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Abstract and Figures
With the aggravation of human sub-health problems, people’s demand for medical assistance is increasing. In the face of an endless stream of diseases, doctors use medical image analysis to intuitively obtain the morphological information of the affected part of the disease, which is convenient for doctors to make a more accurate assessment of the disease. The processing of medical images is essential for the treatment of people’s diseases and subsequent observation and recovery. Therefore, it is necessary to pay attention to the development and innovation of image processing. With the continuous increase of diseases and the innovation and development of technology, the existing medical image processing technology still has problems such as noise and low image contrast. The application of fuzzy genetic clustering algorithms and artificial neural networks can help image processing be more accurate and perfect. In view of the above problems, this paper has carried out data analysis and research on image processing methods based on fuzzy genetic clustering algorithm (FGCA) and artificial neural network (ANN). The research results have shown that in the case of no noise and 5% salt and pepper noise, the FGCA segmentation coefficient is the largest, at 0.9756 and 0.9758, respectively, and the segmentation entropy is the smallest, at 0.0885 and 0.0925, respectively. The liver CT (Computer Tomography) image segmentation method based on DeepLab V3+ has the highest PA, Mlou value, and Dice coefficient, which are 88%, 95%, and 94%, respectively, which has laid the foundation for the innovation and development of image processing methods.
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Autonomous driving demands accurate perception and safe decision-making. To achieve this, automated vehicles are typically equipped with multiple sensors (e.g., cameras, Lidar, etc.), enabling them to exploit complementary environmental contexts by fusing data from different sensing modalities. With the success of Deep Convolutional Neural Network (DCNN), the fusion between multiple DCNNs has been proved to be a promising strategy to achieve satisfactory perception accuracy. However, existing mainstream DCNN fusion strategies conduct fusion by simply element-wisely adding feature maps extracted from different modalities together at various stages, failing to consider whether the features being fused are matched or not. Therefore, we first propose a feature disparity metric to quantitatively measure the degree of feature disparity between the fusing feature maps. Then, we propose a Fusion-filter as the Feature-matching techniques to tackle the feature-mismatching issue. We also propose a Layer-sharing technique in the deep layer of the DCNN to achieve high accuracy. With the assistance of feature disparity working as an additional loss, our proposed technologies enable DCNN to learn corresponding feature maps with similar characteristics and complementary visual context from different modalities. Evaluations demonstrate that our proposed fusion techniques can achieve higher accuracy on KITTI dataset with less computation resources consumption.
Computer vision is one of the fields of computer science that is one of the most powerful and persuasive types of artificial intelligence. It is similar to the human vision system, as it enables computers to recognize and process objects in pictures and videos in the same way as humans do. Computer vision technology has rapidly evolved in many fields and contributed to solving many problems, as computer vision contributed to self-driving cars, and cars were able to understand their surroundings. The cameras record video from different angles around the car, then a computer vision system gets images from the video, and then processes the images in real-time to find roadside ends, detect other cars, and read traffic lights, pedestrians, and objects. Computer vision also contributed to facial recognition; this technology enables computers to match images of people’s faces to their identities. which these algorithms detect facial features in images and then compare them with databases. Computer vision also play important role in Healthcare, in which algorithms can help automate tasks such as detecting Breast cancer, finding symptoms in x-ray, cancerous moles in skin images, and MRI scans. Computer vision also contributed to many fields such as image classification, object discovery, motion recognition, subject tracking, and medicine. The rapid development of artificial intelligence is making machine learning more important in his field of research. Use algorithms to find out every bit of data and predict the outcome. This has become an important key to unlocking the door to AI. If we had looked to deep learning concept, we find deep learning is a subset of machine learning, algorithms inspired by structure and function of the human brain called artificial neural networks, learn from large amounts of data. Deep learning algorithm perform a task repeatedly, each time tweak it a little to improve the outcome. So, the development of computer vision was due to deep learning. Now we'll take a tour around the convolution neural networks, let us say that convolutional neural networks are one of the most powerful supervised deep learning models (abbreviated as CNN or ConvNet). This name ;convolutional ; is a token from a mathematical linear operation between matrixes called convolution. CNN structure can be used in a variety of real-world problems including, computer vision, image recognition, natural language processing (NLP), anomaly detection, video analysis, drug discovery, recommender systems, health risk assessment, and time-series forecasting. If we look at convolutional neural networks, we see that CNN are similar to normal neural networks, the only difference between CNN and ANN is that CNNs are used in the field of pattern recognition within images mainly. This allows us to encode the features of an image into the structure, making the network more suitable for image-focused tasks, with reducing the parameters required to set-up the model. One of the advantages of CNN that it has an excellent performance in machine learning problems. So, we will use CNN as a classifier for image classification. So, the objective of this paper is that we will talk in detail about image classification in the following sections.
Computer vision is one of the fields of computer science that is one of the most powerful and persuasive types of artificial intelligence. It is similar to the human vision system, as it enables computers to recognize and process objects in pictures and videos in the same way as humans do. Computer vision technology has rapidly evolved in many fields and contributed to solving many problems, as computer vision contributed to self-driving cars, and cars were able to understand their surroundings. The cameras record video from different angles around the car, then a computer vision system gets images from the video, and then processes the images in real-time to find roadside ends, detect other cars, and read traffic lights, pedestrians, and objects. Computer vision also contributed to facial recognition; this technology enables computers to match images of people's faces to their identities. which these algorithms detect facial features in images and then compare them with databases. Computer vision also play important role in Healthcare, in which algorithms can help automate tasks such as detecting Breast cancer, finding symptoms in x-ray, cancerous moles in skin images, and MRI scans. Computer vision also contributed to many fields such as image classification, object discovery, motion recognition, subject tracking, and medicine. The rapid development of artificial intelligence is making machine learning more important in his field of research. Use algorithms to find out every bit of data and predict the outcome. This has become an important key to unlocking the door to AI. If we had looked to deep learning concept, we find deep learning is a subset of machine learning, algorithms inspired by structure and function of the human brain called artificial neural networks, learn from large amounts of data. Deep learning algorithm perform a task repeatedly, each time tweak it a little to improve the outcome. So, the development of computer vision was due to deep learning.
Most digital images have uncertainties associated with the intensity levels of pixels and/or edges. These uncertainties can be traced back to the acquisition chain, to uneven lighting conditions used during imaging or to the noisy environment. On the other hand, intuitionistic fuzzy hypergraphs are considered a useful mathematical tool for digital image processing since they can represent digital images as complex relationships between pixels and model uncertain or imprecise knowledge explicitly. This paper presents the approach for noisy color image segmentation and edge detection based on intuitionistic fuzzy hypergraphs. First, the RGB image is transformed to the HLS space resulting in three separated components. Then each component is intuitionistically fuzzified based on entropy measure from which an intuitionistic fuzzy hypergraph is generated automatically. The generated hypergraphs will be used for denoising, segmentation, and edges detection. The first experimentations showed that the proposed approach gave good results especially in the case of dynamic threshold.
Problem statement . An unmanned vertical take-off and landing aircraft equipped with means for analyzing parameters of a certain pattern is usually not able to land on a landing site that is not equipped with the appropriate markings. To solve this problem, it is possible to install onboard the UMA means for generating a special test image that is projected from the aircraft to the intended landing site, and means for automatically measuring and analyzing the parameters of this image.
Objective . Develop an algorithm for applying digital image processing to determine the angular position of the ship’s deck or any other flat landing ground from an unmanned aircraft with vertical take-off and landing. The article presents the calculated relations that allow calculating the angular position and evaluating the general condition of the landing ground using a 3D model of its surface.
Results . An algorithm has been developed according to which a particular test image is generated by a group of laser emitters on the landing site. Digital image processing methods are used to analyze the test image to assess the condition and determine the inclination angles of the landing site.
Practical implications . The test results of the considered algorithm showed the possibility of its application in the system of automatic landing of an unmanned aircraft with vertical take-off and landing.
The mechanism of three-point thick plate bending finished via the specially designed experiment device in this paper. The deformation process discretization analysis completed via gridding method. T2 copper thick plate was chosen as the subject for the experiment, and the ratio of punch radius to sheet thickness was R/t = 1 and 2. The surface of the specimen was meshed by use of fine ink pen. The forming process was accomplished on the experimental facility of three-point free of sheet metal bending. And photos of the specimen in the forming process were recorded on every moment by use of digital camera. The grid pictures of specimen were gotten by image processing, and the accuracy is comparable with finite element analysis. Compared with the grid pictures, the inside and outside of the thick plate bending angle wasn’t strict equaled in the forming process, and there was a trend that the angle would increase gradually from inside to outside. The angle difference would be decreased under the effect of the correction force after spring-back.
The elemental ingredients of deciduous specimens have been analyzed using Laser incited breakdown spectroscopy method (LIBS). Many elements have been discovered in the deciduous teeth samples, the important once are Ca, P, Mg, C and Na. The test of caries part of teeth samples contained less amount of Ca, P, Na, and higher amount of C and Mg than in healthy parts. The atomic line intensities of detected elements using laser with wavelength (=532nm) showed higher intensities than lines using wavelength (=1064nm). From these results it is clear that the technique of LIBS is an effective tool for fast identification of teeth problems, such as the controlling of the drilling and cleaning processes. The estimated average value of the plasma temperature calculated from natural Ca is Te=1.324086 eV. On the other hand, dental cavities were extracted using a proposed algorithm using the MATLAB environment. The proposed algorithm based on a proposed threshold is established on the statistical characteristics of the images (minimum value, standard deviation, highest image value). The results obtained indicate a high accuracy in isolating the tooth decay area if compared with the result of the K-mean algorithm was thresholded by the Otsu threshold.
Here, in this study we can learn about Bird species recognition. In forest areas cameras are fixed at various locations which capture images periodically. From those images the birds living in such dense forest areas can be identified. It would be useful if we can able to classify the species of birds with the help of those images. But that is not an easy task because of the variations in the light effects, illumination and camera viewpoints. So we need to involve image processing techniques for preprocessing the captured image and also deep learning techniques are to be implemented for classifying the images. For classification purpose training is to be done with the help of image data set. Here we propose a method of discriminating birds by means of the ratio of the distance between eye and beak to that of the beak width. By combining this mythology with image processing and SVM classification technique a new bird species recognition algorithm is proposed. The proposed new methodology will improve the accuracy in classifying.
Most ecommerce companies have their receive to pay process as predominantly manual, leading to non-reliability of payments & delayed visibility for sellers and requirement of additional manpower for scaling up for buyers. With the correct image and pdf processing tools, it is possible to automate this process for more efficient and cost-effective results. The research paper focuses on automating the task of invoice processing which is predominantly done manually. The idea is to save time, effort, and costs while eliminating human errors from the process. There are several existing image and pdf processing tools of which we will discuss pdftotext, tesseract and tesseract4.