Download or read book Deep Co occurrence Feature Learning for Visual Object Recognition written by 施雅方 and published by . This book was released on 2017 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Visual Object Recognition written by Kristen Grauman and published by Morgan & Claypool Publishers. This book was released on 2011 with total page 184 pages. Available in PDF, EPUB and Kindle. Book excerpt: The visual recognition problem is central to computer vision research. From robotics to information retrieval, many desired applications demand the ability to identify and localize categories, places, and objects. This tutorial overviews computer vision algorithms for visual object recognition and image classification. We introduce primary representations and learning approaches, with an emphasis on recent advances in the field. The target audience consists of researchers or students working in AI, robotics, or vision who would like to understand what methods and representations are available for these problems. This lecture summarizes what is and isn't possible to do reliably today, and overviews key concepts that could be employed in systems requiring visual categorization. Table of Contents: Introduction / Overview: Recognition of Specific Objects / Local Features: Detection and Description / Matching Local Features / Geometric Verification of Matched Features / Example Systems: Specific-Object Recognition / Overview: Recognition of Generic Object Categories / Representations for Object Categories / Generic Object Detection: Finding and Scoring Candidates / Learning Generic Object Category Models / Example Systems: Generic Object Recognition / Other Considerations and Current Challenges / Conclusions
Download or read book Object Detection with Deep Learning Models written by S Poonkuntran and published by CRC Press. This book was released on 2022-11-01 with total page 345 pages. Available in PDF, EPUB and Kindle. Book excerpt: Object Detection with Deep Learning Models discusses recent advances in object detection and recognition using deep learning methods, which have achieved great success in the field of computer vision and image processing. It provides a systematic and methodical overview of the latest developments in deep learning theory and its applications to computer vision, illustrating them using key topics, including object detection, face analysis, 3D object recognition, and image retrieval. The book offers a rich blend of theory and practice. It is suitable for students, researchers and practitioners interested in deep learning, computer vision and beyond and can also be used as a reference book. The comprehensive comparison of various deep-learning applications helps readers with a basic understanding of machine learning and calculus grasp the theories and inspires applications in other computer vision tasks. Features: A structured overview of deep learning in object detection A diversified collection of applications of object detection using deep neural networks Emphasize agriculture and remote sensing domains Exclusive discussion on moving object detection
Download or read book Advancement of Deep Learning and its Applications in Object Detection and Recognition written by Roohie Naaz Mir and published by CRC Press. This book was released on 2023-05-10 with total page 319 pages. Available in PDF, EPUB and Kindle. Book excerpt: Object detection is a basic visual identification problem in computer vision that has been explored extensively over the years. Visual object detection seeks to discover objects of specific target classes in a given image with pinpoint accuracy and apply a class label to each object instance. Object recognition strategies based on deep learning have been intensively investigated in recent years as a result of the remarkable success of deep learning-based image categorization. In this book, we go through in detail detector architectures, feature learning, proposal generation, sampling strategies, and other issues that affect detection performance. The book describes every newly proposed novel solution but skips through the fundamentals so that readers can see the field's cutting edge more rapidly. Moreover, unlike prior object detection publications, this project analyses deep learning-based object identification methods systematically and exhaustively, and also gives the most recent detection solutions and a collection of noteworthy research trends. The book focuses primarily on step-by-step discussion, an extensive literature review, detailed analysis and discussion, and rigorous experimentation results. Furthermore, a practical approach is displayed and encouraged.
Download or read book Combining Corpus Co occurrence and Attention with Visual Features for Object Recognition written by James Mountstephens and published by . This book was released on 2007 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Visual Object Tracking with Deep Neural Networks written by Pier Luigi Mazzeo and published by BoD – Books on Demand. This book was released on 2019-12-18 with total page 208 pages. Available in PDF, EPUB and Kindle. Book excerpt: Visual object tracking (VOT) and face recognition (FR) are essential tasks in computer vision with various real-world applications including human-computer interaction, autonomous vehicles, robotics, motion-based recognition, video indexing, surveillance and security. This book presents the state-of-the-art and new algorithms, methods, and systems of these research fields by using deep learning. It is organized into nine chapters across three sections. Section I discusses object detection and tracking ideas and algorithms; Section II examines applications based on re-identification challenges; and Section III presents applications based on FR research.
Download or read book Deep Learning in Object Recognition Detection and Segmentation written by Xiaogang Wang and published by . This book was released on 2016 with total page 165 pages. Available in PDF, EPUB and Kindle. Book excerpt: As a major breakthrough in artificial intelligence, deep learning has achieved very impressive success in solving grand challenges in many fields including speech recognition, natural language processing, computer vision, image and video processing, and multimedia. This article provides a historical overview of deep learning and focus on its applications in object recognition, detection, and segmentation, which are key challenges of computer vision and have numerous applications to images and videos. The discussed research topics on object recognition include image classification on ImageNet, face recognition, and video classification. The detection part covers general object detection on ImageNet, pedestrian detection, face landmark detection (face alignment), and human landmark detection (pose estimation). On the segmentation side, the article discusses the most recent progress on scene labeling, semantic segmentation, face parsing, human parsing and saliency detection. Object recognition is considered as whole-image classification, while detection and segmentation are pixelwise classification tasks. Their fundamental differences will be discussed in this article. Fully convolutional neural networks and highly efficient forward and backward propagation algorithms specially designed for pixelwise classification task will be introduced. The covered application domains are also much diversified. Human and face images have regular structures, while general object and scene images have much more complex variations in geometric structures and layout. Videos include the temporal dimension. Therefore, they need to be processed with different deep models. All the selected domain applications have received tremendous attentions in the computer vision and multimedia communities. Through concrete examples of these applications, we explain the key points which make deep learning outperform conventional computer vision systems. (1) Different than traditional pattern recognition systems, which heavily rely on manually designed features, deep learning automatically learns hierarchical feature representations from massive training data and disentangles hidden factors of input data through multi-level nonlinear mappings. (2) Different than existing pattern recognition systems which sequentially design or train their key components, deep learning is able to jointly optimize all the components and crate synergy through close interactions among them. (3) While most machine learning models can be approximated with neural networks with shallow structures, for some tasks, the expressive power of deep models increases exponentially as their architectures go deep. Deep models are especially good at learning global contextual feature representation with their deep structures. (4) Benefitting from the large learning capacity of deep models, some classical computer vision challenges can be recast as high-dimensional data transform problems and can be solved from new perspectives. Finally, some open questions and future works regarding to deep learning in object recognition, detection, and segmentation will be discussed.
Download or read book Computer Vision ACCV 2020 written by Hiroshi Ishikawa and published by Springer Nature. This book was released on 2021-02-24 with total page 730 pages. Available in PDF, EPUB and Kindle. Book excerpt: The six volume set of LNCS 12622-12627 constitutes the proceedings of the 15th Asian Conference on Computer Vision, ACCV 2020, held in Kyoto, Japan, in November/ December 2020.* The total of 254 contributions was carefully reviewed and selected from 768 submissions during two rounds of reviewing and improvement. The papers focus on the following topics: Part I: 3D computer vision; segmentation and grouping Part II: low-level vision, image processing; motion and tracking Part III: recognition and detection; optimization, statistical methods, and learning; robot vision Part IV: deep learning for computer vision, generative models for computer vision Part V: face, pose, action, and gesture; video analysis and event recognition; biomedical image analysis Part VI: applications of computer vision; vision for X; datasets and performance analysis *The conference was held virtually.
Download or read book Image Processing written by Maria M. P. Petrou and published by John Wiley & Sons. This book was released on 2021-01-05 with total page 816 pages. Available in PDF, EPUB and Kindle. Book excerpt: The classic text that covers practical image processing methods and theory for image texture analysis, updated second edition The revised second edition of Image Processing: Dealing with Textures updates the classic work on texture analysis theory and methods without abandoning the foundational essentials of this landmark work. Like the first, the new edition offers an analysis of texture in digital images that are essential to a diverse range of applications such as: robotics, defense, medicine and the geo-sciences. Designed to easily locate information on specific problems, the text is structured around a series of helpful questions and answers. Updated to include the most recent developments in the field, many chapters have been completely revised including: Fractals and Multifractals, Image Statistics, Texture Repair, Local Phase Features, Dual Tree Complex Wavelet Transform, Ridgelets and Curvelets and Deep Texture Features. The book takes a two-level mathematical approach: light math is covered in the main level of the book, with harder math identified in separate boxes. This important text: Contains an update of the classic advanced text that reviews practical image processing methods and theory for image texture analysis Puts the focus exclusively on an in-depth exploration of texture Contains a companion website with exercises and algorithms Includes examples that are fully worked to enhance the learning experience Written for students and researchers of image processing, the second edition of Image Processing has been revised and updated to incorporate the foundational information on the topic and information on the latest advances.
Download or read book International Conference on Computing Communication Electrical and Biomedical Systems written by Arulmurugan Ramu and published by Springer Nature. This book was released on 2022-02-28 with total page 673 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book presents selected papers from the International Conference on Computing, Communication, Electrical and Biomedical Systems (ICCCEBS 2021), held in March 2021 at KPR College of Engineering and Technology, Coimbatore, Tamil Nadu, India. The conference explores the interface between industry and real-time environments with newly developed techniques in computing and communications engineering. The papers describe results of conceptual, constructive, empirical, experimental, and theoretical work in areas of computing, communication, electrical, and biomedical systems. Contributors include academic scientists, researchers, industry representatives, postdoctoral fellows, and research scholars from around the world.
Download or read book Visual Object Tracking from Correlation Filter to Deep Learning written by Weiwei Xing and published by Springer Nature. This book was released on 2021-11-18 with total page 202 pages. Available in PDF, EPUB and Kindle. Book excerpt: The book focuses on visual object tracking systems and approaches based on correlation filter and deep learning. Both foundations and implementations have been addressed. The algorithm, system design and performance evaluation have been explored for three kinds of tracking methods including correlation filter based methods, correlation filter with deep feature based methods, and deep learning based methods. Firstly, context aware and multi-scale strategy are presented in correlation filter based trackers; then, long-short term correlation filter, context aware correlation filter and auxiliary relocation in SiamFC framework are proposed for combining correlation filter and deep learning in visual object tracking; finally, improvements in deep learning based trackers including Siamese network, GAN and reinforcement learning are designed. The goal of this book is to bring, in a timely fashion, the latest advances and developments in visual object tracking, especially correlation filter and deep learning based methods, which is particularly suited for readers who are interested in the research and technology innovation in visual object tracking and related fields.
Download or read book Deep Learning in Object Detection and Recognition written by Xiaoyue Jiang and published by Springer. This book was released on 2020-11-27 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book discusses recent advances in object detection and recognition using deep learning methods, which have achieved great success in the field of computer vision and image processing. It provides a systematic and methodical overview of the latest developments in deep learning theory and its applications to computer vision, illustrating them using key topics, including object detection, face analysis, 3D object recognition, and image retrieval. The book offers a rich blend of theory and practice. It is suitable for students, researchers and practitioners interested in deep learning, computer vision and beyond and can also be used as a reference book. The comprehensive comparison of various deep-learning applications helps readers with a basic understanding of machine learning and calculus grasp the theories and inspires applications in other computer vision tasks.
Download or read book Computer Vision ACCV 2016 Workshops written by Chu-Song Chen and published by Springer. This book was released on 2017-03-14 with total page 666 pages. Available in PDF, EPUB and Kindle. Book excerpt: The three-volume set, consisting of LNCS 10116, 10117, and 10118, contains carefully reviewed and selected papers presented at 17 workshops held in conjunction with the 13th Asian Conference on Computer Vision, ACCV 2016, in Taipei, Taiwan in November 2016. The 134 full papers presented were selected from 223 submissions. LNCS 10116 contains the papers selected
Download or read book Computer Vision and Robotics written by Praveen Kumar Shukla and published by Springer Nature. This book was released on 2023-04-27 with total page 559 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book is a collection of the high-quality research articles in the field of computer vision and robotics which are presented in International Conference on Computer Vision and Robotics (ICCVR 2022), organized by BBD University Lucknow India, during 21 – 22 May 2022. The book discusses applications of computer vision and robotics in the fields like medical science, defence and smart city planning. This book presents recent works from researchers, academicians, industry, and policy makers.
Download or read book Object Recognition Attention and Action written by Naoyuki Osaka and published by Springer Science & Business Media. This book was released on 2009-03-12 with total page 252 pages. Available in PDF, EPUB and Kindle. Book excerpt: Human object recognition is a classical topic both for philosophy and for the natural sciences. Ultimately, understanding of object recognition will be promoted by the cooperation of behavioral research, neurophysiology, and computation. This original book provides an excellent introduction to the issues that are involved. It contains chapters that address the ways in which humans and machines attend to, recognize, and act toward objects in the visual environment.
Download or read book Proceedings of 5th International Conference on the Industry 4 0 Model for Advanced Manufacturing written by Lihui Wang and published by Springer Nature. This book was released on 2020-05-15 with total page 370 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book gathers the proceedings of the 5th International Conference on the Industry 4.0 Model for Advanced Manufacturing (AMP 2020), held in Belgrade, Serbia, on 1–4 June 2020. The event marks the latest in a series of high-level conferences that bring together experts from academia and industry to exchange knowledge, ideas, experiences, research findings, and information in the field of manufacturing. The book addresses a wide range of topics, including: design of smart and intelligent products, developments in CAD/CAM technologies, rapid prototyping and reverse engineering, multistage manufacturing processes, manufacturing automation in the Industry 4.0 model, cloud-based products, and cyber-physical and reconfigurable manufacturing systems. By providing updates on key issues and highlighting recent advances in manufacturing engineering and technologies, the book supports the transfer of vital knowledge to the next generation of academics and practitioners. Further, it will appeal to anyone working or conducting research in this rapidly evolving field.
Download or read book Towards The Deep Model written by Panqu Wang and published by . This book was released on 2017 with total page 186 pages. Available in PDF, EPUB and Kindle. Book excerpt: Understanding how visual recognition is achieved in the human brain is one of the most fundamental questions in vision research. In this thesis I seek to tackle this problem from a neurocomputational modeling perspective. More specifically, I build machine learning-based models to simulate and explain cognitive phenomena related to human visual recognition, and I improve computational models using brain-inspired principles to excel at computer vision tasks. I first describe how a neurocomputational model ("The Model", TM, (Cottrell & Hsiao, 2011)) can be applied to explain the modulation of visual experience on the performance of subordinate-level face and object recognition. Next, by introducing a mixture-of-experts structure in the model, I show that TM can be used to simulate the development of hemispheric lateralization of face processing. In addition, I extend TM to "The Deep Model" (TDM) by coupling it with deep learning techniques, and use TDM to explain the peripheral vision advantage in human scene recognition. Furthermore, I show the performance of these computational methods can be improved by introducing realistic constraints based on the human brain. By combining unsupervised feature learning principles with the Gnostic Fields theory of how the brain performs object recognition across the ventral visual pathway, I show a biologically-inspired model can develop realistic features of the early visual cortex, while performing well on object recognition datasets. By designing better encoding and decoding strategies in the deep neural network, I demonstrate that our system achieves the state-of-the-art performance on pixel-level semantic segmentation task on many popular computer vision benchmarks.
