Dynamic Color Object Recognition Using Fuzzy Logic

Author:

Reyes Napoleon H., ,Dadios Elmer P.,

Abstract

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.

Publisher

Fuji Technology Press Ltd.

Subject

Artificial Intelligence,Computer Vision and Pattern Recognition,Human-Computer Interaction

Cited by 15 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Object Detection Its Progress and Principles;2023 12th International Conference on System Modeling & Advancement in Research Trends (SMART);2023-12-22

2. Machine Learning Based Performance Analysis of Video Object Detection and Classification Using Modified Yolov3 and Mobilenet Algorithm;Journal of Machine and Computing;2023-07-05

3. Approximation of CIEDE2000 color closeness function using Neuro-Fuzzy networks;Applied Intelligence;2021-04-09

4. FHSI: Toward More Human-Consistent Color Representation;Journal of Advanced Computational Intelligence and Intelligent Informatics;2016-05-19

5. Multi-Behaviour Robot Control using Genetic Network Programming with Fuzzy Reinforcement Learning;Advances in Intelligent Systems and Computing;2015

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