Journal articles: 'Computer vision recognition'

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Kotyk, Vladyslav, and Oksana Lashko. "Software Implementation of Gesture Recognition Algorithm Using Computer Vision." Advances in Cyber-Physical Systems 6, no. 1 (2021): 21–26. http://dx.doi.org/10.23939/acps2021.01.021.

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This paper examines the main methods and principles of image formation, display of the sign language recognition algorithm using computer vision to improve communication between people with hearing and speech impairments. This algorithm allows to effectively recognize gestures and display information in the form of labels. A system that includes the main modules for implementing this algorithm has been designed. The modules include the implementation of perception, transformation and image processing, the creation of a neural network using artificial intelligence tools to train a model for predicting input gesture labels. The aim of this work is to create a full-fledged program for implementing a real-time gesture recognition algorithm using computer vision and machine learning.

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Zheng, Nanning, George Loizou, Xiaoyi Jiang, Xuguang Lan, and Xuelong Li. "Computer vision and pattern recognition." International Journal of Computer Mathematics 84, no. 9 (2007): 1265–66. http://dx.doi.org/10.1080/00207160701303912.

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Wang, Xianghan, Jie Jiang, Yingmei Wei, Lai Kang, and Yingying Gao. "Research on Gesture Recognition Method Based on Computer Vision." MATEC Web of Conferences 232 (2018): 03042. http://dx.doi.org/10.1051/matecconf/201823203042.

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Gesture recognition is an important way of human-computer interaction. With time going on, people are no longer satisfied with gesture recognition based on wearable devices, but hope to perform gesture recognition in a more natural way. Computer vision-based gesture recognition can transfer human feelings and instructions to computers conveniently and efficiently, and improve the efficiency of human-computer interaction significantly. The gesture recognition based on computer vision is mainly based on hidden Markov, dynamic time rounding algorithm and neural network algorithm. The process is roughly divided into three steps: image collection, hand segmentation, gesture recognition and classification. This paper reviews the computer vision-based gesture recognition methods in the past 20 years, analyses the research status at home and abroad, summarizes its current development, the advantages and disadvantages of different gesture recognition methods, and looks forward to the development trend of gesture recognition technology in the next stage.

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Vodyanitskyi, V., and V. Yuskovych-Zhukovska. "ADAPTIVE VISION AI." Automation of technological and business processes 16, no. 4 (2024): 73–81. https://doi.org/10.15673/atbp.v16i4.3013.

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Abstract. As of today, computer vision systems are continuously developing and systematically improving. Machines see visual content in the form of numbers, in which each pixel represents its own piece of information. Computer vision, as a component of artificial intelligence, allows machines to see, observe and understand everything. It enables computer systems to obtain useful information from digital images, video, visual data and perform programmed actions. Computer vision technologies rely on pattern recognition, machine learning, and neural networks to allow computers to break down images, interpret data, and identify features. Tracking moving objects and their identification is a difficult task, as it requires the accuracy of pattern recognition. An untrained computer vision algorithm is unable to understand the relationship between the shapes in the image and the objects. Therefore, the algorithm must be trained. The paper considers models that are trained on a high-performance computing cluster with GPU support. The developed open source software allows detection, tracking and recognition of blurry moving objects with the help of artificial intelligence that adapts to any video camera. A significant increase in accuracy is achieved thanks to machine learning.

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Pandey, Mrs Arjoo. "Computer Vision." International Journal for Research in Applied Science and Engineering Technology 11, no. 7 (2023): 510–14. http://dx.doi.org/10.22214/ijraset.2023.54701.

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Abstract: Computer vision is a field of artificial intelligence that focuses on enabling computers to understand and interpret visual information from images or videos. It involves developing algorithms and techniques to extract meaningful insights, patterns, and knowledge from visual data, mimicking human visual perception capabilities. The abstract of computer vision encompasses a range of fundamental tasks and objectives, including: Image Classification: Classifying images into predefined categories or classes, such as distinguishing between different objects, animals, or scenes. Object Detection and Recognition: Locating and identifying specific objects within an image or video, often through the use of bounding boxes or pixel-level segmentation. Semantic Segmentation: Assigning semantic labels to each pixel in an image to distinguish between different objects or regions.

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Matsuzaka, Yasunari, and Ryu Yashiro. "AI-Based Computer Vision Techniques and Expert Systems." AI 4, no. 1 (2023): 289–302. http://dx.doi.org/10.3390/ai4010013.

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Computer vision is a branch of computer science that studies how computers can ‘see’. It is a field that provides significant value for advancements in academia and artificial intelligence by processing images captured with a camera. In other words, the purpose of computer vision is to impart computers with the functions of human eyes and realise ‘vision’ among computers. Deep learning is a method of realising computer vision using image recognition and object detection technologies. Since its emergence, computer vision has evolved rapidly with the development of deep learning and has significantly improved image recognition accuracy. Moreover, an expert system can imitate and reproduce the flow of reasoning and decision making executed in human experts’ brains to derive optimal solutions. Machine learning, including deep learning, has made it possible to ‘acquire the tacit knowledge of experts’, which was not previously achievable with conventional expert systems. Machine learning ‘systematises tacit knowledge’ based on big data and measures phenomena from multiple angles and in large quantities. In this review, we discuss some knowledge-based computer vision techniques that employ deep learning.

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Niu, Xiang Jie, and Bin Lan. "The Agricultural Products Deterioration Recognition Based on Computer Vision." Applied Mechanics and Materials 602-605 (August 2014): 2027–30. http://dx.doi.org/10.4028/www.scientific.net/amm.602-605.2027.

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The computer vision technology is an important branch of computer science and artificial intelligence which is regarded as a non-destructive testing technique in the field of agriculture with a broad application prospects. This paper introduces the application of the computer vision technology in the agricultural products deterioration recognition, builds foundations for the accurate measurement of the agricultural products quality with computer visions, and establish the relationship between the feature information and quality of the agricultural products. Meanwhile, this paper combined the computer vision technology with infrared, microwave, NMR techniques to extract and test the visual information of the internal quality of the agricultural products.

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Xiaoning Bo, Xiaoning Bo, Jin Wang Xiaoning Bo, Qingfang Liu Jin Wang, Peng Yang Qingfang Liu, and Honglan Li Peng Yang. "Computer Vision Recognition Method for Surface Defects of Casting Workpieces." 電腦學刊 34, no. 3 (2023): 305–13. http://dx.doi.org/10.53106/199115992023063403022.

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To improve the recognition efficiency of surface defects in castings, this article first uses median filtering algorithm to denoise the defect image to distinguish between defects and background. Then, gray threshold method is used to segment the image, and the processed image is sent to the improved RefineDet network structure. Improving the RefineDet network structure mainly improves the network depth and incorporates dataset augmentation algorithms. Finally, an experimental platform was built to train, recognize, and compare the collected image dataset. The results show that the accuracy of detecting porosity, blowhole, and flaw defects is 95.6% and 97.3% and 98.15%, the method proposed in this article is accurate and efficient. &nbsp;

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Dharshini, M., P. Santhiya, A. Susmitha, and V. S. Balambiga. "Real Time Sign Language Recognition Using Computer Vision And Ai." International Journal of Research Publication and Reviews 6, no. 5 (2025): 9997–10003. https://doi.org/10.55248/gengpi.6.0525.1867.

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Zheng, Zepei. "Human Gesture Recognition in Computer Vision Research." SHS Web of Conferences 144 (2022): 03011. http://dx.doi.org/10.1051/shsconf/202214403011.

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Human gesture recognition is a popular issue in the studies of computer vision, since it provides technological expertise required to advance the interaction between people and computers, virtual environments, smart surveillance, motion tracking, as well as other domains. Extraction of the human skeleton is a rather typical gesture recognition approach using existing technologies based on two-dimensional human gesture detection. Likewise, I t cannot be overlooked that objects in the surrounding environment give some information about human gestures. To semantically recognize the posture of the human body, the logic system presented in this research integrates the components recognized in the visual environment alongside the human skeletal position. In principle, it can improve the precision of recognizing postures and semantically represent peoples’ actions. As such, the paper suggests a potential and notion for recognizing human gestures, as well as increasing the quantity of information offered through analysis of images to enhance interaction between humans and computers.

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Che, Chang, Haotian Zheng, Zengyi Huang, Wei Jiang, and Bo Liu. "Intelligent robotic control system based on computer vision technology." Applied and Computational Engineering 64, no. 1 (2024): 150–55. http://dx.doi.org/10.54254/2755-2721/64/20241373.

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Computer vision is a kind of simulation of biological vision using computers and related equipment. It is an important part of the field of artificial intelligence. Its research goal is to make computers have the ability to recognize three-dimensional environmental information through two-dimensional images. Computer vision is based on image processing technology, signal processing technology, probability statistical analysis, computational geometry, neural network, machine learning theory and computer information processing technology, through computer analysis and processing of visual information.The article explores the intersection of computer vision technology and robotic control, highlighting its importance in various fields such as industrial automation, healthcare, and environmental protection. Computer vision technology, which simulates human visual observation, plays a crucial role in enabling robots to perceive and understand their surroundings, leading to advancements in tasks like autonomous navigation, object recognition, and waste management. By integrating computer vision with robot control, robots gain the ability to interact intelligently with their environment, improving efficiency, quality, and environmental sustainability. The article also discusses methodologies for developing intelligent garbage sorting robots, emphasizing the application of computer vision image recognition, feature extraction, and reinforcement learning techniques. Overall, the integration of computer vision technology with robot control holds promise for enhancing human-computer interaction, intelligent manufacturing, and environmental protection efforts.

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Astolfi, Gilberto, Fábio Prestes Cesar Rezende, João Vitor De Andrade Porto, Edson Takashi Matsubara, and Hemerson Pistori. "Syntactic Pattern Recognition in Computer Vision." ACM Computing Surveys 54, no. 3 (2021): 1–35. http://dx.doi.org/10.1145/3447241.

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Using techniques derived from the syntactic methods for visual pattern recognition is not new and was much explored in the area called syntactical or structural pattern recognition. Syntactic methods have been useful because they are intuitively simple to understand and have transparent, interpretable, and elegant representations. Their capacity to represent patterns in a semantic, hierarchical, compositional, spatial, and temporal way have made them very popular in the research community. In this article, we try to give an overview of how syntactic methods have been employed for computer vision tasks. We conduct a systematic literature review to survey the most relevant studies that use syntactic methods for pattern recognition tasks in images and videos. Our search returned 597 papers, of which 71 papers were selected for analysis. The results indicated that in most of the studies surveyed, the syntactic methods were used as a high-level structure that makes the hierarchical or semantic relationship among objects or actions to perform the most diverse tasks.

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Manasi, Mohaney, and Sonal Chaudhary. "Computer Vision Technology using Gesture Recognition." International Journal of Computer Applications 179, no. 19 (2018): 1–4. http://dx.doi.org/10.5120/ijca2018916320.

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Zhou, Hui. "Alphabet Recognition Based on Computer Vision." Applied Mechanics and Materials 543-547 (March 2014): 2354–57. http://dx.doi.org/10.4028/www.scientific.net/amm.543-547.2354.

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In order to realize rapid alphabet recognition, the paper proposes an alphabet recognition method based on computer vision optimization technical which can also extract the classification features. Experimental results show that the obtained variance value of the test image and the standard image obtained by the proposed method is the minimum which indicating the method can achieve correct match, effective classification, and provide a great method of identification.

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Akata, Zeynep, Andreas Geiger, and Torsten Sattler. "Computer Vision and Pattern Recognition 2020." International Journal of Computer Vision 129, no. 12 (2021): 3169–70. http://dx.doi.org/10.1007/s11263-021-01522-3.

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Meghana, K. S. "Face Sketch Recognition Using Computer Vision." International Journal for Research in Applied Science and Engineering Technology 9, no. VII (2021): 2005–9. http://dx.doi.org/10.22214/ijraset.2021.36806.

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Now-a-days need for technologies for identification, detection and recognition of suspects has increased. One of the most common biometric techniques is face recognition, since face is the convenient way used by the people to identify each-other. Understanding how humans recognize face sketches drawn by artists is of significant value to both criminal investigators and forensic researchers in Computer Vision. However, studies say that hand-drawn face sketches are still very limited in terms of artists and number of sketches because after any incident a forensic artist prepares a victim’s sketches on behalf of the description provided by an eyewitness. Sometimes suspect uses special mask to hide some common features of faces like nose, eyes, lips, face-color etc. but the outliner features of face biometrics one could never hide. Here we concentrate on some specific facial geometric feature which could be used to calculate some ratio of similarities from the template photograph database against the forensic sketches. The project describes the design of a system for face sketch recognition by a computer vision approach like Discrete Cosine Transform (DCT), Local Binary Pattern Histogram (LBPH) algorithm and a supervised machine learning model called Support Vector Machine (SVM) for face recognition. Tkinter is the standard GUI library for Python. Python when combined with Tkinter provides a fast and easy way to create GUI applications. Tkinter provides a powerful object-oriented interface to the Tk GUI toolkit.

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Moss, Randy H., William V. Stoecker, Shi-Jen Lin, et al. "Skin cancer recognition by computer vision." Computerized Medical Imaging and Graphics 13, no. 1 (1989): 31–36. http://dx.doi.org/10.1016/0895-6111(89)90076-1.

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Murino, Vittorio, and Andrea Trucco. "Underwater Computer Vision and Pattern Recognition." Computer Vision and Image Understanding 79, no. 1 (2000): 1–3. http://dx.doi.org/10.1006/cviu.2000.0852.

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Lu, Yaoyun, and Yueyang Ma. "Mask recognition in computer vision technology." Applied and Computational Engineering 6, no. 1 (2023): 469–73. http://dx.doi.org/10.54254/2755-2721/6/20230838.

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In order to lessen the strain on employees and the potential for carelessness, intelligent identification is required in China where there is a scarcity of staff to monitor the wearing of masks in public areas. In this work, we use a mask recognition technique to determine which members of the population weren't wearing masks by using the CNN and the VGG16 model. The ideas of data augmentation, dropout, non malicious, and transfer learning are used in the proposed study. This method may be used at hospitals, retail centers, transit hubs, dining establishments, and other community gatherings that require monitoring.

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Yu, Huang. "Facial expression recognition with computer vision." Applied and Computational Engineering 37, no. 1 (2024): 74–80. http://dx.doi.org/10.54254/2755-2721/37/20230473.

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Facial Expression Recognition (FER) is a specialized field within the domains of computer vision and pattern recognition, which is dedicated to the automated identification and examination of facial expressions. Facial expression recognition (FER) has attracted considerable scholarly interest in recent years owing to its diverse array of applications and its potential ramifications across multiple disciplines, such as psychology, human-computer interaction, marketing, and security systems. The objective of this study is to present a thorough examination of the scholarly progression of FER, elucidating the significant achievements, approaches, and obstacles encountered by researchers in this domain. The study presents a selection of databases that are appropriate for Facial Expression Recognition (FER) and conducts a comparative analysis of these databases. The primary methodologies are examined, and recommendations are provided for each stage. In conclusion, this research presents several suggestions for addressing both obstacles and potential in future research endeavors.

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Aditya Verma and Ankita Verma. "Vision Based Interface in Human Computer Interaction." International Journal of Scientific Research in Science, Engineering and Technology 11, no. 4 (2024): 71–79. http://dx.doi.org/10.32628/ijsrset241147.

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This paper provides information on Human Computer Interaction, it actually means that, its present use in the world of technology and its importance in the tech- savvy world of today. The paper goes through the various types of interaction that can take place between a human and a computer, the technicalities of the same, and the future scope of each interface. We always want a path of communication which is fast, is efficient and is user friendly which gives us maximum output and good performance consistently. Vision based interface between human and computer is studied in detail. It can be stated as the most popular types of interaction between human beings and computers. Vision based interaction in HCI makes use of four main techniques; gesture recognition, eye movement recognition or tracking, head tracking and facial expressions recognition and judgement. Analysis for their usage in practical systems has been made. This is the most worked upon area is the vision based hand gesture recognition which has been discussed in this paper. The present studies and key findings in this area have been listed and its future scope and utility has also been discussed in this paper.

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Kalenova, S., and Zh Duisebekov. "NOVEL HUMAN-COMPUTER INTERACTION THROUGH A PRISMOF COMPUTER VISION." Suleyman Demirel University Bulletin Natural and Technical Sciences 54, no. 1 (2021): 68–73. https://doi.org/10.47344/sdubnts.v54i1.537.

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For the past two decades, the innovative developments inComputer Science have made the crucial beneficial influence on Human –Computer Interaction (HCI) applications. Recent advanced HCI techniques areessential elements to enhance current computer interfaces and facilitate thefundamental user-friendly interaction. It is now becoming possible tocommunicate with computers by using hand gesture, pose recognition, eyetracking, emotion recognition and several other developed progressivetechnologies. This paper investigates the direct contribution of Computer Vision(CV) applications to improving various modes of human-interaction, specializedin different prominent industries, where cumulative list of use cases includessport, healthcare, agriculture, transportation, retail and manufacturing fields. Thecurrent work also explores the future forecast of CV applications for the nextdecade and identifies possible scenarios of research directions.

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Gomathy, Dr C. K., Kakamanu Jaya Sairam, and Illuru Yadhu Vamsi. "A THE HANDWRITTEN DIGITS RECOGNITION." International Journal of Engineering Applied Sciences and Technology 6, no. 7 (2021): 203–6. http://dx.doi.org/10.33564/ijeast.2021.v06i07.033.

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Humans can see and visually smell the earth around them by using their eyes and smarts. Computer vision works on enabling computers to ascertain and reuse images within the same way that mortal vision does. Several algorithms developed within the area of computer vision to admit images. The thing of our work is going to be to make a model which will be ready to identify and determine the handwritten number from its image with better delicacy. We aim to finish this by using the generalities of Convolution Neural Network and MNIST dataset. This paper presents an approach to out• line handwritten number recognition supported different machine learning fashion. the most objective of this paper is to make sure effective and dependable approaches for recognition of handwritten integers.

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Wang, Peng. "Research on Sports Training Action Recognition Based on Deep Learning." Scientific Programming 2021 (June 29, 2021): 1–8. http://dx.doi.org/10.1155/2021/3396878.

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With the rapid development of science and technology in today’s society, various industries are pursuing information digitization and intelligence, and pattern recognition and computer vision are also constantly carrying out technological innovation. Computer vision is to let computers, cameras, and other machines receive information like human beings, analyze and process their semantic information, and make coping strategies. As an important research direction in the field of computer vision, human motion recognition has new solutions with the gradual rise of deep learning. Human motion recognition technology has a high market value, and it has broad application prospects in the fields of intelligent monitoring, motion analysis, human-computer interaction, and medical monitoring. This paper mainly studies the recognition of sports training action based on deep learning algorithm. Experimental work has been carried out in order to show the validity of the proposed research.

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Chen, Yizhi, Sihao Wang, Luqi Lin, Zhengrong Cui, and Yanqi Zong. "Computer Vision and Deep Learning Transforming Image Recognition and Beyond." International Journal of Computer Science and Information Technology 2, no. 1 (2024): 45–51. http://dx.doi.org/10.62051/ijcsit.v2n1.06.

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Computer vision is a cutting-edge information processing technology that seeks to mimic the human visual nervous system. Its primary aim is to emulate the psychological processes of human vision to interpret and depict objective scenery. This revolutionary field encompasses a wide range of applications, including life sciences, medical diagnosis, military operations, scientific research, and many others. At the heart of computer vision lies the theoretical core, which includes deep learning, image recognition, target detection, and target tracking These elements combine to enable computers to process, analyze, and understand images, allowing for the classification of objects based on various patterns One of the standout advantages of deep learning techniques, when compared to traditional methods, is their ability to automatically learn and adapt to the specific features required for a given problem. This adaptive nature of deep learning networks has opened up new possibilities and paved the way for remarkable breakthroughs in the field of computer vision. This paper examines the practical application of computer vision processing technology and convolutional neural networks (CNNs) and elucidates the advancements in artificial intelligence within the field of computer vision image recognition. It does so by showcasing the tangible benefits and functionalities of these technologies.

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Ramadoss, Janarthanan, J. Venkatesh, Shubham Joshi, et al. "Computer Vision for Human-Computer Interaction Using Noninvasive Technology." Scientific Programming 2021 (November 3, 2021): 1–15. http://dx.doi.org/10.1155/2021/3902030.

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Computer vision is a significant component of human-computer interaction (HCI) processes in interactive control systems. In general, the interaction between humans and computers relies on the flexibility of the interactive visualization system. Electromyography (EMG) is a bioelectric signal used in HCI that can be captured noninvasively by placing electrodes on the human hand. Due to the impact of complex background, accurate recognition and analysis of human motion in real-time multitarget scenarios are considered challenging in HCI. Further, EMG signals of human hand motions are exceedingly nonlinear, and it is important to utilize a dynamic approach to address the noise problem in EMG signals. Hence, in this paper, the Optimized Noninvasive Human-Computer Interaction (ONIHCI) model has been proposed to predict human motion recognition. Average Intrinsic Mode Function (AIMF) has been used to reduce the noise factor in EMG signals. Furthermore, this paper introduces spatial thermographic imaging to overcome the conventional sensor problem, such as gesture recognition and human target identification in multitarget scenarios. The human motion behavior in spatial thermographic images is examined by target trajectory, and body movement kinematics is employed to classify human targets and objects. The experimental findings demonstrate that the proposed method reduces noise by 7.2% and improves accuracy by 97.2% in human motion recognition and human target identification.

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Abrar, Muhammad Fauzan, and Vina Ayumi. "Aircraft Recognition in Remote Sensing Images Based on Artificial Neural Networks." Journal of Computer Science and Engineering (JCSE) 4, no. 2 (2023): 97–112. http://dx.doi.org/10.36596/jcse.v4i2.381.

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Computer Vision (CV) is a field of Artificial Intelligence (AI) that enables computers and systems to obtain data from images, recordings and other visual information sources. Image Recognition, a subcategory of Computer Vision, addresses a bunch of strategies for perceiving and taking apart pictures to engage the automation of a specific task. It is fit for perceiving places, people, objects and various types of parts inside an image, and reaching deductions from them by analyzing them. With these kinds of utilities it is a no-brainer that Computer Vision has its use cases in the military world. Computer Vision can be immensely useful for Intelligence, Surveillance and Reconnaissance (ISR) work. This paper provides on how Computer Vision might be used in ISR work. This paper utilises Artificial Neural Network (ANN) such as Convolutional Neural Network (CNN) and Residual Neural Network (ResNet) for demonstration purposes. In the end, the ResNet model managed to edge out the CNN model with a final validation accuracy of 90.9% compared to a validation accuracy of 86% on the CNN model. With this, Computer Vision can help enhance the efficiency of human operators in image and video data related work.

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Patel, Dhyan. "Computer Vision and Image Segmentation." International Journal for Research in Applied Science and Engineering Technology 12, no. 2 (2024): 915–25. http://dx.doi.org/10.22214/ijraset.2024.58479.

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Abstract: Image segmentation is a critical step in image processing, computer vision, and pattern recognition, which involves dividing an image into different regions or segments. Image segmentation plays an essential role in many applications, such as object recognition, medical image analysis, autonomous driving, and robotics. This paper aims to provide an overview of image segmentation techniques, including traditional and deep learning-based approaches. The paper also discusses the challenges associated with image segmentation, such as noise, illumination variations, and occlusions. Finally, the paper provides a brief discussion on the evaluation metrics used to assess the performance of image segmentation algorithms.

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Bhumkar, Prathamesh. "HAND GESTURE CONTROLLER." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 05 (2024): 1–5. http://dx.doi.org/10.55041/ijsrem35055.

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In this paper we present an interaction between humans and computer, gesture recognition does play a critical role. While technology has developed to such a level it made possible to communicate with computers with the Gesture Recognition system. Having reached all the best possible ways for data acquisition like cameras, hand Movement now these are of less concern. The desire for human-machine interaction is rapidly growing due to advancements in computer vision technology. Gesture recognition is used extensively in many different types of fields. It indicates that research into vision-based hand gesture recognition is an expanding field, with many studies and papers appearing on a regular basis in research publications and conference papers. Our study further assesses the accuracy with which vision-based recognition of hand gestures systems work. The three primary phases are hand shape recognition, hand tracing, and data transformation to the required command. Keywords— Deep Learning, CNN-Convolutional Neural Networks, Hand Gesture Controller, Human-Computer interaction

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Dementiev, M., and O. Lashko. "ALGORITHM FOR PRIMARY OBJECT RECOGNITION IN THE WAREHOUSE MANAGEMENT SYSTEM." Computer systems and network 5, no. 1 (2023): 20–28. http://dx.doi.org/10.23939/csn2023.01.020.

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This article examines the peculiarities of warehouse management systems and presents the principles and implementation of an in-house software system for warehouse management using computer vision technology. A structural diagram of the application is developed, which consists of eight modules: image capture service, image storage, computer vision service, database, API server, client application, task scheduler, and task queue. The architecture is designed based on cloud technologies, namely Google Cloud Platform. A computer vision algorithm for determining the state of cells in the warehouse is proposed. A functional software product based on modern technologies has been developed. The purpose of this article is to reflect the results of the study of the subject area of warehouse management systems and to highlight the results of the implementation of a proprietary software system using computer vision. Keywords: Python, OpenCV, computer vision, Google Cloud, warehouse management system, cloud computing, serverless computing.

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Zhang, Dongbing, Xinggang Zhang, Yihua Lan, and Guangwei Wang. "Fatty Liver Recognition Based on Computer Vision." Journal of Applied Sciences 13, no. 14 (2013): 2730–34. http://dx.doi.org/10.3923/jas.2013.2730.2734.

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Kravets, A. M., and V. P. Simonenko. "LANDMARKS RECOGNITION METHOD USING COMPUTER VISION TECHNOLOGIES." Scientific notes of Taurida National V.I. Vernadsky University. Series: Technical Sciences 1, no. 1 (2021): 93–97. http://dx.doi.org/10.32838/2663-5941/2021.1-1/15.

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Ивановский, А. Н., Н. Н. Марковкина, and С. Г. Черный. "Draft mark recognition with computer vision algorithms." MORSKIE INTELLEKTUAL`NYE TEHNOLOGII), no. 1(51) (March 5, 2021): 102–7. http://dx.doi.org/10.37220/mit.2021.51.1.032.

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Снятие осадки судна – одна из ключевых операций на большинстве морских судов. Значение осадки используется при определении массы груза на судах типа балкер, а также при планировании перехода и обеспечении безопасности на всех других судах. Однако, если при планировании перехода погрешность в пять – десять сантиметров не играет существенной роли, то при определении загрузки судна каждый сантиметр ошибки может стоить компаниям тысячи, а то и десятки тысяч долларов США. Для определения осадки судна существует несколько способов, однако, несмотря на их наличие, в большинстве случаев измерения проводятся исключительно визуальным способом. Связано это с низкой точностью существующих методов, особенно в условиях волнения. В качестве направления исследования предлагается способ определения осадки судна при помощи алгоритмов компьютерного зрения и машинного обучения по видеозаписи. Обработка видео проводится покадрово. Общее исследование предполагает наличие трех частей – выделение марки углубления на изображении, сегментацию водной поверхности на изображении и снятие замеров с их последующей обработкой методами математической статистики и линейной фильтрации. В данной работе описана первая часть исследования, целью которой является выявление марки углубления. Каждый кадр подвергается бинаризации при помощи порогового разделения, затем после проведения ряда морфологических операций проводится определение связных областей на кадре. На основании полученных областей строится координатная прямая, по которой и будет в дальнейшем производится снятие осадки судна. В настоящее время, аналоги данной технологии отсутствуют на рынке. Также, исследование позволяет достичь высокой точности измерений даже при неблагоприятных погодных условиях. Ship's draft marks reading is a key procedure in cargo operations on bulk carriers, as well it`s significant part of passage planning and ensuring safety of navigation on all other ships. Though, whereas an error of five – ten centimeters doesn`t affect too much on passage planning, it is weighty while talking about cargo operations, as each centimeter of error can cost companies thousands, or even dozens of thousands of US dollars. There are several ways to determine the ship's draft, nevertheless, visual readings are still the primary manner of carrying out mentioned procedure. That`s caused by the reason of low accuracy of existing methods, especially in case of swell. In general, it is considered to describe alternative method for determining the ship's draft by using fundamentally new method, based on computer vision and machine learning technologies applied to video recording. Video processing is carried out frame by frame. Full research assumes the presence of three parts - highlighting the draft mark`s numerals on the image, segmentation of the water surface and taking measurements with their subsequent processing by methods of mathematical statistics and linear filtering. This paper describes the first part of research, so, there are threshold and morphological computer vision algorithms were applied. Thus, draft marks numerals segmentation was carried out. On the basis of the obtained areas, a coordinate line is constructed, along which the vessel`s draft will be measured in the future. The are no analogues of this method on the market. Besides, the high level of measurement accuracy is expected to be achieved even in adverse weather conditions.

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DEGUCHI, Koichiro. "Introduction to Pattern Recognition and Computer Vision." Interdisciplinary Information Sciences 17, no. 2 (2011): 49–129. http://dx.doi.org/10.4036/iis.2011.49.

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单, 勇. "Train Interval Recognition Based on Computer Vision." Hans Journal of Data Mining 05, no. 02 (2015): 17–24. http://dx.doi.org/10.12677/hjdm.2015.52003.

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Juang, Li-Hong, Ming-Ni Wu, and Shin-An Lin. "Gender recognition based on computer vision system." Intelligent Automation and Soft Computing 24, no. 2 (2018): 249–56. http://dx.doi.org/10.1080/10798587.2016.1272777.

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Farinella, Giovanni Maria, Marco Leo, Gerard G. Medioni, and Mohan Trivedi. "Learning and recognition for assistive computer vision." Pattern Recognition Letters 137 (September 2020): 1–2. http://dx.doi.org/10.1016/j.patrec.2019.11.006.

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Brill, Michael H. "Computer Vision and Pattern Recognition: CVPR 92." Color Research & Application 17, no. 6 (1992): 426–27. http://dx.doi.org/10.1002/col.5080170616.

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Jahnavi, Mudili. "Controlling Computer Using Hand Gestures." INTERNATIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 05 (2025): 1–9. https://doi.org/10.55041/ijsrem49260.

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Abstract: In the realm of Human-Computer Interaction (HCI), the integration of webcams and various sensors has made gesture recognition increasingly accessible and impactful. Hand gestures provide a natural and intuitive mode of communication, enabling seamless interaction between humans and computers. This paper highlights the potential of hand gestures as an effective medium for non-verbal communication and control, with applications spanning across multiple domains. The proposed system leverages image processing techniques, sensor technologies, and computer vision to enable gesture-based computer control. Emphasis is placed on the interdisciplinary nature of the research, including its applications in fields such as machine learning, healthcare, and mobile technology. Keywords: Hand Gesture Recognition, Human-Computer Interaction, Sensor Technology, Image Processing, Machine Learning, Android Application, Diabetes Monitoring, Computer Vision

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Sikri, Nidhi, and Navleen Singh Rekhi. "Vision Based Analysis of Hand Gesture Recognition for Human Computer Interaction (HCI)." International Journal of Scientific Engineering and Research 5, no. 7 (2017): 316–20. https://doi.org/10.70729/ijser171683.

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Fahrudin, Fikri, Mesi Andriani, Muallimin, and Eka Altiarika. "Gerakan Tangan Pemain Otomatis Menggunakan Computer Vision." Journal of Information Technology and society 1, no. 1 (2023): 15–19. http://dx.doi.org/10.35438/jits.v1i1.19.

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Gesture recognition allows users of computer science technology to connect with their digital devices more conveniently. Technology for gesture recognition can be helpful in a variety of contexts, such as automated household appliances, automobiles, and interpretation of hand gestures. Gesture recognition is part of gesture recognition which determines what message a certain hand movement wants to convey. In developing this automatic hand movement we use segmentation and object detection where this method involves using algorithms to detect and identify objects or areas related to hand movements. , such as human skin, fingers, and others. Detection of human hand movements using computer vision is a digital image processing technique that aims to recognize human hand movements from image or video data. This technique can be applied in various applications such as human-computer interaction, hand gesture recognition, or video games. Making Automatic Player Hand Movements Using Computer Vision has the potential to improve the user experience when playing games or using interactive applications that require hand movements, according to the research and development that has been done. Games could be controlled more precisely and with less need for additional hardware such as joysticks or controllers if computer vision technology was able to accurately distinguish hand movements.

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Lee, Minhoon, Hobin Kim, Mikyeong Moon, and Seung-Min Park. "Computer-Vision-Based Advanced Optical Music Recognition System." Journal of Computational and Theoretical Nanoscience 18, no. 5 (2021): 1345–51. http://dx.doi.org/10.1166/jctn.2021.9626.

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Computer vision is an artificial intelligence technology that studies techniques for extracting information from images. Several studies have been performed to identify and edit music scores using computer vision. This study proposes a system to identify musical notes and print arranged music. Music is produced by general rules; consequently, the components of music have specific patterns. There are four approaches in pattern recognition that can be used classify images using patterns. Our proposed method of identifying music sheets is as follows. Several pretreatment processes (image binary, noise and staff elimination, image resizing) are performed to aid the identification. The components of the music sheet are identified by statistical pattern recognition. Applying an artificial intelligence model (Markov chain) to extracted music data aids in arranging the data. From applying the pattern recognition technique, a recognition rate of 100% was shown for music sheets of low complexity. The components included in the recognition rate are signs, notes, and beats. However, there was a low recognition rate for some music sheet and can be addressed by adding a classification to the navigation process. To increase the recognition rate of the music sheet with intermediate complexity, it is necessary to refine the pre-processing process and pattern recognition algorithm. We will also apply neural network-based models to the arrangement process.

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Lee, Minhoon, Hobin Kim, Mikyeong Moon, and Seung-Min Park. "Computer-Vision-Based Advanced Optical Music Recognition System." Journal of Computational and Theoretical Nanoscience 18, no. 5 (2021): 1345–51. http://dx.doi.org/10.1166/jctn.2021.9626.

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Computer vision is an artificial intelligence technology that studies techniques for extracting information from images. Several studies have been performed to identify and edit music scores using computer vision. This study proposes a system to identify musical notes and print arranged music. Music is produced by general rules; consequently, the components of music have specific patterns. There are four approaches in pattern recognition that can be used classify images using patterns. Our proposed method of identifying music sheets is as follows. Several pretreatment processes (image binary, noise and staff elimination, image resizing) are performed to aid the identification. The components of the music sheet are identified by statistical pattern recognition. Applying an artificial intelligence model (Markov chain) to extracted music data aids in arranging the data. From applying the pattern recognition technique, a recognition rate of 100% was shown for music sheets of low complexity. The components included in the recognition rate are signs, notes, and beats. However, there was a low recognition rate for some music sheet and can be addressed by adding a classification to the navigation process. To increase the recognition rate of the music sheet with intermediate complexity, it is necessary to refine the pre-processing process and pattern recognition algorithm. We will also apply neural network-based models to the arrangement process.

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Kovashka, Adriana, Olga Russakovsky, Li Fei-Fei, and Kristen Grauman. "Crowdsourcing in Computer Vision." Foundations and Trends® in Computer Graphics and Vision 10, no. 3 (2016): 177–243. http://dx.doi.org/10.1561/0600000071.

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Kadam, Pratiksha, Prof Minal Junagre, Sakshi Khalate, Vaishnavi Jadhav, and Pragati Shewale. "Gesture Recognition based Virtual Mouse and Keyboard." International Journal for Research in Applied Science and Engineering Technology 11, no. 5 (2023): 1824–30. http://dx.doi.org/10.22214/ijraset.2023.51971.

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Abstract: The field of computer vision has advanced significantly, enabling computers to identify their users using simple programs based on image processing. This technology has been widely used in various day-to-day applications, such as face recognition, color detection, and autonomous driving. This research project aims to use computer vision to develop an optical mouse and keyboard that can be operated through hand movements. The computer camera will capture images of different hand gestures made by the user, and the mouse pointer or cursor on the computer screen will move accordingly. Different hand gestures can be used to execute right and left-clicks. Similarly, the keyboard functions can be performed using different hand actions, such as using a finger to select an alphabet and a four-digit swipe left or right. The virtual mouse and keyboard can be used wirelessly or externally, and the only hardware required for the project is a webcam.

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JAIN, NISHIT, and PALAK LOTHE. "Computer Vision Plant Diagnostics." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 04 (2025): 1–9. https://doi.org/10.55041/ijsrem44084.

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To address the issues facing agriculture, the Computer Vision Plant Diagnostics offers an innovative and practical solution. It is important to implement better farming methods and efficiently manage Plant diseases as the world's population continues to rise and puts strain on food production. This system makes use of the machine's power. Using machine learning and image recognition, farmers, gardeners, and agricultural experts can now have access to a program that can accurately identify Plant illnesses. Users can rely on model within the system to identify the disease and suggest required measures for limiting its impact, by uploading pictures of plants or leaf’s. Key-Words: PyTorch, Disease, Convolutional Neural Networks

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Peng-Jie Du, Peng-Jie Du, and Mu-Zhuo Zhang Peng-Jie Du. "Computer Vision Aided Pantograph Fault Identification Method for Multiple Units." 電腦學刊 34, no. 4 (2023): 145–52. http://dx.doi.org/10.53106/199115992023083404012.

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In order to solve the technical requirements for automatic recognition and judgment of pantograph wear degree of Multiple Units, this paper designs a network structure based on Mask R-CNN structure. At the same time, in order to improve the ability of image feature extraction in the network, the original backbone network is replaced with ResNet-50, a residual network with more prominent feature extraction ability. Secondly, in order to improve the ability to search for targets in the image, the detection head is reconstructed, to improve the recognition ability of targets. Finally, the effectiveness of the algorithm and its ability to identify pantograph faults were verified through simulation experiments. &nbsp;

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Jolion, J. "Computer Vision Methodologies." Computer Vision and Image Understanding 59, no. 1 (1994): 53–71. http://dx.doi.org/10.1006/cviu.1994.1004.

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MA, Ramanuja, Pradyumna Ramesh, Shikha Yadav, Rohan BJ, and Yashpal Gupta S. "A Review on Hand Gesture Recognition." International Journal for Research in Applied Science and Engineering Technology 10, no. 6 (2022): 5122–26. http://dx.doi.org/10.22214/ijraset.2022.44603.

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Abstract: User Interface (UI) and Human Computer Interaction (HCI) have come a long way from the time computers went personal. The constant work and research done to explore HCI allows for new ways and improvements. Hand gesture recognition is part of HCI that quickly picked up after the advancements in computer hardware and machine learning, particularly computer vision. Hand gesture recognition is a natural alternative to interact with computer as compared to mechanical devices(mouse, keyboard, etc),just as we interact with other humans through hand gestures. We review the existing tools and techniques that make hand gesture possible today, along with some common pitfalls and potential fixes.

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Cheng, Wen-Huang, Sijie Song, Chieh-Yun Chen, Shintami Chusnul Hidayati, and Jiaying Liu. "Fashion Meets Computer Vision." ACM Computing Surveys 54, no. 4 (2021): 1–41. http://dx.doi.org/10.1145/3447239.

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Fashion is the way we present ourselves to the world and has become one of the world’s largest industries. Fashion, mainly conveyed by vision, has thus attracted much attention from computer vision researchers in recent years. Given the rapid development, this article provides a comprehensive survey of more than 200 major fashion-related works covering four main aspects for enabling intelligent fashion: (1) Fashion detection includes landmark detection, fashion parsing, and item retrieval; (2) Fashion analysis contains attribute recognition, style learning, and popularity prediction; (3) Fashion synthesis involves style transfer, pose transformation, and physical simulation; and (4) Fashion recommendation comprises fashion compatibility, outfit matching, and hairstyle suggestion. For each task, the benchmark datasets and the evaluation protocols are summarized. Furthermore, we highlight promising directions for future research.

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Journal articles on the topic 'Computer vision recognition'

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