View-based dynamic object recognition based on human perception

2003 ◽  
Author(s):  
H.H. Bulthoff ◽  
C. Wallraven ◽  
A. Graf
Keyword(s):  
Object Recognition ◽  
Human Perception ◽  
Dynamic Object

scholarly journals Facilitation by view combination and coherent motion in dynamic object recognition

2010 ◽  
Vol 50 (2) ◽  
pp. 202-210 ◽  
Author(s):  
Alinda Friedman ◽  
Quoc C. Vuong ◽  
Marcia Spetch
Keyword(s):  
Object Recognition ◽  
Coherent Motion ◽  
Dynamic Object

scholarly journals A 320 mW 342 GOPS Real-Time Dynamic Object Recognition Processor for HD 720p Video Streams

2013 ◽  
Vol 48 (1) ◽  
pp. 33-45 ◽  
Author(s):  
Jinwook Oh ◽  
Gyeonghoon Kim ◽  
Junyoung Park ◽  
Injoon Hong ◽  
Seungjin Lee ◽  
...  
Keyword(s):  
Object Recognition ◽  
Real Time ◽  
Dynamic Object ◽  
Video Streams ◽  
Time Dynamic

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.

scholarly journals Dynamic object recognition in pigeons and humans

2006 ◽  
Vol 34 (3) ◽  
pp. 215-228 ◽  
Author(s):  
Marcia L. Spetch ◽  
Alinda Friedman ◽  
Quoc C. Vuong
Keyword(s):  
Object Recognition ◽  
Dynamic Object

scholarly journals Qualitative similarities and differences in visual object representations between brains and deep networks

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Georgin Jacob ◽  
R. T. Pramod ◽  
Harish Katti ◽  
S. P. Arun
Keyword(s):  
Neural Networks ◽  
Object Recognition ◽  
Deep Neural Networks ◽  
Relative Size ◽  
Sufficient Conditions ◽  
Human Perception ◽  
Visual Object ◽  
Object Representations ◽  
Deep Networks ◽  
Global Advantage

AbstractDeep neural networks have revolutionized computer vision, and their object representations across layers match coarsely with visual cortical areas in the brain. However, whether these representations exhibit qualitative patterns seen in human perception or brain representations remains unresolved. Here, we recast well-known perceptual and neural phenomena in terms of distance comparisons, and ask whether they are present in feedforward deep neural networks trained for object recognition. Some phenomena were present in randomly initialized networks, such as the global advantage effect, sparseness, and relative size. Many others were present after object recognition training, such as the Thatcher effect, mirror confusion, Weber’s law, relative size, multiple object normalization and correlated sparseness. Yet other phenomena were absent in trained networks, such as 3D shape processing, surface invariance, occlusion, natural parts and the global advantage. These findings indicate sufficient conditions for the emergence of these phenomena in brains and deep networks, and offer clues to the properties that could be incorporated to improve deep networks.

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