A biologically inspired spatio-chromatic feature for color object recognition

2017 ◽  
Vol 76 (18) ◽  
pp. 18731-18747 ◽  
Author(s):  
Tian Tian ◽  
Yun Zhang ◽  
Kim-Kwang Raymond Choo ◽  
Weijing Song
Keyword(s):  
Object Recognition ◽  
Biologically Inspired ◽  
Color Object Recognition

scholarly journals Visual Behavior Based Bio-Inspired Polarization Techniques in Computer Vision and Robotics

2012 ◽  
pp. 243-272 ◽  
Author(s):  
Abd El Rahman Shabayek ◽  
Olivier Morel ◽  
David Fofi
Keyword(s):  
Computer Vision ◽  
Object Recognition ◽  
Visual Perception ◽  
Polarization Vision ◽  
Visual Behavior ◽  
Biologically Inspired ◽  
Long Time ◽  
Comprehensive Survey ◽  
High Level ◽  
And Robotics

For long time, it was thought that the sensing of polarization by animals is invariably related to their behavior, such as navigation and orientation. Recently, it was found that polarization can be part of a high-level visual perception, permitting a wide area of vision applications. Polarization vision can be used for most tasks of color vision including object recognition, contrast enhancement, camouflage breaking, and signal detection and discrimination. The polarization based visual behavior found in the animal kingdom is briefly covered. Then, the authors go in depth with the bio-inspired applications based on polarization in computer vision and robotics. The aim is to have a comprehensive survey highlighting the key principles of polarization based techniques and how they are biologically inspired.

Machine Learning Applications in Computer Vision

2013 ◽  
pp. 896-926
Author(s):  
Mehrtash Harandi ◽  
Javid Taheri ◽  
Brian C. Lovell
Keyword(s):  
Machine Learning ◽  
Computer Vision ◽  
Object Recognition ◽  
Visual Recognition ◽  
Sequential Decision ◽  
Biologically Inspired ◽  
Transductive Learning ◽  
Machine Learning Applications ◽  
Appearance Models ◽  
Learning Frameworks

Recognizing objects based on their appearance (visual recognition) is one of the most significant abilities of many living creatures. In this study, recent advances in the area of automated object recognition are reviewed; the authors specifically look into several learning frameworks to discuss how they can be utilized in solving object recognition paradigms. This includes reinforcement learning, a biologically-inspired machine learning technique to solve sequential decision problems and transductive learning, and a framework where the learner observes query data and potentially exploits its structure for classification. The authors also discuss local and global appearance models for object recognition, as well as how similarities between objects can be learnt and evaluated.

scholarly journals Number detectors spontaneously emerge in a deep neural network designed for visual object recognition

2019 ◽  
Vol 5 (5) ◽  
pp. eaav7903 ◽  
Author(s):  
Khaled Nasr ◽  
Pooja Viswanathan ◽  
Andreas Nieder
Keyword(s):  
Neural Network ◽  
Object Recognition ◽  
Visual System ◽  
Deep Neural Network ◽  
Number Sense ◽  
Discrimination Performance ◽  
Visual Object ◽  
Visual Object Recognition ◽  
Biologically Inspired ◽  
Number Discrimination

Humans and animals have a “number sense,” an innate capability to intuitively assess the number of visual items in a set, its numerosity. This capability implies that mechanisms to extract numerosity indwell the brain’s visual system, which is primarily concerned with visual object recognition. Here, we show that network units tuned to abstract numerosity, and therefore reminiscent of real number neurons, spontaneously emerge in a biologically inspired deep neural network that was merely trained on visual object recognition. These numerosity-tuned units underlay the network’s number discrimination performance that showed all the characteristics of human and animal number discriminations as predicted by the Weber-Fechner law. These findings explain the spontaneous emergence of the number sense based on mechanisms inherent to the visual system.

Dual nonlinear correlation applied to textured and color object recognition

1998 ◽  
Author(s):  
Elisabet Perez ◽  
Maria S. Millan Garcia-Verela ◽  
Katarzyna Chalasinska-Macukow
Keyword(s):  
Object Recognition ◽  
Nonlinear Correlation ◽  
Color Object Recognition

scholarly journals How Can Selection of Biologically Inspired Features Improve the Performance of a Robust Object Recognition Model?

PLoS ONE ◽  
2012 ◽  
Vol 7 (2) ◽  
pp. e32357 ◽  
Author(s):  
Masoud Ghodrati ◽  
Seyed-Mahdi Khaligh-Razavi ◽  
Reza Ebrahimpour ◽  
Karim Rajaei ◽  
Mohammad Pooyan
Keyword(s):  
Object Recognition ◽  
Biologically Inspired ◽  
Recognition Model ◽  
Biologically Inspired Features ◽  
Selection Of

scholarly journals Dynamic Color Object Recognition Using Fuzzy Logic

2004 ◽  
Vol 8 (1) ◽  
pp. 29-38 ◽  
Author(s):  
Napoleon H. Reyes ◽  
◽  
Elmer P. Dadios ◽  
Keyword(s):  
Object Recognition ◽  
Fuzzy Inference ◽  
Color Constancy ◽  
Human Perception ◽  
Light Sources ◽  
Inference System ◽  
Image Appearance ◽  
Color Descriptors ◽  
New Perspective ◽  
Color Object Recognition

This paper presents a novel Logit-Logistic Fuzzy Color Constancy (LLFCC) algorithm and its variants for dynamic color object recognition. Contrary to existing color constancy algorithms, the proposed scheme focuses on manipulating a color locus depicting the colors of an object, and not stabilizing the whole image appearance per se. In this paper, a new set of adaptive contrast manipulation operators is introduced and utilized in conjunction with a fuzzy inference system. Moreover, a new perspective in extracting color descriptors of an object from the rg-chromaticity space is presented. Such color descriptors allow for the reduction of the effects of brightness/darkness and at the same time adhere to human perception of colors. The proposed scheme tremendously cuts processing time by simultaneously compensating for the effects of a multitude of factors that plague the scene of traversal, eliminating the need for image pre-processing steps. Experiment results attest to its robustness in scenes with multiple white light sources, spatially varying illumination intensities, varying object position, and presence of highlights.

Quaternion Harmonic moments and extreme learning machine for color object recognition

2019 ◽  
Vol 78 (15) ◽  
pp. 20935-20959 ◽  
Author(s):  
Nisrine Dad ◽  
Noureddine En-nahnahi ◽  
Said El Alaoui Ouatik
Keyword(s):  
Object Recognition ◽  
Extreme Learning Machine ◽  
Learning Machine ◽  
Color Object Recognition
Sign in / Sign up

Export Citation Format

Share Document