Fast Object Recognition By Selectively Examining Hypotheses
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Object Detection with Deep Learning Models
Author | : S Poonkuntran |
Publisher | : CRC Press |
Total Pages | : 276 |
Release | : 2022-11-01 |
Genre | : Computers |
ISBN | : 1000686744 |
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
Bulletin of the Atomic Scientists
Author | : |
Publisher | : |
Total Pages | : 88 |
Release | : 1961-05 |
Genre | : |
ISBN | : |
The Bulletin of the Atomic Scientists is the premier public resource on scientific and technological developments that impact global security. Founded by Manhattan Project Scientists, the Bulletin's iconic "Doomsday Clock" stimulates solutions for a safer world.
How Humans Recognize Objects: Segmentation, Categorization and Individual Identification
Author | : Chris Fields |
Publisher | : Frontiers Media SA |
Total Pages | : 267 |
Release | : 2016-08-18 |
Genre | : Psychology |
ISBN | : 2889199401 |
Human beings experience a world of objects: bounded entities that occupy space and persist through time. Our actions are directed toward objects, and our language describes objects. We categorize objects into kinds that have different typical properties and behaviors. We regard some kinds of objects – each other, for example – as animate agents capable of independent experience and action, while we regard other kinds of objects as inert. We re-identify objects, immediately and without conscious deliberation, after days or even years of non-observation, and often following changes in the features, locations, or contexts of the objects being re-identified. Comparative, developmental and adult observations using a variety of approaches and methods have yielded a detailed understanding of object detection and recognition by the visual system and an advancing understanding of haptic and auditory information processing. Many fundamental questions, however, remain unanswered. What, for example, physically constitutes an “object”? How do specific, classically-characterizable object boundaries emerge from the physical dynamics described by quantum theory, and can this emergence process be described independently of any assumptions regarding the perceptual capabilities of observers? How are visual motion and feature information combined to create object information? How are the object trajectories that indicate persistence to human observers implemented, and how are these trajectory representations bound to feature representations? How, for example, are point-light walkers recognized as single objects? How are conflicts between trajectory-driven and feature-driven identifications of objects resolved, for example in multiple-object tracking situations? Are there separate “what” and “where” processing streams for haptic and auditory perception? Are there haptic and/or auditory equivalents of the visual object file? Are there equivalents of the visual object token? How are object-identification conflicts between different perceptual systems resolved? Is the common assumption that “persistent object” is a fundamental innate category justified? How does the ability to identify and categorize objects relate to the ability to name and describe them using language? How are features that an individual object had in the past but does not have currently represented? How are categorical constraints on how objects move or act represented, and how do such constraints influence categorization and the re-identification of individuals? How do human beings re-identify objects, including each other, as persistent individuals across changes in location, context and features, even after gaps in observation lasting months or years? How do human capabilities for object categorization and re-identification over time relate to those of other species, and how do human infants develop these capabilities? What can modeling approaches such as cognitive robotics tell us about the answers to these questions? Primary research reports, reviews, and hypothesis and theory papers addressing questions relevant to the understanding of perceptual object segmentation, categorization and individual identification at any scale and from any experimental or modeling perspective are solicited for this Research Topic. Papers that review particular sets of issues from multiple disciplinary perspectives or that advance integrative hypotheses or models that take data from multiple experimental approaches into account are especially encouraged.
The Visual Neurosciences
Author | : John Simon Werner |
Publisher | : MIT Press |
Total Pages | : 975 |
Release | : 2004 |
Genre | : Cell physiology |
ISBN | : 0262033089 |
An essential reference book for visual science.
Visual Object Recognition
Author | : Kristen Grauman |
Publisher | : Morgan & Claypool Publishers |
Total Pages | : 184 |
Release | : 2011 |
Genre | : Computers |
ISBN | : 1598299689 |
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
The Brain: A Systems Neuroscience Perspective
Author | : Vikas Rai |
Publisher | : Bentham Science Publishers |
Total Pages | : 119 |
Release | : 2024-10-10 |
Genre | : Science |
ISBN | : 9815256998 |
The Brain: A Systems Neuroscience Perspective is a comprehensive textbook designed for undergraduate students in neuroscience. It offers a detailed exploration of brain dynamics, spatial navigation, and the neuroscience of Alzheimer's disease, with an emphasis on understanding complex concepts through simplified mathematical models. The objective is to provide a solid foundation for readers in systems neuroscience. Key Topics Fundamental Brain Dynamics: Covers the basics of brain organization, neural systems, and the role of differential equations in neuroscience (Chapters 1-3). Spatial Navigation: Discusses the neural mechanisms underlying spatial navigation and the geometry of neural maps (Chapter 4). Alzheimer’s Disease: Presents a simplified mathematical theory of Alzheimer’s dementia, exploring its onset, progression, and potential interventions (Chapter 5). Key Features Accessible Approach: Minimizes mathematical complexity to make the subject approachable for readers with a basic understanding of differential equations. Standalone Resource: Provides all essential knowledge on brain function, making it a valuable tool for both coursework and self-study. Includes references for advanced readers.
Neurobiology of Attention
Author | : Laurent Itti |
Publisher | : Elsevier |
Total Pages | : 757 |
Release | : 2005-03-31 |
Genre | : Psychology |
ISBN | : 0080454313 |
A key property of neural processing in higher mammals is the ability to focus resources by selectively directing attention to relevant perceptions, thoughts or actions. Research into attention has grown rapidly over the past two decades, as new techniques have become available to study higher brain function in humans, non-human primates, and other mammals. Neurobiology of Attention is the first encyclopedic volume to summarize the latest developments in attention research.An authoritative collection of over 100 chapters organized into thematic sections provides both broad coverage and access to focused, up-to-date research findings. This book presents a state-of-the-art multidisciplinary perspective on psychological, physiological and computational approaches to understanding the neurobiology of attention. Ideal for students, as a reference handbook or for rapid browsing, the book has a wide appeal to anybody interested in attention research.* Contains numerous quick-reference articles covering the breadth of investigation into the subject of attention* Provides extensive introductory commentary to orient and guide the reader* Includes the most recent research results in this field of study