Download or read book A Neural Network Model of Visual Attention and Object Classification written by Hayden Walles and published by . This book was released on 2013 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Selective Visual Attention written by Liming Zhang and published by John Wiley & Sons. This book was released on 2013-03-15 with total page 344 pages. Available in PDF, EPUB and Kindle. Book excerpt: Visual attention is a relatively new area of study combining a number of disciplines: artificial neural networks, artificial intelligence, vision science and psychology. The aim is to build computational models similar to human vision in order to solve tough problems for many potential applications including object recognition, unmanned vehicle navigation, and image and video coding and processing. In this book, the authors provide an up to date and highly applied introduction to the topic of visual attention, aiding researchers in creating powerful computer vision systems. Areas covered include the significance of vision research, psychology and computer vision, existing computational visual attention models, and the authors' contributions on visual attention models, and applications in various image and video processing tasks. This book is geared for graduates students and researchers in neural networks, image processing, machine learning, computer vision, and other areas of biologically inspired model building and applications. The book can also be used by practicing engineers looking for techniques involving the application of image coding, video processing, machine vision and brain-like robots to real-world systems. Other students and researchers with interdisciplinary interests will also find this book appealing. Provides a key knowledge boost to developers of image processing applications Is unique in emphasizing the practical utility of attention mechanisms Includes a number of real-world examples that readers can implement in their own work: robot navigation and object selection image and video quality assessment image and video coding Provides codes for users to apply in practical attentional models and mechanisms
Download or read book Biologically based Interactive Neural Network Models for Visual Attention and Object Recognition written by Mohammad Saifullah and published by . This book was released on 2012 with total page 200 pages. Available in PDF, EPUB and Kindle. Book excerpt: The main focus of this thesis is to develop biologically-based computational models for object recognition. A series of models for attention and object recognition were developed in the order of increasing functionality and complexity. These models are based on information processing in the primate brain, and specially inspired from the theory of visual information processing along the two parallel processing pathways of the primate visual cortex. To capture the true essence of incremental, constraint satisfaction style processing in the visual system, interactive neural networks were used for implementing our models. Results from eye-tracking studies on the relevant visual tasks, as well as our hypothesis regarding the information processing in the primate visual system, were implemented in the models and tested with simulations. As a first step, a model based on the ventral pathway was developed to recognize single objects. Through systematic testing, structural and algorithmic parameters of these models were fine tuned for performing their task optimally. In the second step, the model was extended by considering the dorsal pathway, which enables simulation of visual attention as an emergent phenomenon. The extended model was then investigated for visual search tasks. In the last step, we focussed on occluded and overlapped object recognition. A couple of eye-tracking studies were conducted in this regard and on the basis of the results we made some hypotheses regarding information processing in the primate visual system. The models were further advanced on the lines of the presented hypothesis, and simulated on the tasks of occluded and overlapped object recognition. On the basis of the results and analysis of our simulations we have further found that the generalization performance of interactive hierarchical networks improves with the addition of a small amount of Hebbian learning to an otherwise pure error-driven learning. We also concluded that the size of the receptive fields in our networks is an important parameter for the generalization task and depends on the object of interest in the image. Our results show that networks using hard coded feature extraction perform better than the networks that use Hebbian learning for developing feature detectors. We have successfully demonstrated the emergence of visual attention within an interactive network and also the role of context in the search task. Simulation results with occluded and overlapped objects support our extended interactive processing approach, which is a combination of the interactive and top-down approach, to the segmentation-recognition issue. Furthermore, the simulation behavior of our models is in line with known human behavior for similar tasks. In general, the work in this thesis will improve the understanding and performance of biologically-based interactive networks for object recognition and provide a biologically-plausible solution to recognition of occluded and overlapped objects. Moreover, our models provide some suggestions for the underlying neural mechanism and strategies behind biological object recognition.
Download or read book Objekt Object Classification Using Simple Colour Based Visual Attention and a Hierarchical Neural Network for Neuro symbolic Integration written by and published by . This book was released on 1999 with total page 26 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Visual Attention and Cognition written by W.H. Zangemeister and published by Elsevier. This book was released on 1996-09-23 with total page 413 pages. Available in PDF, EPUB and Kindle. Book excerpt: The goal of this book is to put together some of the main interdisciplinary aspects that play a role in visual attention and cognition. The book is aimed at researchers and students with interdisciplinary interest. In the first chapter a general discussion of the influential scanpath theory and its implications for human and robot vision is presented. Subsequently, four characteristic aspects of the general theme are dealt with in topical chapters, each of which presents some of the different viewpoints of the various disciplines involved. They cover neuropsychology, clinical neuroscience, modeling, and applications. Each of the chapters opens with a synopsis tying together the individual contributions.
Download or read book VOCUS A Visual Attention System for Object Detection and Goal Directed Search written by Simone Frintrop and published by Springer Science & Business Media. This book was released on 2006-04-06 with total page 219 pages. Available in PDF, EPUB and Kindle. Book excerpt: This monograph presents a complete computational system for visual attention and object detection. VOCUS (Visual Object detection with a Computational attention System) represents a major step forward on integrating data-driven and model-driven information into a single framework. Additionally, the volume contains an extensive review of the literature on visual attention, detailed evaluations of VOCUS in different settings, and applications of the system.
Download or read book Biologically Motivated Computer Vision written by Heinrich H. Bülthoff and published by Springer. This book was released on 2003-08-02 with total page 676 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the refereed proceedings of the Second International Workshop on Biologically Motivated Computer Vision, BMCV 2002, held in Tübingen, Germany, in November 2002. The 22 revised full papers and 37 revised short papers presented together with 6 invited papers were carefully reviewed and selected from 97 submissions. The papers are organized in topical sections on neurons and features, motion, mid-level vision, recognition - from scenes to neurons, attention, robotics, and cognitive vision.
Download or read book Computer Vision ECCV 2016 written by Bastian Leibe and published by Springer. This book was released on 2016-09-16 with total page 902 pages. Available in PDF, EPUB and Kindle. Book excerpt: The eight-volume set comprising LNCS volumes 9905-9912 constitutes the refereed proceedings of the 14th European Conference on Computer Vision, ECCV 2016, held in Amsterdam, The Netherlands, in October 2016. The 415 revised papers presented were carefully reviewed and selected from 1480 submissions. The papers cover all aspects of computer vision and pattern recognition such as 3D computer vision; computational photography, sensing and display; face and gesture; low-level vision and image processing; motion and tracking; optimization methods; physicsbased vision, photometry and shape-from-X; recognition: detection, categorization, indexing, matching; segmentation, grouping and shape representation; statistical methods and learning; video: events, activities and surveillance; applications. They are organized in topical sections on detection, recognition and retrieval; scene understanding; optimization; image and video processing; learning; action activity and tracking; 3D; and 9 poster sessions.
Download or read book Visual Attention written by Anders Petersen and published by . This book was released on 2006 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:
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 Learning to Attend with Neural Networks written by Jimmy Ba and published by . This book was released on 2020 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: As more computational resources become widely available, artificial intelligence and machine learning researchers design ever larger and more complicated neural networks to learn from millions of data points. Although the traditional convolutional neural networks (CNNs) can achieve superhuman accuracy in object recognition tasks, they brute-force the problem by scanning over every location in the input images with the same fidelity. This thesis introduces a new class of neural networks inspired by the human visual system. Unlike CNNs that process the entire image at once into the current hidden layer, attention allows for salient features to dynamically come to the forefront as needed. The ability to attend is especially important when there is a lot of clutter in a scene. However, learning attention-based neural networks poses some challenges to the current machine learning techniques: What information should the neural network ``pay attention''? Where does the network store its sequences of ``glimpses''? Can our learning algorithms do better than simply ``trial-and-error''? To address these computational questions, we first describe a novel recurrent visual attention model in the context of variational inference. Because the standard REINFORCE or the trial-and-error algorithm can be slow due to its high variance gradient estimates, we show a re-weighted wake-sleep objective can improve the training performance. We also demonstrate the visual attention models outperform the previous state-of-the-art methods based on CNNs in the images and captions generation tasks. Furthermore, we discuss how the visual attention mechanism can improve the working memory of recurrent neural networks (RNNs) through a novel form of self-attention. The second half of the thesis focuses on gradient-based learning algorithms. We developed a new first-order optimization algorithm to overcome the slow convergence of the stochastic gradient descent algorithms in RNNs and attention-based models. In the end, we explored the benefit of applying second-order optimization methods in training neural networks.
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 Matters of Intelligence written by L.M. Vaina and published by Springer. This book was released on 1987-03-31 with total page 528 pages. Available in PDF, EPUB and Kindle. Book excerpt: This volume is not an attempt to give a comprehensive treatment of the many facets of intelligence. Rather, the intention is to present multiple approaches to interesting and novel ways of looking at old problems. The focus is on the visual and some of the conceptual intelligences. Vision is man's primary cognitive contact with the world around him, and we are vividly reminded of this by Roman Jakobson's autobiographical note, "The Evasive Initial" with which this volume begins. That we see the world as well as we do is something of a miracle. Looking out through our eyes, our brains give us reliable knowledge about the world around us in all it beauty of form, color and movement. The chapters in the first section look at how this may come about from various perspectives. How from the intensity array which the world casts on the eye's retina does the brain achieve recognition? What may be some of the processes involved in seeing? We see shapes, textures and colors, and subsequently, at the more cognitive levels, recognize them as objects which we can manipulate: we inspect them to discover what to use them for. The objects are tools or food; they are things, beautiful, lovable or frightening. They are things to remember and to talk about to our friends, or to ask someone for. We can ask for many or just a few. They are important to us or trivial.
Download or read book Artificial Neural Networks ICANN 2009 written by Cesare Alippi and published by Springer Science & Business Media. This book was released on 2009-09-03 with total page 1034 pages. Available in PDF, EPUB and Kindle. Book excerpt: This two volume set LNCS 5768 and LNCS 5769 constitutes the refereed proceedings of the 19th International Conference on Artificial Neural Networks, ICANN 2009, held in Limassol, Cyprus, in September 2009. The 200 revised full papers presented were carefully reviewed and selected from more than 300 submissions. The first volume is divided in topical sections on learning algorithms; computational neuroscience; hardware implementations and embedded systems; self organization; intelligent control and adaptive systems; neural and hybrid architectures; support vector machine; and recurrent neural network.
Download or read book Neural Network Models of Attentive Visual Search and Object Recognition written by William D. Ross and published by . This book was released on 1994 with total page 288 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Neural Networks and Brain Function written by Edmund T. Rolls and published by Oxford University Press, USA. This book was released on 1998 with total page 418 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book describes the types of computation that can be performed by biologically plausible neural networks and shows how they may be implemented in different systems of the brain. It is structured in three sections, each of which addresses a different need. The first introduces and analyzes the operation of several fundamental types of neural networks. The second discusses real neural networks in several brain systems, and shows how it is becoming possible to construct theories about the way different parts of the brain work. This section also analyzes the various neuroscience and neurocomputation techniques that need to be combined to ensure further progress in understanding the mechanism of brain processes. The third section, a collection of appendices. introduces the formal quantitative approaches to many of the networks described. Neural Networks and Brain Function is an accessible, clear introduction for researchers and students in neuroscience and artificial intelligence to the fascinating problems of how the brain works and how behavior is determined.
Download or read book A Neural Model Combining Attentional Orienting to Object Recognition Preliminary Explorations on the Interplay Between Where and What written by and published by . This book was released on 2001 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: We propose a model of primate vision that integrates both an attentional orienting ("where") pathway and an object recognition ("what") pathway. The fast visual attention front-end rapidly selects the few most conspicuous image locations, and the slower object recognition back-end identifies objects at the selected locations. The model is applied to classical visual search tasks, consisting of finding a specific target among an array of distracting visual patterns (e.g., a circle among many squares). The encouraging results obtained, in which substantial speedup is achieved by the combined attention- recognition model while maintaining good recognition performance compared to an exhaustive search, suggest that the biologically-inspired architecture proposed represents an efficient solution to the difficult problem of rapid scene analysis.
