Hierarchical Object Representations in the Visual Cortex and Computer Vision

Over the past 40 years, neurobiology and computational neuroscience has proved that deeper understanding of visual processes in humans and non-human primates can lead to important advancements in computational perception theories and systems. One of the main difficulties that arises when designing a...

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Main Authors: Antonio Rodriguez-Sanchez, Mazyar Fallah, Ales Leonardis
Formato: Online
Idioma:inglês
Publicado em: Frontiers Media SA 2021
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Acesso em linha:20291
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author Antonio Rodriguez-Sanchez
Mazyar Fallah
Ales Leonardis
author_browse Ales Leonardis
Antonio Rodriguez-Sanchez
Mazyar Fallah
author_facet Antonio Rodriguez-Sanchez
Mazyar Fallah
Ales Leonardis
author_sort Antonio Rodriguez-Sanchez
collection Directory of Open Access Books
description Over the past 40 years, neurobiology and computational neuroscience has proved that deeper understanding of visual processes in humans and non-human primates can lead to important advancements in computational perception theories and systems. One of the main difficulties that arises when designing automatic vision systems is developing a mechanism that can recognize - or simply find - an object when faced with all the possible variations that may occur in a natural scene, with the ease of the primate visual system. The area of the brain in primates that is dedicated at analyzing visual information is the visual cortex. The visual cortex performs a wide variety of complex tasks by means of simple operations. These seemingly simple operations are applied to several layers of neurons organized into a hierarchy, the layers representing increasingly complex, abstract intermediate processing stages. In this Research Topic we propose to bring together current efforts in neurophysiology and computer vision in order 1) To understand how the visual cortex encodes an object from a starting point where neurons respond to lines, bars or edges to the representation of an object at the top of the hierarchy that is invariant to illumination, size, location, viewpoint, rotation and robust to occlusions and clutter; and 2) How the design of automatic vision systems benefit from that knowledge to get closer to human accuracy, efficiency and robustness to variations.
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publishDate 2021
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spelling doab-20.500.12854ir-492612024-04-05T12:36:10Z Hierarchical Object Representations in the Visual Cortex and Computer Vision Antonio Rodriguez-Sanchez Mazyar Fallah Ales Leonardis RC321-571 Q1-390 object recognition Neuronal modeling shape Neuromorphic Computational neuroscence Attention Visual Cortex Computer Vision thema EDItEUR::P Mathematics and Science::PS Biology, life sciences::PSA Life sciences: general issues::PSAN Neurosciences Over the past 40 years, neurobiology and computational neuroscience has proved that deeper understanding of visual processes in humans and non-human primates can lead to important advancements in computational perception theories and systems. One of the main difficulties that arises when designing automatic vision systems is developing a mechanism that can recognize - or simply find - an object when faced with all the possible variations that may occur in a natural scene, with the ease of the primate visual system. The area of the brain in primates that is dedicated at analyzing visual information is the visual cortex. The visual cortex performs a wide variety of complex tasks by means of simple operations. These seemingly simple operations are applied to several layers of neurons organized into a hierarchy, the layers representing increasingly complex, abstract intermediate processing stages. In this Research Topic we propose to bring together current efforts in neurophysiology and computer vision in order 1) To understand how the visual cortex encodes an object from a starting point where neurons respond to lines, bars or edges to the representation of an object at the top of the hierarchy that is invariant to illumination, size, location, viewpoint, rotation and robust to occlusions and clutter; and 2) How the design of automatic vision systems benefit from that knowledge to get closer to human accuracy, efficiency and robustness to variations. 2021-02-11T15:15:24Z 2021-02-11T15:15:24Z 2017-02-03 17:04:57 2016 book 20291 16648714 9782889197989 https://directory.doabooks.org/handle/20.500.12854/49261 eng Frontiers Research Topics image/jpeg Attribution 4.0 International http://www.frontiersin.org/books/Hierarchical_Object_Representations_in_the_Visual_Cortex_and_Computer_Vision/842#nogo http://journal.frontiersin.org/researchtopic/2258/hierarchical-object-representations-in-the-visual-cortex-and-computer-vision Frontiers Media SA 10.3389/978-2-88919-798-9 10.3389/978-2-88919-798-9 bf5ce210-e72e-4860-ba9b-c305640ff3ae 9782889197989 290 open access
spellingShingle RC321-571
Q1-390
object recognition
Neuronal modeling
shape
Neuromorphic
Computational neuroscence
Attention
Visual Cortex
Computer Vision
thema EDItEUR::P Mathematics and Science::PS Biology, life sciences::PSA Life sciences: general issues::PSAN Neurosciences
Antonio Rodriguez-Sanchez
Mazyar Fallah
Ales Leonardis
Hierarchical Object Representations in the Visual Cortex and Computer Vision
title Hierarchical Object Representations in the Visual Cortex and Computer Vision
title_full Hierarchical Object Representations in the Visual Cortex and Computer Vision
title_fullStr Hierarchical Object Representations in the Visual Cortex and Computer Vision
title_full_unstemmed Hierarchical Object Representations in the Visual Cortex and Computer Vision
title_short Hierarchical Object Representations in the Visual Cortex and Computer Vision
title_sort hierarchical object representations in the visual cortex and computer vision
topic RC321-571
Q1-390
object recognition
Neuronal modeling
shape
Neuromorphic
Computational neuroscence
Attention
Visual Cortex
Computer Vision
thema EDItEUR::P Mathematics and Science::PS Biology, life sciences::PSA Life sciences: general issues::PSAN Neurosciences
topic_facet RC321-571
Q1-390
object recognition
Neuronal modeling
shape
Neuromorphic
Computational neuroscence
Attention
Visual Cortex
Computer Vision
thema EDItEUR::P Mathematics and Science::PS Biology, life sciences::PSA Life sciences: general issues::PSAN Neurosciences
url 20291
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