Editorial: Hierarchical Object Representations in the Visual Cortex and Computer Vision

Antonio J Rodríguez-Sánchez,Aleš Leonardis,Mazyar Fallah

doi:10.3389/fncom.2015.00142

Abstract

Over the past 40 years, Neurobiology and Computational Neuroscience have 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, and with the ease of the primate visual system. The area of the brain in primates that is dedicated to analyzing visual information is the visual cortex. The visual cortex performs a wide variety of complex tasks by means of seemingly simple operations. These operations are applied to several layers of neurons organized into a hierarchy, the layers representing increasingly complex, abstract intermediate processing stages.

Highlights

Over the past 40 years, Neurobiology and Computational Neuroscience have 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 find—an object when faced with all the possible variations that may occur in a natural scene, and with the ease of the primate visual system
In this research topic we propose to bring together current efforts in Neurophysiology and Computer Vision in order to better understand (1) 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 benefits from that knowledge to get closer to human accuracy, efficiency and robustness to variations

Summary

Introduction

Over the past 40 years, Neurobiology and Computational Neuroscience have proved that deeper understanding of visual processes in humans and non-human primates can lead to important advancements in computational perception theories and systems. In this research topic we propose to bring together current efforts in Neurophysiology and Computer Vision in order to better understand (1) 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 benefits from that knowledge to get closer to human accuracy, efficiency and robustness to variations.

Results

Conclusion