Fuzzy Color Space Research Articles

An image-based approach for building fuzzy color spaces

Color is one of the most used features for image analysis. However, two uncertainty problems arise in this scope: first, color computer representation does not match with how humans understand the color concept; second, the color feature is imprecise by nature. For this reason, fuzzy colors and fuzzy color spaces were developed as a suitable way to model color categories. The most recent and best accurate approaches use crisp color prototypes to build fuzzy color spaces, considering standard and fixed set of colors prototypes (such as ISCC-NBS system) for this purpose. The use of these types of general purpose prototypes may not be flexible enough to collect particular colors of a given image (or a set of images); in addition, image colors may be context dependent in some cases (for example, red color in winery or medicine context). In order to solve these drawbacks, in this paper we propose to learn fuzzy color spaces by analyzing the relevant colors in a given set of representative images for a specific context and/or application; then, we will use them as prototypes in order to build adaptive fuzzy color spaces. Some real experiments are performed in order to illustrate the advantages of our proposal.

Color comparison in fuzzy color spaces

In this paper we study the problem of color comparison in computers in the setting of fuzzy color spaces. First, we study resemblance relations between precise colors induced by fuzzy colors and fuzzy color spaces. Such resemblance relations are equivalent to fuzzy categorizations of color spaces, that are crucial in order to capture the human's perception of color. Second, we consider the case of color information expressed by means of fuzzy colors used either in a conjunctive or disjunctive way. In order to match pieces of color information we use concepts of resemblance, inclusion, compatibility, and possibility/necessity between the fuzzy colors involved in the definition of different pieces of color information, using well known results from the fuzzy set and possibility theories. Finally, we define inclusion/similarity indexes for comparison of fuzzy colors, including a novel approach to calculate inclusion and overlapping between fuzzy colors on the basis of quantification techniques.

Fuzzy Color Spaces: A Conceptual Approach to Color Vision

In this paper, we introduce formal definitions of the concepts of fuzzy color and fuzzy color space. First, we formalize the notion of fuzzy color for representing the correspondence between computational representation of colors and perceptual color categories identified by a color name. Second, we propose a methodology for learning fuzzy colors based on the paradigm of conceptual spaces, where prototypes are used for each category to be learnt. Since the conceptual space approach yields crisp categorizations, we introduce a novel methodology for defining fuzzy boundaries of color categories on the basis of a Voronoi tessellation of a color space. Finally, we also formalize the notion of fuzzy color space as the collection of fuzzy colors corresponding to the color categories employed in a certain context/application and/or for a specific user. Different typologies of fuzzy color spaces are proposed in order to be consistent with the nature of the categories we want to model. Our approach is illustrated by defining fuzzy color spaces using RGB with the Euclidean distance. Examples based on the well-known ISCC-NBS color naming system are presented, as well as others based on collections of color names and prototypes provided by users. The proposal is evaluated and compared with the most used approaches for color modeling. Additionally, a website located at http://www.jfcssoftware.com including all experimentation data, software implementing our models, and additional materials is available to researchers in color modeling.

Moving Object Tracking and Its Application to an Indoor Dual-Robot Patrol

This paper presents an application of image tracking using an omnidirectional wheeled mobile robot (WMR). The objective of this study is to integrate image processing of hue, saturation, and lightness (HSL) for fuzzy color space, and use mean shift tracking for object detection and a Radio Frequency Identification (RFID) reader for confirming destination. Fuzzy control is applied to omnidirectional WMR for indoor patrol and intruder detection. Experimental results show that the proposed control scheme can make the WMRs perform indoor security service.

JFCS: A Color Modeling Java Software Based on Fuzzy Color Spaces

This paper introduces JFCS (Java Fuzzy Color Space), an open source software for modeling colors on the basis of fuzzy color spaces, which is able to fill the semantic gap between the color representation in computers and the subjective human perception. Fuzzy colors allow introducing semantics in the description of color by using linguistic labels, taking into account the fuzzy boundaries between the representation of color terms. The methodology for building fuzzy color spaces implemented in JFCS was proposed by the authors in previous works, it is based on the paradigm of conceptual spaces including fuzzy boundaries and considering a collection of color names and corresponding crisp color representatives. In this sense, the necessary information for modeling fuzzy color spaces can be easily obtained from non-expert users, since the only information required is a representative crisp color for each linguistic color term to be modeled. The software is implemented in Java and it includes several graphical tools for building different types of spaces with different characteristics in an easy way. These are needed to be consistent with the different nature of the colors a user wants to model. Membership degrees of pixels in images to each fuzzy color in a certain fuzzy color space can be obtained. Tools for 3D visualization of fuzzy color spaces, as well as for describing colors and mapping pixels in images in term of linguistic labels, are also included in JFCS.

A discussion on fuzzy cardinality and quantification. Some applications in image processing

In this paper we discuss on some different representations of the cardinality of a fuzzy set and their use in fuzzy quantification. We have considered the widely employed sigma-count, fuzzy numbers, and gradual numbers. Gradual numbers assign numbers to values of a relevance scale, typically [0,1]. Contrary to sigma-count and fuzzy numbers, they provide a precise representation of the cardinality of a fuzzy set. We illustrate our claims by calculating the cardinality of the fuzzy set of pixels that match a certain fuzzy color in an image. For that purpose we consider fuzzy color spaces previously defined by the authors, consisting of a collection of fuzzy sets providing a suitable, conceptual quantization with soft boundaries of crisp color spaces. Finally, we show the suitability of our approaches to fuzzy quantification for different applications in image processing. First, the calculation of histograms. Second, the definition of the notion of dominant fuzzy color, and the calculation of the degree to which we can say that a certain color is dominant in an image.