture. ...
Existing scanners produce paper images with different types of deformations such as noise, rotation and shear. These deformations affect the accuracy of the fingerprinting the document images, which entails utilizing advanced feature extraction operators. Existing feature extractor such as the Uniform Local Binary Patterns (ULBP) has been found to be limited in dealing with the global view of the texture and neglecting useful information about the images. This article presents an Automated Paper Fingerprinting (APF) method that deploys a combination approach for Gabor Filters (GF) and Uniform Local Binary Patterns (ULBP) called the GFULBP operator to cater for both local and global image information during the feature extraction process for higher texture classification accuracy. The APF method is evaluated by a standard dataset of 306 blank paper images derived from pre-existing scanner image dataset from Universiti Kebangsaan Malaysia (UKM) with properties ranges from 50 DPI, 100 DPI, and 150 DPI respectively. The images are captured by a flatbed scanner with 50 DPI, 100 DPI, and 150 DPI resolutions. Each image is represented by four patches that are segmented from specific locations of the image. The test results of the APF show that GFULBP is able to outperform the ULBP alone by 30.68% when the GF has a 5 scale and π/2 orientation degree. This work finds that the integration of Gabor filters and ULBP significantly enhances the feature extraction quality and fingerprinting accuracy.
... In single view still image applications, a number of binary operators are used to handle non-uniform texture regions that arises from the sudden illumination changes. Of these the Local Binary Pattern (LBP) operator transforms these images captured under radiometric variations to an illumination invariant one in a better manner (Ojala et al., 2001). This work incorporates simpler and accurate LBP method for the radiometric invariant conversion of stereo images. ...
... The pioneering binary descriptor can be traced back to Ojala T proposed LBP [19][20][21], in which, the feature pattern distribution is used to achieve texture description. Due to effectiveness and simple computation, LBP-based approaches attract extensive attention, and some noise tolerant [22], rotation-invariant [20,23], non-redundant [24,25] and discriminative [26] LBP descriptors are further proposed to improve the robustness and distinctiveness of conventional LBP descriptors. ...
Based on pairs of spatial symmetric patches, a novel efficient and distinctive binary descriptor is proposed in this paper for rotated circular image recognition. To achieve rotation invariance during feature computation, a local coordinate system is first found with radial transform technology. On the basis of that, local binary patterns against rotation can be extracted. Meanwhile, the circular image is divided into a set of overlapped annular regions, and pairs of patch description are constructed with histogram within the ring. Finally, the rotation-invariant image description can be obtained by concatenating the ring features from inner to outer. The performance of proposed method is tested with three datasets, i.e., the euro coins, QQ expression and car logo. The test results show that its recognition accuracies reach 100, 100 and 97.07%, respectively, which are superior to the results of traditional methods based on LBP feature. And our method presents competitive performance contrasted to recently proposed LBP invariants, i.e., R_LBP, CS_LBP and PRI_COLBP, and conventional floating and binary descriptors, such as SIFT, SURF, BRISK and FREAK. Moreover, the algorithm is efficient, as single-point feature computation needed only 0.045 ms.
... Therefore, the original joint gray level distribution T is dependent on joint difference distribution tðg 0 À g c ; g 1 À g c ; . . .; g LÀ1 À g c Þ [31], ...
The recognition of staining patterns present in human epithelial type 2 (HEp-2) cells helps to diagnose connective tissue disease. In this context, the paper introduces a robust method, termed as CanSuR, for automatic recognition of antinuclear autoantibodies by HEp-2 cell indirect immunofluorescence (IIF) image analysis. The proposed method combines the advantages of a new sequential supervised canonical correlation analysis (CCA), introduced in this paper, with the theory of rough hypercuboid approach. While the proposed CCA efficiently combines the local textural information of HEp-2 cells, derived from various scales of rotation-invariant local binary patterns, the relevant and significant features of HEp-2 cell for staining pattern recognition are extracted using rough hypercuboid approach. Finally, the support vector machine, with radial basis function kernel, is used to recognize one of the known staining patterns present in IIF images. The effectiveness of the proposed staining pattern recognition method, along with a comparison with related approaches, is demonstrated on MIVIA, SNP and ICPR HEp-2 cell image databases. An important finding is that the proposed method performs significantly better than state-of-the art methods, on three HEp-2 cell image databases with respect to both classification accuracy and F1 score.
... Junhua Yan received her BSc, MSc, and PhD degrees from Nanjing University of Aeronautics and Astronautics in 1993, 2001, respectively. She is a professor at Nanjing University of Aeronautics and Astronautics. ...
Considering the relatively poor real-time performance when extracting transform-domain image features and the insufficiency of spatial domain features extraction, a no-reference remote sensing image quality assessment method based on gradient-weighted spatial natural scene statistics is proposed. A 36-dimensional image feature vector is constructed by extracting the local normalized luminance features and the gradient-weighted local binary pattern features of local normalized luminance map in three scales. First, a support vector machine classifier is obtained by learning the relationship between image features and distortion types. Then based on the support vector machine classifier, the support vector regression scorer is obtained by learning the relationship between image features and image quality scores. A series of comparative experiments were carried out in the optics remote sensing image database, the LIVE database, the LIVEMD database, and the TID2013 database, respectively. Experimental results show the high accuracy of distinguishing distortion types, the high consistency with subjective scores, and the high robustness of the method for remote sensing images. In addition, experiments also show the independence for the database and the relatively high operation efficiency of this method.
... There have been several proposed approaches to new image feature descriptors that improve image retrieval performance accuracy (Bianconi et al. 2009;Campana and Keogh 2010;Chen et al. 2010;Guha and Ward 2014;Guo et al. 2015aGuo et al. , 2010Hoang and Geusebroek 2002;Hoang et al. 2005;Junior et al. 2014;Kokare et al. 2005;Uhl 2008, 2010;Lasmar and Berthoumieu 2014;Liu et al. 2011;Lowe 2004;Manjunath and Ma 1996;Murala et al. 2012;Ojala et al. 1996Ojala et al. , 2001Paschos and Petrou 2003;Porebski et al. 2008;Satpathy et al. 2014;Subrahmanyam et al. 2012Subrahmanyam et al. , 2013Tan and Triggs 2010;Zhang et al. 2010). Improvement of image retrieval performance in the color or grayscale domain has also been examined (Wang and Wang 2013). ...
With the advent of mobile smart devices and ubiquitous network connections, digital images can now be conveniently captured, edited, and shared online worldwide. The ever-increasing number of pictures poses a technically challenging, yet unavoidable problem of efficiently and accurately finding a desired image. In this work, we develop a new image retrieval system based on an image compression technique, namely dot-diffused block truncation coding (DDBTC) with bit probability. Specifically, the color feature derived from color distribution and the bitmap feature (BF) derived from both edges and textures jointly describe the image. The degree of similarity between two images is then measured by their respective color and BFs by using the modified Canberra distance metric. Experimental results show that the proposed feature descriptor achieves superior image retrieval performance as compared to the former DDBTC image retrieval feature and conventional non-DDBTC based features. Additional experiments on image classification verify that the proposed feature descriptor outperforms conventional image classification methods.
... Another aspect of change detection is texture feature. LBP, advanced in its rotational invariance and brightness invariance, is adopted to improve detection precision and eliminate influence of different illumination [ [16], [18]]. In this paper, P=8, R=1. Figure 2 shows the sampling range of LBP in this paper. ...
... After described the ELBP P,R method, there is another extension of original LBP called Local Binary Pattern uniform [79] which defined by equation 3.7: ...
Because of its natural and non-intrusive interaction, identity verification and recognition using facial information is among the most active areas in computer vision research. Unfortunately, it has been shown that conventional 2D face recognition techniques are vulnerable to spoof attack, where a person tries to masquerade as another one by falsifying his biometric data and thereby gaining an illegitimate advantage. This thesis explores different directions for software-based face anti-spoofing. In this context, we proposed a new approach which can be applied in both static and dynamic face antispoofing. The proposed approach consists of the following three main stages: 1) face alignment and preprocessing; 2) feature extraction and selection; 3) classification. The purpose of face alignment is to localize faces in images, rectify the 2D pose of each face and then crop the region of interest. The preprocessing stage is important since the subsequent stages depend on it and since it can affect the final performance of the system. Feature extraction and selection stage extract the facial features. These features are extracted either by a holistic method or by a local method. The extracted features are then selected using a supervised feature selection method in order to omit possible irrelevant features. In the last stage, the classification is used to differentiate between real and fake faces.
... [88] has been extended in several ways, such as neighborhoods with different sizes [83], multi-resolution [70], uniform patterns [83], etc. The extended LBP operator can be used for rotation invariant texture classification, and has a wide application, such as texture analysis and classification [56,68,69,71,81,82,83,84,85,87,90,93,94], face detection and recognition [2,3,11,31,45,54,113,117], image retrieval [115], etc. The LBP algorithms yield good classification results on large and complex databases [124,92]. ...
... The original LBP operator [20] has been extended in several ways, such as neighborhoods with different sizes [21], multi-resolution [17], uniform patterns [21], etc. The extended LBP operator can be used for rotation invariant texture classification, and has a wide application, such as texture analysis and classification [14], [15], [16], [19], [21], [22], [23], [24], [34], [35], [37], [38], face detection and recognition [8], [7], [9], [10], [11], [13], [26], [28], [36] image retrieval [27], etc. The LBP algorithms yield good classification results on large and complex databases [25]. ...
One of the recently invented theoretically simple and efficient operators that is playing a significant role in image processing, pattern recognition and other domains is the Local Binary Pattern (LBP). LBP measures the local texture features on a given neighbourhood efficiently. The Uniform LBP (ULBP) that is derived on LBP is treated as the fundamental unit of the textures. There 58 ULBP’s out of 256 LBP’s on a 3 x3 neighbourhood. Further the textures on average contain 75% to 90% of the patterns as ULBP only. One disadvantage is representing all 58 ULB’S. To say whether a given LBP is uniform or not one should march the given LBP with 58 Patterns. Very few research scholars derived grammar to represent patterns that arises from 2-D images. The present paper addresses the above problem by generating a simple array grammar that represents all ULBP on a 3 x 3 neighbourhood or with 8 neighbours. The present paper tests the proposed Array Grammar model of ULBP (AG-ULBP) with various LBP patterns to prove its accuracy.
... Le descripteur LBP a été proposé initialement par Ojala et al. [104,105]. L'opérateur décrit chaque pixel par la valeur relative des niveaux de gris des 8 pixels voisins (voir la Figure 3.12). Si la valeur du niveau de gris du pixel voisin est supérieure ou égale à celle du pixel central, on lui attribue la valeur de 1, sinon 0. Les valeurs binaires associées aux voisins sont alors lues de façon séquentielle, dans le sens horaire depuis un point de référence, pour former une suite binaire qui est utilisée pour caractériser la texture locale. ...
Ce travail s’inscrit dans la thématique de la reconnaissance de visages. Il s’agit de décider de manière automatique de l’identité d’une personne en fonction des traits caractéristiques de son visage. Nous présentons une approche bimodale 2D-3D qui combine des caractéristiques visuelles et de profon- deur, afin d’améliorer la précision et la robustesse de la reconnaissance par rapport aux approches monomodales classiques. Dans un premier temps, une méthode d’acquisition 3D par reconstruction stéréoscopique dédiée aux visages est proposée. Cette méthode s’appuie sur un modèle actif de forme permettant de tenir compte de la topologie du visage. Ensuite, un nouveau descripteur DLBP (Depth Local Binary Patterns) est défini pour caractériser les informations de profondeur. Ce descripteur étend aux images de profondeur les LBP traditionnels utilisés pour décrire les textures. Enfin, une stratégie de fusion bi-niveaux est proposée, permettant une combinaison à la fois précoce et tardive des deux modalités. Des expérimentations menées sur différentes collections publiques de tests, ainsi que sur une collection spécialement élaborée pour les besoins de l’évaluation, ont permis de valider les contributions proposées dans le cadre de ce travail. En particulier, les résultats ont montré d’une part la qualité des données obtenues à l’aide de la méthode de reconstruction, et d’autre part un gain de précision obtenu en utilisant le descripteur DLBP et la fusion bi-niveaux.
... LBP which was originally aimed to help measuring the local contract is a simple yet efficient and rotation invariant operator to describe local image pattern. On the basis of their early research [8][9][10], Ojala extended the IBP to a multidimensional distribution of Signed Gray Level Difference (SGLD) [11] which can be applied to any scaled structure texture. LBP made many good results in early texture classification experiments. ...
This paper proposes a local texture feature descriptor which fuses the center pixel information into the Center-Symmetric Local Binary Pattern (CS-LBP) for the purpose of face recognition. Because of its tolerance to illumination changes, and computational efficiency, the CS-LBP is widely used in face recognition. But this operator completely ignores the center pixel information which may affect the discriminative result in some applications. In order to take advantage of more useful information, this paper fuses the center pixel information into CS-LBP descriptor, namely CS-LBP/Center. In face recognition, the face image is first divided into small blocks from which CS-LBP/Center histograms are extracted and then weighted by image entropy. Finally, all the weighted histograms are connected serially to create a final texture descriptor for face recognition. The experimental results on some face datasets show that a higher recognition accuracy can be obtained by employing the proposed method with nearest neighbor classification.
... Some researchers improved the threshold mechanism of LBP[34]to sustain against noise. The other approaches used vector quantization[38,39,40]to reduce the dimensionality. A good classification results are obtained by LBP and many of LBP-like methods on different representative texture databases, they still have limitations. ...
Background
Cytochrome P450, family 1, subfamily A, polypeptide 1 (CYP1A1) is an imperative enzyme due to its immersion in the biotransformation of a wide range of drugs and other xenobiotics. The involvement in drug metabolism suggests this enzyme an effective drug target for the development of novel therapeutics. The discovery of CYP1A1 specific inhibitors would be of particular relevance for the clinical pharmacology.
Method
In current work, in silico approaches were utilized to identify the novel potential compounds through a diverse set of reported inhibitors against CYP1A1. A dataset of reported compounds against CYP1 belongs to 10 different classes (alkaloids, coumarins, flavonoids, natural compounds, synthetic inhibitors, drugs, MBI’s, PAHs, naphthoquinone and stilbenoids) was retrieved and utilized for the comparative molecular docking analyses followed by pharmacophore modeling. The total eleven novel compounds were scrutinized on the basis of highest binding affinity and least binding energy values. ZINC08792486 attained highest gold fitness score of 90.11 against CYP1A1 among the scrutinized molecules.
Result
It has been elucidated that the residues Phe-224, Gly-316 and Ala-317 are the common binding residues in all ligand-receptor interactions and critical for developing effective therapies.
Conclusion
The ADMET analyses also predict the better absorption and distribution of the selected hits that may be used in future for in vitro validations and drug development.
This paper compares a series of traditional and deep learning methodologies for the segmentation of textures. Six well-known texture composites first published by Randen and Hus{\o}y were used to compare traditional segmentation techniques (co-occurrence, filtering, local binary patterns, watershed, multiresolution sub-band filtering) against a deep-learning approach based on the U-Net architecture. For the latter, the effects of depth of the network, number of epochs and different optimisation algorithms were investigated. Overall, the best results were provided by the deep-learning approach. However, the best results were distributed within the parameters, and many configurations provided results well below the traditional techniques.
Partial discharge (PD) pattern recognition plays an important role in determining insulation defects and understanding insulation condition of transformers. In this paper, four PD models are set up in laboratory and pulse current method is used to measure the amplitude of apparent charge, the power frequency phase of PD pulses and the frequency of PD pulses. There are 19 feature parameters which include fractal features, moment features, and textural features are extracted form grey-scale images of phase resolved partial discharge (PRPD) patterns. In order to reduce the computational complexity of the classifier, principal component analysis (PCA) is used to reduce the dimensions of feature parameters and five new feature parameters which explain 93.50% of total variance are obtained. The kernel function and shared nearest neighbors (SNN) are used to improve affinity propagation (AP) algorithm. A classifier based on improved AP with particle swarm optimization (PSO) is established for PD pattern recognition in transformers. Based on these new feature parameters, the PD patterns are recognized by AP classifier, back propagation neural networks (BPNN) and least squares support vector machine (LSSVM). Recognition rate of AP classifier is 85%
The computer and image processing has a significant role in detecting tumor area. The decision support systems for human brain MR images are essentially encouraged with the requirement of attaining maximal achievable efficiency and the motivation of the approach which is to enhance the performance of Computer-Aided Diagnosis (CAD) system to detect a tumor in the human brain. Even though numerous support systems have been introduced in the past, this is still an open problem seeking for an accurate and robust decision support system. The Interactive Diagnosis Support System (IDSS) approach has addressed the limitations of nonillumination and low contrast of a brain tumor MR image that influences the procedure of accurate image classification. Thus, the IDSS is implemented in three phases namely image preprocessing for enhancing non-illuminated features, feature extraction and image classification which is accomplished using two-stage interactive SVM Classification. The local binary patterns are detected in the feature extraction for accurate classification of usual and unusual brain MR Images. The experimental outcomes for this approach are carried out using MATLAB R2016a and evaluated using the brain images downloaded from the Internet. The performance metrics such as structured similarity index, sensitivity, specificity and accuracy were used to assess the IDSS-based tumor classification system. When compared with the traditional classifiers such as ANFIS, Backpropagation and K-NN, the IDSS approach has significant brain tumor classification accuracy.
Постановка проблемы: Обнаружение динамических текстур на видеоизображениях в настоящее время находит все более широкое применение в системах компьютерного зрения. Например, обнаружение дыма и пламени в системах экологического мониторинга, анализ автомобильного трафика при мониторинге загруженности дорог, и в других системах. Поиск объекта интереса на динамическом фоне часто бывает затруднен за счет похожих текстурных признаков или признаков движения у фона и искомого объекта. В связи с этим возникает необходимость разработки алгоритма классификации динамических текстур для выделения объектов интереса на динамическом фоне.Методы: распознавание образов, компьютерное зрение.Результаты: В данной работе рассматривается обработка видеоизображений содержащих объекты с динамическим поведением на динамическом фоне, такие как вода, туман, пламя, текстиль на ветру и др. Разработан алгоритм отнесения объектов видеоизображения к одной из четырех предлагаемых категорий. Извлекаются признаки движения, цветовые особенности, фрактальности, энергетические признаки Ласа, строятся ELBP-гистограммы. В качестве классификатора использован бустинговый случайный лес.Практическая значимость: Разработан метод, позволяющий разделить динамические текстур на категории: по типу движения (периодическое и хаотичное) и типу объектов интереса (природные и искусственные). Экспериментальные исследования подтверждают эффективность предложенного алгоритма для отнесения объектов изображения к той или иной категории. Средняя точность классификации составила 95.2%.
Large image archives formed by satellite remote sensing missions are getting an increasing valuable source of information in Geographic Information Systems (GIS). The need for retrieving a required image from a huge image database is increasing significantly for the purpose of analyzing resources in GIS. Content Based Geographic Image Retrieval (CBGIR) in the image processing field is the best solution to meet the requirement. In this work, we used Local Vector Pattern (LVP) to extract fine features present in the geographical image and retrieve the applicable images from a large remote sensing image database. The primary idea of our method is generating micro patterns of LVP by the vectors of each pixel that are constructed by calculating the values between the centre pixels and its neighbourhood pixels with various distances of different directions. Then the proposed method was designed for concatenating these vector patterns to produce more unique features of geographical images and comparing the results with Local Binary Pattern (LBP), Local Derivative Pattern (LDP) and Local Tetra Pattern (LTrP). Ultimately, the extensive analysis carried out on different geographical image collections proved that the proposed method achieves the improved classification accuracy and better retrieving results.
In recent years, a rapid increase in the size of digital image databases has been observed. Everyday gigabytes of images are generated. Consequently, the search for the relevant information from image and video databases has become more challenging. To get accurate retrieval results is still an unsolved problem and an active research area. Content-based image retrieval (CBIR) is a process in which for a given query image, similar images are retrieved from a large image database based on their content similarity. A number of techniques have been suggested by researchers for content-based image retrieval. In this chapter, a review of some state-of-the-art retrieval techniques is provided.
Both the Local Binary Pattern (LBP) and the Coordinated Clusters Representation (CCR) are two methods used successfully in the classification and segmentation of images. They look very similar at first sight. In this work we analyze the principles of the two methods and show that the methods are not reducible to each other. Topologically they are as different as a sphere and a torus. In extracting of image features, the LBP uses a specific technique of binarization of images with the local threshold, defined by the central pixel of a local binary pattern of an image. Then, the central pixel is excluded of each local binary pattern. As a consequence, the mathematical basis of the LBP method is more limited than that of the CCR. In particular, the scanning window of the LBP has always an odd dimensions, while the CCR has no this restriction. The CCR uses a binarization as a preprocessing of images, so that a global or a local threshold can be used for that purpose. We show that a classification based on the CCR of images is potentially more versatile, even though the high performance of both methods was demonstrated in various applications.
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