scholarly journals Robust Local Descriptor for Color Object Recognition

2019 ◽  
Vol 36 (6) ◽  
pp. 471-482
Author(s):  
Rabah Hamdini ◽  
Nacira Diffellah ◽  
Abderrahmane Namane
Keyword(s):  
Object Recognition ◽  
Local Descriptor ◽  
Color Object Recognition

scholarly journals Object recognition using hybrid boosting method

2016 ◽  
Author(s):  
Osama Ashfaq
Keyword(s):  
Object Recognition ◽  
Covariance Matrix ◽  
Local Region ◽  
Computational Time ◽  
Local Descriptor ◽  
Alternative Approach ◽  
Different Types ◽  
Boosting Method ◽  
Mixture Of Gaussian ◽  
Diagonal Covariance Matrix

Li (ICCV, 2005) proposed a novel generative/discriminative way to combine features with different types and use them to learn labels in the images. However, the mixture of Gaussian used in Li’s paper suffers greatly from the curse of dimensionality. Here I propose an alternative approach to generate local region descriptor. I treat GMM with diagonal covariance matrix and PCA as separate features, and combine them as the local descriptor. In this way, we could reduce the computational time for mixture model greatly while score greater 90% accuracies for caltech-4 image sets.

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

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 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

COLOR NORMALIZATION FOR COLOR OBJECT RECOGNITION

1999 ◽  
Vol 13 (08) ◽  
pp. 1271-1285 ◽  
Author(s):  
GRAHAM D. FINLAYSON ◽  
GUI YUN TIAN
Keyword(s):  
Object Recognition ◽  
Color Image ◽  
Color Constancy ◽  
Object Identity ◽  
Physical Processes ◽  
Image Normalization ◽  
Invariant Normalization ◽  
Second Stage ◽  
Color Bias ◽  
Color Object Recognition

Color images depend on the color of the capture illuminant and object reflectance. As such image colors are not stable features for object recognition, however stability is necessary since perceived colors (the colors we see) are illuminant independent and do correlate with object identity. Before the colors in images can be compared, they must first be preprocessed to remove the effect of illumination. Two types of preprocessing have been proposed: first, run a color constancy algorithm or second apply an invariant normalization. In color constancy preprocessing the illuminant color is estimated and then, at a second stage, the image colors are corrected to remove color bias due to illumination. In color invariant normalization image RGBs are redescribed, in an illuminant independent way, relative to the context in which they are seen (e.g. RGBs might be divided by a local RGB average). In theory the color constancy approach is superior since it works in a scene independently: color invariant normalization can be calculated post-color constancy but the converse is not true. However, in practice color invariant normalization usually supports better indexing. In this paper we ask whether color constancy algorithms will ever deliver better indexing than color normalization. The main result of this paper is to demonstrate equivalence between color constancy and color invariant computation. The equivalence is empirically derived based on color object recognition experiments. colorful objects are imaged under several different colors of light. To remove dependency due to illumination these images are preprocessed using either a perfect color constancy algorithm or the comprehensive color image normalization. In the perfect color constancy algorithm the illuminant is measured rather than estimated. The import of this is that the perfect color constancy algorithm can determine the actual illuminant without error and so bounds the performance of all existing and future algorithms. Post-color constancy or color normalization processing, the color content is used as cue for object recognition. Counter-intuitively perfect color constancy does not support perfect recognition. In comparison the color invariant normalization does deliver near-perfect recognition. That the color constancy approach fails implies that the scene effective illuminant is different from the measured illuminant. This explanation has merit since it is well known that color constancy is more difficult in the presence of physical processes such as fluorescence and mutual illumination. Thus, in a second experiment, image colors are corrected based on a scene dependent "effective illuminant". Here, color constancy preprocessing facilitates near-perfect recognition. Of course, if the effective light is scene dependent then optimal color constancy processing is also scene dependent and so, is equally a color invariant normalization.

Sign in / Sign up

Export Citation Format

Share Document