Image Recognition - Science topic

The purpose of this research is to find out several factors: 1. The role of eyebrows within the face 2. Relevant literature accuracy: Galton and Darwin’s analysis on facial recognition 3. Extend findings into mathematical models and proof of the entire facial imaging system Evaluation of relevant literature was given and compared, especially on rel...

Image recognition has been widely used in various fields of applications such as human-computer interaction, where it can enhance fluency, accuracy, and naturalness in interaction. The need to automate the decision on human expression is high. This paper presents a technique for emotion recognition and classification based on a combination of deep-...

Capsule networks are deep neural networks that perform a part-to-whole association and instantiate the parameters of a “whole” (e.g., a class) by searching the agreement of “parts”. These networks are based on grouping neurons into units called capsules. The activity of “part” capsules is propagated to the “whole” capsules in the next layer by a tr...

Image recognition and quality assessment are two important viewing tasks, while potentially following different visual mechanisms. This paper investigates if the two tasks can be performed in a multitask learning manner. A sequential spatial-channel attention module is proposed to simulate the visual attention and contrast sensitivity mechanisms th...

Conventional authentication methods, like simple text-based passwords, have shown vulnerabilities to different types of security attacks. Indeed, 61% of all breaches involve credentials, whether stolen via social engineering or hacked using brute force. Therefore, a robust user authentication mechanism is crucial to have secure systems. Combining t...

Image recognition and classification is a significant research topic in computational vision and widely used computer technology. Themethods often used in image classification and recognition tasks are based on deep learning, like Convolutional Neural Networks(CNNs), LeNet, and Long Short-Term Memory networks (LSTM). Unfortunately, the classificati...

Optical correlators are efficient optical systems that have gained a wide range of applications both in image recognition and encryption, due to their special properties that benefit from the optoelectronic setup instead of an all-electronic one. This paper presents, to the best of our knowledge, the most extensive review of optical correlators to...

Vision transformers have shown great potential in various computer vision tasks owing to their strong capability to model long-range dependency using the self-attention mechanism. Nevertheless, they treat an image as a 1D sequence of visual tokens, lacking an intrinsic inductive bias (IB) in modeling local visual structures and dealing with scale v...

Feature fusion techniques have been proposed and tested for many medical applications to improve diagnostic and classification problems. Specifically, cervical cancer classification can be improved by using such techniques. Feature fusion combines information from different datasets into a single dataset. This dataset contains superior discriminant...

There is a large amount of drilling core data in the Mackay River oil sands block in Canada, and the accurate identification of facies from the cores is important and necessary for the understanding of the subsurface reservoir. The traditional recognition method of facies from cores is by human work and is very time consuming. Furthermore, the resu...

Deep learning-based models usually require a large amount of data for training, which guarantees the effectiveness of the trained model. Generative models are no exception, and sufficient training data are necessary for the diversity of generated images. However, for SAR images, data acquisition is expensive. Therefore, SAR image generation under f...

A lightweight image recognition model L-GhostNet based on GhostNet is proposed to address the problems of large computation and high storage cost of deep convolutional neural networks.The model incorporates learning group convolution and improved CA into GhostNet to reduce the computation and number of parameters and improve the flexibility of the...

Occlusions are universal disruptions constantly present in the real world. Especially for sparse representations, such as human skeletons, a few occluded points might destroy the geometrical and temporal continuity critically affecting the results. Yet, the research of data-scarce recognition from skeleton sequences, such as one-shot action recogni...

Many complex electromechanical assemblies that are essential to the vital function of certain products can be time-consuming to inspect to a sufficient level of certainty. Examples include subsystems of machine tools, robots, aircraft, and automobiles. Out-of-tolerance conditions can occur due to either random common-cause variability or undetected...

The housing crisis in Ireland has rapidly grown in recent years. To make a more significant profit, many landlords are no longer renting out their houses under long-term tenancies but under short-term tenancies. The shift from long-term to short-term rentals has harmed the supply of private housing rentals. Regulating rentals in Rent Pressure Zones...

Image recognition on deep neural network is vulnerable to adversarial sample attacks. The adversarial attack accuracy is low when only limited queries on the target are allowed with the current black box environment. This paper proposes a target adversarial attack algorithm discrete cosine transform‐mean target feature attack (DTFA) based on the ta...

Spatial structure information is very important in image analysis algorithms. Traditional machine learning methods based on vectorization strategies often ignore the spatial information of the original data, resulting in low image recognition and classification accuracy. Different from the vector representation, the tensor representation can preser...

En el presente trabajo se exponen algunos enfoques planteados para el diseño de interfaces de usuario mediante técnicas de machine learning. En la primera parte, se hace una revisión de diversos enfoques para el diseño de interfaces, tales como los optimizadores combinacionales, el uso de frameworks y el diseño de interfaces libres de texto. En cua...

Ear recognition is a new kind of biometric identification technology now. Feature extraction is a key step in pattern recognition technology, which determines the accuracy of classification results. The method of single feature extraction can achieve high recognition rate under certain conditions, but the use of double feature extraction can overco...

As a crucial food crop, potatoes are highly consumed worldwide, while they are also susceptible to being infected by diverse diseases. Early detection and diagnosis can prevent the epidemic of plant diseases and raise crop yields. To this end, this study proposed a weakly-supervised learning approach for the identification of potato plant diseases....

Pests and diseases are an inevitable problem in agricultural production, causing substantial economic losses yearly. The application of convolutional neural networks to the intelligent recognition of crop pest images has become increasingly popular due to advances in deep learning methods and the rise of large-scale datasets. However, the diversity...

With the rapid development of image recognition technology, it has its applications in medical, security and other fields, but this technology has many areas to be improved, such as the accuracy of recognition, real-time and other issues, which have always been a hot research topic in this field. This article involves the development process of ima...

The parking problem, which is caused by a low parking space utilization ratio, has always plagued drivers. In this work, we proposed an intelligent detection method based on deep learning technology. First, we constructed a TensorFlow deep learning platform for detecting vehicles. Second, the optimal time interval for extracting video stream images...

Image recognition technology systems have existed in the realm of computer security since nearly the inception of electronics, and have seen vast improvements in recent years. Currently implemented facial detection systems regularly achieve accuracy rates close to 100 percent. This includes even challenging environments, such as with low light or s...

The aim of webly supervised fine-grained image recognition (FGIR) is to distinguish sub-ordinate categories based on data retrieved from the Internet, which can significantly mitigate the dependence of deep learning on manually annotated labels. Most current fine-grained image recognition algorithms use a large-scale data-driven deep learning parad...

The critical component of HCI is face recognition technology. Emotional computing heavily relies on the identification of facial emotions. Applications for emotion-driven face animation and dynamic assessment are numerous (FER). Universities have started to support real-world face expression recognition research as a result. Short video clips are c...

Floating-algae detection plays an important role in marine-pollution monitoring. The surveillance cameras on ships and shores provide a powerful way of monitoring floating macroalgae. However, the previous methods cannot effectively solve the challenging problem of detecting Ulva prolifera and Sargassum, due to many factors, such as strong interfer...

Notwithstanding the great progress on deep convolutional neural networks (CNNs) has been made during last decade, the representation ability may still be restricted and it usually needs more epochs to converge in training, due to the information loss caused by the up-/down-sampling operations. In this paper, we propose a general deep feature recove...

In this article, the analysis of existing models of satellite image recognition was carried out, the problems in the field of satellite image recognition as a source of information were considered and analyzed, deep learning methods were compared, and existing image recognition methods were analyzed. The results obtained will be used as a basis for...

Journal of Clinical Cases & Reports ISSN: 2582-0435 NLM ID:101773973 (PubMed Listed) Indexed in: Google Scholar, Research Gate, Publons, Cross Ref, Scilit, Semantic Scholars, DRJI, H-index, etc.. Dear Doc, Greetings!! Journal of Clinical Cases & Reports invites to invites submissions to the special issue “Emerging Technologies: Research and Prac...

In order to monitor traffic in congested waters, permanent video stations are now commonly used on interior riverbank bases. It is frequently challenging to identify ships properly and effectively in such images because of the intricate backdrop scenery and overlap between ships brought on by the fixed camera location. This work proposes Ship R-CNN...

Image recognition has been successfully applied in automatic driving and face recognition, and its application in agriculture can greatly promote the process of intelligent agriculture. This article firstly expounds the basic concepts of the intelligent agriculture and image recognition, and enumerates the part of the development of intelligent agr...

Bio‐inspired machine visions have caused wide attentions due to the higher time/power efficiencies over the conventional architectures. Although bio‐mimic photo‐sensors and neuromorphic computing have been individually demonstrated, a complete monolithic vision system has rarely been studied. Here, a neuromorphic machine vision system (NMVS) integr...

Image recognition and optical character recognition technologies have become an integral part of our daily lives due to increasing computing power and the proliferation of scanning devices. A printed document can be quickly converted to a digital text file using optical character recognition and edited by the user. The time required to digitize doc...

Background Neuraxial anesthesia in obese parturients can be challenging due to anatomical and physiological modifications secondary to pregnancy; this led to growing popularity of spine ultrasound in this population for easing landmark identification and procedure execution. Integration of Artificial Intelligence with ultrasound (AI-US) for image e...

Application of deep-learning to polarization imaging technology for image restoration has led to many technological breakthroughs, especially in underwater image recovery and recognition. In this work, a four-input deep learning model with the Polarimetric Residual Dense Network is proposed for underwater image recovery. The diverse polarization co...

Colour sensing is a technique for identifying physical changes in materials based on appearance assessment. Dirt deposition on solar panels can change their physical appearance and performance. Considering that dirt accumulation on solar panels needs monitoring to make efficient cleaning schedules, reduce unnecessary costs, and optimize solar panel...

Image recognition is a technology that, through specific algorithms and methodologies, aims to reproduce the typical biological vision systems by identifying particular objects, patterns, colors or geometric shapes. Artificial intelligence was used in this study to define and test an algorithm for image recognition. In recent years it has been appl...

In this paper, we took the urban roads in the Changsha downtown areas as an example to identify the green view index (GVI) of urban roads based on street view images (SVIs). First, the road network information was obtained through OpenStreetMap, and the coordinate information of sampling points was processed using ArcGIS. Secondly, the SVIs were do...

In order to improve the speed and accuracy of mahjong factory packaging detection, the neural network-based chess and card recognition system designed in this paper mainly includes image preprocessing and image recognition. The image preprocessing uses the OpenCV computer vision library to segment the complete chess and cards into individual chess...

Few-shot learning (FSL) aims to recognize unseen classes with only a few samples for each class. This challenging research endeavors to narrow the gap between the computer vision technology and the human visual system. Recently, mainstream approaches for FSL can be grouped into meta-learning and classification learning. These two methods train the...

Computer image recognition (CIR) on the diffraction patterns of X-ray single crystal diffractometer was used to assist the analysis of the unit cell parameters in terms of lattice volume. This method can improve the prediction accuracy of unit cell parameters in the pre-experiment step since erroneous prediction by the software may occur for 5% tes...

More than four million people worldwide suffer from hearing loss. Recently, new CNNs and deep ensemble-learning technologies have brought promising opportunities to the image-recognition field, so many studies aiming to recognize American Sign Language (ASL) have been conducted to help these people express their thoughts. This paper proposes an ASL...

Objectives A well-known drawback to the implementation of Convolutional Neural Networks (CNNs) for image-recognition is the intensive annotation effort for large enough training dataset, that can become prohibitive in several applications. In this study we focus on applications in the agricultural domain and we implement Deep Learning (DL) techniqu...

Many real-life applications and research projects require data having sensitive information. For example, medical data have sensitive information. Important data tasks such as Entity Resolution, Sentiment Analysis, and Image Recognition use crowdsourcing platforms for label generation. Data sensitivity may make data access, usage, and availability...

With the rapid development of artificial intelligence technology, the network image recognition technology of machine learning is intelligent and production and life in computer vision system technology in China have gradually achieved significant success. On this basis, the computer network image recognition system is constructed by machine learni...

Nowadays, most of the deep learning coal gangue identification methods need to be performed on high-performance CPU or GPU hardware devices, which are inconvenient to use in complex underground coal mine environments due to their high power consumption, huge size, and significant heat generation. Aiming to resolve these problems, this paper propose...

Recognizing handwriting images is challenging due to the vast variation in writing style across many people and distinct linguistic aspects of writing languages. In Vietnamese, besides the modern Latin characters, there are accent and letter marks together with characters that draw confusion to state-of-the-art handwriting recognition methods. More...

Classification of medical images is a crucial aspect of clinical therapy and education. However, the performance of the conventional approach has reached its limit. In addition, the extraction and selection of classification features requires a substantial amount of time and effort when they are employed. Deep neural networks are an up-and-coming m...

Image recognition has always been a popular research topic in computer vision, whose basic task is to learn a model to predict the category of a given image. Early image classification algorithms mainly relied on handcrafted features, while their classification results often failed to meet practical application requirements due to the limitation of...

Diabetic retinopathy (DR) is the leading cause of blindness in diabetics. The low contrast and microscopic nature of the lesions lead to a high false positive rate for automated DR screening. To address this issue, we propose a neural network named AC-DenseNet for the five-stage DR classification. In order to exploit the shallow features and enhanc...

Real-time recognition of human Body position and shooting that target using relative rotations. Real-time human movement recognition is one of the most important technologies for controlling how a human and a robot work together in a wearable robot. I was planning to build a system that would move on its own and shoot at a target that it would figu...

This study investigates the use of pollen elastically scattered light images for species identification. The aim was to identify the best recognition algorithms for pollen classification based on the scattering images. A series of laboratory experiments with a Rapid-E device of Plair S.A. was conducted collecting scattering images and fluorescence...

Visual place recognition (VPR) is a widely investigated but challenging problem, because of the appearance change caused by varying weather conditions, illumination, and seasons, as well as camera occlusion and dynamic objects in complex environments. For robust place recognition in complex environments and extreme appearance changes, in this paper...

Image signal processor (ISP) plays an important role not only for human perceptual quality but also for computer vision. In most cases, experts resort to manual tuning of many parameters in the ISPs for perceptual quality. It failed in sub-optimal, especially for computer vision. Aiming to improve ISPs, two approaches have been actively proposed; t...

Image recognition tasks typically use deep learning and require enormous processing power, thus relying on hardware accelerators like GPUs and FPGAs for fast, timely processing. Failure in real-time image recognition tasks can occur due to incorrect mapping on hardware accelerators, which may lead to timing uncertainty and incorrect behavior. Owing...

This paper proposes a robotic system that automatically identifies and removes spatters generated while removing the back-bead left after the electric resistance welding of the outer and inner surfaces during pipe production. Traditionally, to remove internal spatters on the front and rear of small pipes with diameters of 18–25 cm and lengths of up...

Purpose of Review Artificial Intelligence (AI) has the potential to transform detection and management of nutrition-related complications through advances in wearable technology, mobile applications, and machine learning. The literature, however, lacks studies specific to the interplay between AI and nutrition in patients with liver disease. The ai...

This letter proposes the multiscale domain gradient boosting (MDGB) based approach for the automated recognition of imagined vowels using the multichannel electroencephalogram (MCEEG) signals. The multiscale analysis of the MCEEG signals is performed using multivariate automatic singular spectrum analysis (MASSA) and multivariate fast and adaptive...

Image recognition models that can work in challenging environments (e.g., extremely dark, blurry, or high dynamic range conditions) must be useful. However, creating a training dataset for such environments is expensive and hard due to the difficulties of data collection and annotation. It is desirable if we could get a robust model without the nee...

Numerous researchers have used machine vision in recent years to identify and categorize clouds according to their volume, shape, thickness, height, and coverage. Due to the significant variations in illumination, climate, and distortion that frequently characterize cloud images as a type of naturally striated structure, the Local Binary Patterns (...

Convolutional neural networks are widely used in image feature extraction, but the architecture of existing models is overly complex. To solve this problem, this paper proposes a novel convolutional neural network named CFRW, which consists of two parts, C-FnetT and R-WN. C-FnetT focuses on deepening the network through the Cross-Connection algorit...

High-resolution images are prevalent in various applications, such as autonomous driving and computer-aided diagnosis. However, training neural networks on such images is computationally challenging and easily leads to out-of-memory errors even on modern GPUs. We propose a simple method, Iterative Patch Selection (IPS), which decouples the memory u...