Download or read book Enabling Scalable and Efficient Visual Attention Object based Attention and Object Recognition for Humanoid Robots a Biologically inspired Approach written by Andreas Holzbach and published by . This book was released on 2016 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Visual Perception for Humanoid Robots written by David Israel González Aguirre and published by Springer. This book was released on 2018-09-01 with total page 253 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book provides an overview of model-based environmental visual perception for humanoid robots. The visual perception of a humanoid robot creates a bidirectional bridge connecting sensor signals with internal representations of environmental objects. The objective of such perception systems is to answer two fundamental questions: What & where is it? To answer these questions using a sensor-to-representation bridge, coordinated processes are conducted to extract and exploit cues matching robot’s mental representations to physical entities. These include sensor & actuator modeling, calibration, filtering, and feature extraction for state estimation. This book discusses the following topics in depth: • Active Sensing: Robust probabilistic methods for optimal, high dynamic range image acquisition are suitable for use with inexpensive cameras. This enables ideal sensing in arbitrary environmental conditions encountered in human-centric spaces. The book quantitatively shows the importance of equipping robots with dependable visual sensing. • Feature Extraction & Recognition: Parameter-free, edge extraction methods based on structural graphs enable the representation of geometric primitives effectively and efficiently. This is done by eccentricity segmentation providing excellent recognition even on noisy & low-resolution images. Stereoscopic vision, Euclidean metric and graph-shape descriptors are shown to be powerful mechanisms for difficult recognition tasks. • Global Self-Localization & Depth Uncertainty Learning: Simultaneous feature matching for global localization and 6D self-pose estimation are addressed by a novel geometric and probabilistic concept using intersection of Gaussian spheres. The path from intuition to the closed-form optimal solution determining the robot location is described, including a supervised learning method for uncertainty depth modeling based on extensive ground-truth training data from a motion capture system. The methods and experiments are presented in self-contained chapters with comparisons and the state of the art. The algorithms were implemented and empirically evaluated on two humanoid robots: ARMAR III-A & B. The excellent robustness, performance and derived results received an award at the IEEE conference on humanoid robots and the contributions have been utilized for numerous visual manipulation tasks with demonstration at distinguished venues such as ICRA, CeBIT, IAS, and Automatica.
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 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 Visual Attention for Robotic Cognition written by Momotaz Begum and published by . This book was released on 2010 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 Visual Perception and Robotic Manipulation written by Geoffrey Taylor and published by Springer. This book was released on 2008-08-18 with total page 231 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book moves toward the realization of domestic robots by presenting an integrated view of computer vision and robotics, covering fundamental topics including optimal sensor design, visual servo-ing, 3D object modelling and recognition, and multi-cue tracking, emphasizing robustness throughout. Covering theory and implementation, experimental results and comprehensive multimedia support including video clips, VRML data, C++ code and lecture slides, this book is a practical reference for roboticists and a valuable teaching resource.
Download or read book Modelling Active Bio inspired Object Recognition in Autonomous Mobile Agents written by Edgar Bermudez Contreras and published by . This book was released on 2010 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Object recognition is arguably one of the main tasks carried out by the visual cortex. This task has been studied for decades and is one of the main topics being investigated in the computer vision field. While vertebrates perform this task with exceptional reliability and in very short amounts of time, the visual processes involved are still not completely understood. Considering the desirable properties of the visual systems in nature, many models have been proposed to not only match their performance in object recognition tasks, but also to study and understand the object recognition processes in the brain. One important point most of the classical models have failed to consider when modelling object recognition is the fact that all the visual systems in nature are active. Active object recognition opens different perspectives in contrast with the classical isolated way of modelling neural processes such as the exploitation of the body to aid the perceptual processes. Biologically inspired models are a good alternative to study embodied object recognition since animals are a working example that demonstrates that object recognition can be performed with great efficiency in an active manner. In this thesis I study biologically inspired models for object recognition from an active perspective. I demonstrate that by considering the problem of object recognition from this perspective, the computational complexity present in some of the classical models of object recognition can be reduced. In particular, chapter 3 compares a simple V1-like model (RBF model) with a complex hierarchical model (HMAX model) under certain conditions which make the RBF model perform as the HMAX model when using a simple attentional mechanism. Additionally, I compare the RBF and HMAX model with some other visual systems using well-known object libraries. This comparison demonstrates that the performance of the implementations of the RBF and HMAX models employed in this thesis is similar to the performance of other state-of-the-art visual systems. In chapter 4, I study the role of sensors in the neural dynamics of controllers and the behaviour of simulated agents. I also show how to employ an Evolutionary Robotics approach to study autonomous mobile agents performing visually guided tasks. In addition, in chapter 5 I investigate whether the variation in the visual information, which is determined by simple movements of an agent, can impact the performance of the RBF and HMAX models. In chapter 6 I investigate the impact of several movement strategies in the recognition performance of the models. In particular I study the impact of the variation in visual information using different movement strategies to collect training views. In addition, I show that temporal information can be exploited to improve the object recognition performance using movement strategies. In chapter 7 experiments to study the exploitation of movement and temporal information are carried out in a real world scenario using a robot. These experiments validate the results obtained in simulations in the previous chapters. Finally, in chapter 8 I show that by exploiting regularities in the visual input imposed by movement in the selection of training views, the complexity of the RBF model can be reduced in a real robot. The approach of this work proposes to gradually increase the complexity of the processes involved in active object recognition, from studying the role of moving the focus of attention while comparing object recognition models in static tasks, to analysing the exploitation of an active approach in the selection of training views for a object recognition task in a real world robot.
Download or read book Visual Perception for Manipulation and Imitation in Humanoid Robots written by Pedram Azad and published by Springer Science & Business Media. This book was released on 2009-11-19 with total page 273 pages. Available in PDF, EPUB and Kindle. Book excerpt: Dealing with visual perception in robots and its applications to manipulation and imitation, this monograph focuses on stereo-based methods and systems for object recognition and 6 DoF pose estimation as well as for marker-less human motion capture.
Download or read book Visual Object Recognition written by Kristen Thielscher and published by Springer Nature. This book was released on 2022-05-31 with total page 163 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 From Human Attention to Computational Attention written by Matei Mancas and published by Springer. This book was released on 2016-06-29 with total page 456 pages. Available in PDF, EPUB and Kindle. Book excerpt: This both accessible and exhaustive book will help to improve modeling of attention and to inspire innovations in industry. It introduces the study of attention and focuses on attention modeling, addressing such themes as saliency models, signal detection and different types of signals, as well as real-life applications. The book is truly multi-disciplinary, collating work from psychology, neuroscience, engineering and computer science, amongst other disciplines. What is attention? We all pay attention every single moment of our lives. Attention is how the brain selects and prioritizes information. The study of attention has become incredibly complex and divided: this timely volume assists the reader by drawing together work on the computational aspects of attention from across the disciplines. Those working in the field as engineers will benefit from this book’s introduction to the psychological and biological approaches to attention, and neuroscientists can learn about engineering work on attention. The work features practical reviews and chapters that are quick and easy to read, as well as chapters which present deeper, more complex knowledge. Everyone whose work relates to human perception, to image, audio and video processing will find something of value in this book, from students to researchers and those in industry.
Download or read book Learning and Execution of Object Manipulation Tasks on Humanoid Robots written by Waechter, Mirko and published by KIT Scientific Publishing. This book was released on 2018-03-21 with total page 258 pages. Available in PDF, EPUB and Kindle. Book excerpt: Equipping robots with complex capabilities still requires a great amount of effort. In this work, a novel approach is proposed to understand, to represent and to execute object manipulation tasks learned from observation by combining methods of data analysis, graphical modeling and artificial intelligence. Employing this approach enables robots to reason about how to solve tasks in dynamic environments and to adapt to unseen situations.
Download or read book Biologically Inspired Efficiencies in Computer Vision and Audition written by Mohammadkazem Ebrahimpour and published by . This book was released on 2020 with total page 236 pages. Available in PDF, EPUB and Kindle. Book excerpt: Computer perception is one of the fundamental problems in artificial intelligence. Given an image or a recorded audio, a human can quickly recognize and detect objects based on image or sound or both. In computer vision, Object Detection is concerned with recognizing objects in images and drawing a bounding box around them. Researchers have been working on developing algorithms to recognize, detect, and segment objects/scenes in images for decades. Numerous challenges make these problems significantly challenging in real-world scenarios, since objects usually appear in different conditions, such as viewpoints, scales, and with background noise, and they even may deform into different shapes, parts, or poses. Real-time object detection has many important applications, such as autonomous driving cars and video surveillance. In this dissertation, we approach visual understanding in the following ways: First, we utilize implicit information in trained neural networks to localize all objects of interest in an image using a sensitivity analysis approach. Second, we introduce a novel framework for object detection called "Ventral- Dorsal" Neural Networks, inspired by the structure of the human brain. Third, we expand the Ventral-Dorsal framework, focusing on attaining real-time performance needed for online applications. Forth, we compare human attention with deep neural network attention algorithms in order to understand whether neural network attention matches human attention. Also, auditory perception is crucial in artificial intelligence systems. Until recently, auditory object recognition pipelines were in need of substantial hand engineering for feature extraction. Engineered features need to be tuned for every individual problem. Also, some popular feature extraction methods are time-consuming, limiting real-time applications. Here we attempt to avoid these problems using end-to-end training. Due to the recent improvements in deep neural networks, we are able to eliminate feature learning by optimizing feature extraction and classification jointly in one network. In this dissertation, we approach the auditory object recognition problem in the following ways: we proposed a novel "end-to-end" deep neural network architecture that takes raw audio as input and maps it to class labels. We also applied our proposed architecture to a new dataset of infant vocalization sounds for further investigation
Download or read book Cognitive Visual Perception Mechanism for Robots Using Object based Visual Attention written by Yuanlong Yu and published by . This book was released on 2010 with total page 488 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book A Reconfigurable Accelerator For Neuromorphic Object Recognition written by Jagdish Sabarad and published by . This book was released on 2016 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: A significant challenge in creating machines with artificial vision is designing systems which can process visual information as efficiently as the human brain. Recent advances in neuroscience have enabled researchers to develop computational models of auditory, visual and learning perceptions in the human brain. Among these models, the two widely accepted algorithms that model the process of attention and recognition in the mammalian visual pathway are - the Saliency based model for visual attention and HMAX model for object recognition. One of the major burdens of these biologically plausible models is their massive computational demands. Real time implemen- tation of these biologically inspired vision algorithms, while challenging, can have a diverse and profound impact in applications like autonomous vehicle navigation, surveillance, robotics and face, text and gesture recognition. To mimic true biological systems, implementations of these algorithms must not only meet real-time performance goals, but also stringent power budgets and small form-factors. Previous attempts to parallelize the HMAX model on multi-core processors have been unable to provide real-time performance due to limited parallelism and high computational complexity. Researchers have leveraged graphics processors due to their ease of programmability and high parallelism. However, their excessive power consumption hinders deployment in embedded or low-power systems. The focus of this work is on the design and architecture of a reconfigurable hardware acceler- ator for the time consuming S2-C2 stage of the HMAX model. The accelerator leverages spatial parallelism, dedicated wide data buses with on-chip memories to provide an energy efficient solution to enable adoption into embedded systems. This work presents a systolic array-based architecture which includes a run-time reconfigurable convolution engine which can perform mul- tiple variable-sized convolutions in parallel. An automation flow is described for this accelerator which can generate optimal hardware configurations for a given algorithmic specification and also perform run-time configuration and execution seamlessly. Experimental results on Virtex-6 FPGA platforms show 5X to 11X speedups and 14X to 33X higher performance-per-Watt over a CNS-based implementation on a Tesla GPU.
Download or read book VOCUS A Visual Attention System for Object Detection and Goal Directed Search written by Simone Frintrop and published by Springer. This book was released on 2009-09-02 with total page 216 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 Object Recognition in Man Monkey and Machine written by Michael J. Tarr and published by MIT Press. This book was released on 1999-03-15 with total page 228 pages. Available in PDF, EPUB and Kindle. Book excerpt: The contributors bring a wide range of methodologies to bear on the common problem of image-based object recognition. These interconnected essays on three-dimensional visual object recognition present cutting-edge research by some of the most creative neuroscientific, cognitive, and computational scientists in the field. Cassandra Moore and Patrick Cavanagh take a classic demonstration, the perception of "two-tone" images, and turn it into a method for understanding the nature of object representations in terms of surfaces and the interaction between bottom-up and top-down processes. Michael J. Tarr and Isabel Gauthier use computer graphics to study whether viewpoint-dependent recognition mechanisms can generalize between exemplars of perceptually defined classes. Melvyn A. Goodale and G. Keith Humphrey use innovative psychophysical techniques to investigate dissociable aspects of visual and spatial processing in brain-injured subjects. D.I. Perrett, M.W. Oram, and E. Ashbridge combine neurophysiological single-cell data from monkeys with computational analyses for a new way of thinking about the mechanisms that mediate viewpoint-dependent object recognition and mental rotation. Shimon Ullman also addresses possible mechanisms to account for viewpoint-dependent behavior, but from the perspective of machine vision. Finally, Philippe G. Schyns synthesizes work from many areas, to provide a coherent account of how stimulus class and recognition task interact. The contributors bring a wide range of methodologies to bear on the common problem of image-based object recognition.
