Download or read book Investigation of a Neural Network Model for Human Early Visual Perception written by Mei Wu and published by . This book was released on 1990 with total page 200 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Vision written by Jeanny Hrault and published by World Scientific. This book was released on 2010 with total page 308 pages. Available in PDF, EPUB and Kindle. Book excerpt: At the fascinating frontiers of neurobiology, mathematics and psychophysics, this book addresses the problem of human and computer vision on the basis of cognitive modeling. After recalling the physics of light and its transformation through media and optics, Hrault presents the principles of the primate's visual system in terms of anatomy and functionality. Then, the neuronal circuitry of the retina is analyzed in terms of spatio?temporal filtering. This basic model is extended to the concept of neuromorphic circuits for motion processing and to the processing of color in the retina. For more in-depth studies, the adaptive non-linear properties of the photoreceptors and of ganglion cells are addressed, exhibiting all the power of the retinal pre-processing of images as a system of information cleaning suitable for further cortical processing. As a target of retinal information, the primary visual area is presented as a bank of filters able to extract valuable descriptors of images, suitable for categorization and recognition and also for local information extraction such as saliency and perspective. All along the book, many comparisons between the models and human perception are discussed as well as detailed applications to computer vision.
Download or read book Adapting Deep Neural Networks as Models of Human Visual Perception written by Patrick McClure and published by . This book was released on 2018 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Perceptual Learning written by Barbara Dosher and published by MIT Press. This book was released on 2020-10-13 with total page 521 pages. Available in PDF, EPUB and Kindle. Book excerpt: A comprehensive and integrated introduction to the phenomena and theories of perceptual learning, focusing on the visual domain. Practice or training in perceptual tasks improves the quality of perceptual performance, often by a substantial amount. This improvement is called perceptual learning (in contrast to learning in the cognitive or motor domains), and it has become an active area of research of both theoretical and practical significance. This book offers a comprehensive introduction to the phenomena and theories of perceptual learning, focusing on the visual domain. Perceptual Learning explores the tradeoff between the competing goals of system stability and system adaptability, signal and noise, retuning and reweighting, and top-down versus bottom-down processes. It examines and evaluates existing research and potential future directions, including evidence from behavior, physiology, and brain imaging, and existing perceptual learning applications, with a focus on important theories and computational models. It also compares visual learning to learning in other perceptual domains, and considers the application of visual training methods in the development of perceptual expertise and education as well as in remediation for limiting visual conditions. It provides an integrated treatment of the subject for students and researchers and for practitioners who want to incorporate perceptual learning into their practice.Practice or training in perceptual tasks improves the quality of perceptual performance, often by a substantial amount. This improvement is called perceptual learning, in contrast with learning in the cognitive or motor domains. Perceptual learning has been a very active area of research of both theoretical and practical interest. Research on perceptual learning is of theoretical significance in illuminating plasticity in adult perceptual systems, and in understanding the limitations of human information processing and how to improve them. It is of practical significance as a potential method for the development of perceptual expertise in the normal population, for its potential in advancing development and supporting healthy aging, and for noninvasive amelioration of deficits in challenged populations by training. Perceptual learning has become an increasingly important topic in biomedical research. Practitioners in this area include science disciplines such as psychology, neuroscience, computer sciences, and optometry, and developers in applied areas of learning game design, cognitive development and aging, and military and biomedical applications. Commercial development of training products, protocols, and games is a multi-billion dollar industry. Perceptual learning provides the basis for many of the developments in these areas. This book is written for anyone who wants to understand the phenomena and theories of perceptual learning or to apply the technology of perceptual learning to the development of training methods and products. Our aim is to provide an introduction to those researchers and students just entering this exciting field, to provide a comprehensive and integrated treatment of the phenomena and the theories of perceptual learning for active perceptual learning researchers, and to describe and develop the basic techniques and principles for readers who want to successfully incorporate perceptual learning into applied developments. The book considers the special challenges of perceptual learning that balance the competing goals of system stability and system adaptability. It provides a systematic treatment of the major phenomena and models in perceptual learning, the determinants of successful learning and of specificity and transfer. The book provides a cohesive consideration of the broad range of perceptual learning through the theoretical framework of incremental learning of reweighting evidence that supports successful task performance. It provides a detailed analysis of the mechanisms by which perceptual learning improves perceptual limitations, the relationship of perceptual learning and the critical period of development, and the semi-supervised modes of learning that dominate perceptual learning. It considers limitations and constraints on learning multiple tasks and stimuli simultaneously, the implications of training at high or low levels of performance accuracy, and the importance of feedback to perceptual learning. The basis of perceptual learning in physiology is discussed along with the relationship of visual perceptual learning to learning in other sensory domains. The book considers the applications of perceptual learning in the development of expertise, in education and gaming, in training during development and aging, and applications to remediation of mental health and vision disorders. Finally, it applies the phenomena and models of perceptual learning to considerations of optimizing training.
Download or read book Vision written by David Marr and published by MIT Press. This book was released on 2010-07-09 with total page 429 pages. Available in PDF, EPUB and Kindle. Book excerpt: Available again, an influential book that offers a framework for understanding visual perception and considers fundamental questions about the brain and its functions. David Marr's posthumously published Vision (1982) influenced a generation of brain and cognitive scientists, inspiring many to enter the field. In Vision, Marr describes a general framework for understanding visual perception and touches on broader questions about how the brain and its functions can be studied and understood. Researchers from a range of brain and cognitive sciences have long valued Marr's creativity, intellectual power, and ability to integrate insights and data from neuroscience, psychology, and computation. This MIT Press edition makes Marr's influential work available to a new generation of students and scientists. In Marr's framework, the process of vision constructs a set of representations, starting from a description of the input image and culminating with a description of three-dimensional objects in the surrounding environment. A central theme, and one that has had far-reaching influence in both neuroscience and cognitive science, is the notion of different levels of analysis—in Marr's framework, the computational level, the algorithmic level, and the hardware implementation level. Now, thirty years later, the main problems that occupied Marr remain fundamental open problems in the study of perception. Vision provides inspiration for the continuing efforts to integrate knowledge from cognition and computation to understand vision and the brain.
Download or read book Visual Perception of Depth from Occlusion A Neural Network Model written by and published by . This book was released on 1990 with total page 1 pages. Available in PDF, EPUB and Kindle. Book excerpt: This report covers research activity during the three month period from 6/1/90 to 9/1/90. We have been engaged in several preparatory projects aimed at developing a biologically-based model of how the visual cortex extracts depth-from-occlusion. Our major effort has focused on writing software for a large-scale neural network simulator. This simulator, which is now nearing testing phase, will allow the simulation of physiologically based networks of hundreds of thousands of interconnected cells. The simulator features a graphical user interface that controls the building, analyzing, running, and monitoring of multiple interconnected networks. The states of neurons (voltage, firing rate, ion concentrations, etc.) can be displayed in a variety of graphical formats depending upon the type of data required. We feel that, when complete, this package will be the most useful tool available to the community for simulating large, mapped networks.
Download or read book Early Visual Learning written by Shree K. Nayar and published by . This book was released on 1996 with total page 392 pages. Available in PDF, EPUB and Kindle. Book excerpt: Featuring contributions from experts in the field of computer vision, Early Visual Learning represents the cutting edge of research in this field. The editors focus on learning techniques that are applied more or less directly to the signals provided by vision sensors. The emphasis is on low-level visual learning techniques that draw on results in the fields of statistics, pattern recognition and neural networks. This book will be of interest to researchers and has potential as a graduate level text in a visual learning course.
Download or read book Studies of Human Vision written by Luiz Pessoa and published by . This book was released on 1996 with total page 256 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Explainable AI Interpreting Explaining and Visualizing Deep Learning written by Wojciech Samek and published by Springer Nature. This book was released on 2019-09-10 with total page 435 pages. Available in PDF, EPUB and Kindle. Book excerpt: The development of “intelligent” systems that can take decisions and perform autonomously might lead to faster and more consistent decisions. A limiting factor for a broader adoption of AI technology is the inherent risks that come with giving up human control and oversight to “intelligent” machines. For sensitive tasks involving critical infrastructures and affecting human well-being or health, it is crucial to limit the possibility of improper, non-robust and unsafe decisions and actions. Before deploying an AI system, we see a strong need to validate its behavior, and thus establish guarantees that it will continue to perform as expected when deployed in a real-world environment. In pursuit of that objective, ways for humans to verify the agreement between the AI decision structure and their own ground-truth knowledge have been explored. Explainable AI (XAI) has developed as a subfield of AI, focused on exposing complex AI models to humans in a systematic and interpretable manner. The 22 chapters included in this book provide a timely snapshot of algorithms, theory, and applications of interpretable and explainable AI and AI techniques that have been proposed recently reflecting the current discourse in this field and providing directions of future development. The book is organized in six parts: towards AI transparency; methods for interpreting AI systems; explaining the decisions of AI systems; evaluating interpretability and explanations; applications of explainable AI; and software for explainable AI.
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 Deep Neural Network Applications written by Hasmik Osipyan and published by CRC Press. This book was released on 2022-04-28 with total page 158 pages. Available in PDF, EPUB and Kindle. Book excerpt: The world is on the verge of fully ushering in the fourth industrial revolution, of which artificial intelligence (AI) is the most important new general-purpose technology. Like the steam engine that led to the widespread commercial use of driving machineries in the industries during the first industrial revolution; the internal combustion engine that gave rise to cars, trucks, and airplanes; electricity that caused the second industrial revolution through the discovery of direct and alternating current; and the Internet, which led to the emergence of the information age, AI is a transformational technology. It will cause a paradigm shift in the way’s problems are solved in every aspect of our lives, and, from it, innovative technologies will emerge. AI is the theory and development of machines that can imitate human intelligence in tasks such as visual perception, speech recognition, decision-making, and human language translation. This book provides a complete overview on the deep learning applications and deep neural network architectures. It also gives an overview on most advanced future-looking fundamental research in deep learning application in artificial intelligence. Research overview includes reasoning approaches, problem solving, knowledge representation, planning, learning, natural language processing, perception, motion and manipulation, social intelligence and creativity. It will allow the reader to gain a deep and broad knowledge of the latest engineering technologies of AI and Deep Learning and is an excellent resource for academic research and industry applications.
Download or read book Cortical Neural Network Models of Visual Motion Perception for Decision Making and Reactive Navigation written by Michael Beyeler and published by . This book was released on 2016 with total page 212 pages. Available in PDF, EPUB and Kindle. Book excerpt: Animals use vision to traverse novel cluttered environments with apparent ease. Evidence suggests that the mammalian brain integrates visual motion cues across a number of remote but interconnected brain regions that make up a visual motion pathway. Although much is known about the neural circuitry that is concerned with motion perception in the Primary Visual Cortex (V1) and the Middle Temporal area (MT), little is known about how relevant perceptual variables might be represented in higher-order areas of the motion pathway, and how neural activity in these areas might relate to the behavioral dynamics of locomotion.The main goal of this dissertation is to investigate the computational principles that the mammalian brain might be using to organize low-level motion signals into distributed representations of perceptual variables, and how neural activity in the motion pathway might mediate behavior in reactive navigation tasks. I first investigated how the aperture problem, a fundamental conceptual challenge encountered by all low-level motion systems, can be solved in a spiking neural network model of V1 and MT (consisting of 153,216 neurons and 40 million synapses), relying solely on dynamics and properties gleaned from known electrophysiological and neuroanatomical evidence, and how this neural activity might influence perceptual decision-making. Second, when used with a physical robot performing a reactive navigation task in the real world, I found that the model produced behavioral trajectories that closely matched human psychophysics data. Essential to the success of these studies were software implementations that could execute in real time, which are freely and openly available to the community. Third, using ideas from the efficient-coding and free-energy principles, I demonstrated that a variety of response properties of neurons in the dorsal sub-region of the Medial Superior Temporal area (MSTd) area could be derived from MT-like input features. This finding suggests that response properties such as 3D translation and rotation selectivity, complex motion perception, and heading selectivity might simply be a by-product of MSTd neurons performing dimensionality reduction on their inputs. The hope is that these studies will not only further our understanding of how the brain works, but also lead to novel algorithms and brain-inspired robots capable of outperforming current artificial systems.
Download or read book Modeling Human Vision Using Feedforward Neural Networks written by Francis Xinghang Chen and published by . This book was released on 2016 with total page 86 pages. Available in PDF, EPUB and Kindle. Book excerpt: In this thesis, we discuss the implementation, characterization, and evaluation of a new computational model for human vision. Our goal is to understand the mechanisms enabling invariant perception under scaling, translation, and clutter. The model is based on I-Theory [50], and uses convolutional neural networks. We investigate the explanatory power of this approach using the task of object recognition. We find that the model has important similarities with neural architectures and that it can reproduce human perceptual phenomena. This work may be an early step towards a more general and unified human vision model.
Download or read book From Line Drawings to Human Actions written by Fang Wang and published by . This book was released on 2017 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: In recent years, deep neural networks have been very successful in computer vision, speech recognition, and artificial intelligent systems. The rapid growth of data and fast increasing computational tools provide solid foundations for the applications which rely on the learning of large scale deep neural networks with millions of parameters. The deep learning approaches have been proved to be able to learn powerful representations of the inputs in various tasks, such as image classification, object recognition, and scene understanding. This thesis demonstrates the generality and capacity of deep learning approaches through a series of case studies including image matching and human activity understanding. In these studies, I explore the combinations of the neural network models with existing machine learning techniques and extend the deep learning approach for each task. Four related tasks are investigated: 1) image matching through similarity learning; 2) human action prediction; 3) finger force estimation in manipulation actions; and 4) bimodal learning for human action understanding. Deep neural networks have been shown to be very efficient in supervised learning. Further, in some tasks, one would like to group the features of the samples in the same category close to each other, in additional to the discriminative representation. Such kind of properties is desired in a number of applications, such as semantic retrieval, image quality measurement, and social network analysis, etc. My first study is to develop a similarity learning method based on deep neural networks for image matching between sketch images and 3D models. In this task, I propose to use Siamese network to learn similarities of sketches and develop a novel method for sketch based 3D shape retrieval. The proposed method can successfully learn the representations of sketch images as well as the similarities, then the 3D shape retrieval problem can be solved with off-the-shelf nearest neighbor methods. After studying the representation learning methods for static inputs, my focus turns to learning the representations of sequential data. To be specific, I focus on manipulation actions, because they are widely used in the daily life and play important parts in the human-robot collaboration system. Deep neural networks have been shown to be powerful to represent short video clips [Donahue et al., 2015]. However, most existing methods consider the action recognition problem as a classification task. These methods assume the inputs are pre-segmented videos and the outputs are category labels. In the scenarios such as the human-robot collaboration system, the ability to predict the ongoing human actions at an early stage is highly important. I first attempt to address this issue with a fast manipulation action prediction method. Then I build the action prediction model based on Long Short-Term Memory (LSTM) architecture. The proposed approach processes the sequential inputs as continuous signals and keeps updating the prediction of the intended action based on the learned action representations. Further, I study the relationships between visual inputs and the physical information, such as finger forces, that involved in the manipulation actions. This is motivated by recent studies in cognitive science which show that the subject's intention is strongly related to the hand movements during an action execution. Human observers can interpret other's actions in terms of movements and forces, which can be used to repeat the observed actions. If a robot system has the ability to estimate the force feedbacks, it can learn how to manipulate an object by watching human demonstrations. In this work, the finger forces are estimated by only watching the movement of hands. A modified LSTM model is used to regress the finger forces from video frames. To facilitate this study, a specially designed sensor glove has been used to collect data of finger forces, and a new dataset has been collected to provide synchronized streams of videos and finger forces. Last, I investigate the usefulness of physical information in human action recognition, which is an application of bimodal learning, where both the vision inputs and the additional information are used to learn the action representation. My study demonstrates that, by combining additional information with the vision inputs, the accuracy of human action recognition can be improved steadily. I extend the LSTM architecture to accept both video frames and sensor data as bimodal inputs to predict the action. A hallucination network is jointly trained to approximate the representations of the additional inputs. During the testing stage, the hallucination network generates approximated representations that used for classification. In this way, the proposed method does not rely on the additional inputs for testing.
Download or read book Hierarchical Neural Networks for Image Interpretation written by Sven Behnke and published by Springer Science & Business Media. This book was released on 2003-08-21 with total page 230 pages. Available in PDF, EPUB and Kindle. Book excerpt: Human performance in visual perception by far exceeds the performance of contemporary computer vision systems. While humans are able to perceive their environment almost instantly and reliably under a wide range of conditions, computer vision systems work well only under controlled conditions in limited domains. This book sets out to reproduce the robustness and speed of human perception by proposing a hierarchical neural network architecture for iterative image interpretation. The proposed architecture can be trained using unsupervised and supervised learning techniques. Applications of the proposed architecture are illustrated using small networks. Furthermore, several larger networks were trained to perform various nontrivial computer vision tasks.
Download or read book An Artificial Model for Understanding from Images Inspired by Human Perceptive and Cognitive System written by Jeewanee Tharanga Bamunusinghe Bamunusinghe Arachchige and published by . This book was released on 2013 with total page 278 pages. Available in PDF, EPUB and Kindle. Book excerpt: Artificial Intelligence (AI) is an exciting and fascinating research area in computer science emerging to develop machines with human intelligence or human behaviour. One of the goals in AI is to develop algorithms and techniques based on the study of pinnacle of intelligence, human brain. The new direction makes a breakthrough in AI while facilitating powerful clues to the neuroscience to understand the unrevealed mechanisms in human brain.The traditional means of manipulating visual inputs by artificial systems are highly dependent on human intervention. A study of human vision system can be considered as a promising direction towards achieving truly intelligent artificial vision system. In this thesis, the main contribution is to investigate the human visual system from biological and psychological perspective and propose a new model for artificial perception.The new model, Artificial Model for Visual Perception (AMVP), accumulates knowledge from the environment in different aspects and abstraction levels and generate interpretations similar way to the humans using past experiences. The AMVP model is domain independent and has the flexibility to tailor for any situation. The subsequent aim of the thesis is to propose implementation architecture for the conceptual framework. Our implementation was based on the structure adaptive neural network. The conceptual model is applied in a real-life image data set and the functionalities of the proposed model are demonstrated.The difficulties faced during the feature extraction from the images for implementation of the conceptual model encouraged us to perform an analysis of differences between the visual inputs received by humans and artificial systems. This study continues to investigate the differences between subsequent stages of the human visual pathway with the methods used by artificial systems.This thesis also propose a possible representation for an artificial percept.
Download or read book Research Awards Index written by and published by . This book was released on 1989 with total page 776 pages. Available in PDF, EPUB and Kindle. Book excerpt:
