Vehicle Color Recognition Using Fuzzy Rules and Center of Maximum Defuzzification

Defuzzification and chaining of rules in hierarchical fuzzy systems

Vehicle Color Recognition using Convolutional Neural Network

Vehicle color information is one of the important elements in ITS (Intelligent Traffic System). In this paper, we present a vehicle color recognition method using convolutional neural network (CNN). Naturally, CNN is designed to learn classification method based on shape information, but we proved that CNN can also learn classification based on color distribution. In our method, we convert the ...

Object Recognition using Fuzzy Membership Rules

In 2004, we began working to create an intelligent vision system. To define the system as intelligent were striving for 5 main characteristics. In this update report, we will describe our research through 2005. 1. The system should learn broad categories of objects by learning from experience. By broad categories, we mean general classes of objects that may naturally be encountered in an enviro...

Defuzzification of intuitionistic fuzzy sets

Defuzzification is the process of converting a fuzzy quantity to precise quantity, just as fuzzification is the conversion of a precise quantity to a fuzzy quantity. Various types of defuzzification methods, are available for conversion of fuzzy to non-fuzzy. In this paper, defuzzification functions in intuitionistic fuzzy environment such as triangular, trapezoidal, L-trapezoidal, R-trapezoida...

Dynamic Color Object Recognition Using Fuzzy Logic

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 ope...