Fuzzy Color Research Articles

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.

Identifying Rhodamine Dye Plume Sources in Near-Shore Oceanic Environments by Integration of Chemical and Visual Sensors

This article presents a strategy for identifying the source location of a chemical plume in near-shore oceanic environments where the plume is developed under the influence of turbulence, tides and waves. This strategy includes two modules: source declaration (or identification) and source verification embedded in a subsumption architecture. Algorithms for source identification are derived from the moth-inspired plume tracing strategies based on a chemical sensor. The in-water test missions, conducted in November 2002 at San Clemente Island (California, USA) in June 2003 in Duck (North Carolina, USA) and in October 2010 at Dalian Bay (China), successfully identified the source locations after autonomous underwater vehicles tracked the rhodamine dye plumes with a significant meander over 100 meters. The objective of the verification module is to verify the declared plume source using a visual sensor. Because images taken in near shore oceanic environments are very vague and colors in the images are not well-defined, we adopt a fuzzy color extractor to segment the color components and recognize the chemical plume and its source by measuring color similarity. The source verification module is tested by images taken during the CPT missions.

Robust color image retrieval using visual interest point feature of significant bit-planes

In this paper, we propose a robust color image retrieval method using visual interest point feature of significant bit-planes. We firstly extract the visually significant bit-plane image from the original color image according to the bit-plane theory and noise attack characteristic. And then, we extract the visual interest points from the original color image by using the significant bit-plane image and multi-scale Harris–Laplace detector, and construct the fuzzy color histogram of visual interest points. We finally compute the similarity between color images by using the fuzzy color histogram of visual interest points. Experiments on large databases show that the proposed algorithm is significantly more effective than the state-of-the-art approaches. Especially, it can retrieve the noise-attacked (including blurring, sharpening, and illumination, etc.) image effectively. ► To extract the visual interest points from the original color image by using the significant bit-plane image and multi-scale Harris–Laplace detector. ► To construct the fuzzy color histogram of visual interest points. ► To compute the similarity between color images by using the fuzzy color histogram of visual interest points.

Evaluation of software architecture using fuzzy color Petri net

Evaluation of software architecture using fuzzy color Petri net

Visual multi-touch air interface for barehanded users by skeleton models of hand regions

In this paper, we propose a visual multi-touch air interface. The proposed air interface is based on the image processing and does not require any additional equipment except a built-in camera. The implemented device provides a barehanded interface which copes with the multi-touch operation. The proposed device is easy to apply to the real-time systems because of its low computational load and is cheaper than the existing methods using glove data or 3-dimensional data because any additional equipment is not required. To improve the robustness of the extraction of hand regions under various circumstances, we propose an image processing algorithm based on the HCbCr color model, the fuzzy color filter, and the labeling. In addition, to improve the accuracy of the recognition of hand gestures, we propose a motion recognition algorithm based on the geometric feature points, the skeleton model, and the Kalman filter. Finally, the experiments show that the proposed device is applicable to remote controllers for video games, smart TVs and any computer applications.

An Intelligent mutli-object retrieval system for historical mosaics

In this work we present a Mosaics Intelligent Retrieval System (MIRS) for digital museums. The objective of this work is to attain a semantic interpretation of images of historical mosaics. We use the fuzzy logic techniques and semantic similarity measure to extract knowledge from the images for multi-object indexing. The extracted knowledge provides the users (experts and laypersons) with an intuitive way to describe and to query the images in the database. Our contribution in this paper is firstly, to define semantic fuzzy linguistic terms to encode the object position and the inter-objects spatial relationships in the mosaic image. Secondly, to present a fuzzy color quantization approach using the human perceptual HSV color space and finally, to classify semantically the mosaics images using a semantic similarity measure. The automatically extracted knowledge are collected and traduced into XML language to create mosaics metadata. This system uses a simple Graphic User Interface (GUI) in natural language and applies the classification approach both on the mosaics images database and on user queries, to limit images classes in the retrieval process. MIRS is tested on images from the exceptional Tunisian collection of complex mosaics. Experimental results are based on queries of various complexities which yielded a system’s recall and precision rates of 86.6% and 87.1%, respectively, while the classification approach gives an average success rate evaluated to 76%.

Vision system for tracking handball players using fuzzy color processing

The sports community needs technological aid to extract accurate statistics and performance data from both practice sessions and games. To obtain such information, players must be tracked over time and their movements processed so that individual actions and team plays are simultaneously analyzed. In order to perform this analysis in an automated, formal and accurate way, the authors developed a cost conscientious processing system fed by two overhead cameras (roughly one video stream for each half-field). Players are detected by vest colors, and Fuzzy Logic is used to allow for a given color to be shared by different teams. Color models for the background and the teams are dynamic over time to make up for changes in natural lighting conditions and consequent color changes. Player tracking is further enhanced using Kalman Filtering. Some examples of the analysis, made possible by the proposed system, are shown. Results are based on videos collected during the Portuguese Handball SuperCup competition for the year 2011.

Person re-identification by fuzzy space color histogram

In this paper we propose a new feature for person re-identification with or without the full gallery of persons: the Fuzzy Space Color Histogram. This feature contains both the space and color info...

Object Tracking based on Fuzzy Color Blobs

One of the mayor goals in computer vision is object representation. Object representation aims to determine a set of features that best represents a specific object in an image, for example interest points, edges, color and texture. However, objects are generally composed of several regions containing different information which is more or less convenient to be represented by one of these features. Furthermore, each of these regions could be static or moving with respect to each other. In this sense, this paper presents an object representation based on fuzzy color blobs and spatial relationships among them. This approach of object representation is used to track rigid and articulated objects.

Background Subtraction for Dynamic Texture Scenes Using Fuzzy Color Histograms

Background subtraction is a very popular approach for detecting moving objects from a still scene. For this, most of previous methods depend on the assumption that the background is static over short time periods. However, structured motion patterns of the background (e.g., waving leaves, spouting fountain, rippling water, etc.), which are distinctive from variations due to noise, are hardly tolerated in this assumption and thus still lead to high-level false positive rates when using previous models. In this letter, we introduce a novel background subtraction algorithm for temporally dynamic texture scenes. Specifically, we propose to adopt a clustering-based feature, called fuzzy color histogram (FCH), which has an ability of greatly attenuating color variations generated by background motions while still highlighting moving objects. Experimental results demonstrate that the proposed method is effective for background subtraction in dynamic texture scenes compared to several competitive methods proposed in the literature.

Design Patent Image Retrieval Based on Shape and Color Features

Based on the concrete characteristics and application background of design patent, according to the deficiency of the present retrieval system of design patent database, this paper proposed a synthesis design patent image retrieval method based on shape and color features. First, we present a shape retrieval approach based on the morphological description moment invariants, which can not only reflects the morphological characteristics of design patent image, but also has translation, rotation and scale invariability. Second, an improved color retrieval method based on the key local fuzzy color histogram which contains the colors’ statistic and local distributing characteristics is used to improve the color retrieval accuracy. At last, set a weight coefficient with the consideration of the similarity degree of the shape and color features. The experimental results indicated that the proposed method can retrieve similar design patent images fast and efficiently, and improve the recall ratio and precision of design patent image retrieval.

Design of Feature Extraction in Content Based Image Retrieval (CBIR) using Color and Texture

Retrieval of images based on visual features such as color, texture and shape have proven to have its own set of limitations under different conditions. Various techniques have been implemented using these features like fuzzy color histogram, Tammura texture etc. In this paper we propose a novel method with highly accurate and retrieval efficient approach which will work on large image database with varied contents and background.

Intuitionistic fuzzy color clustering of human cell images on different color models

This paper provides a color cell image clustering algorithm using intuitionistic fuzzy set theory using different color models. The clustering algorithm clusters the blood cells very clearly that helps in detecting various types of human diseases. Clustering of medical images is a challenging task as medical images are vague in nature due to poor illumination. So the boundaries or regions are not clear. Clustering using fuzzy set theory is very robust but still there is some uncertainty present while defining the membership function in fuzzy set theory. This uncertainty is due the lack of knowledge or personal error while defining the membership function. Intuitionistic fuzzy set takes into account this uncertainty and thus it may be useful in medical or real time image processing. The two uncertainty parameters in intuitionistic fuzzy set thus help in converging the cluster center to a desirable location than the cluster centers obtained by fuzzy C means algorithm. Different color models e.g., RGB, HSV, and CIELab are used in this algorithm and it is found that RGB and CIELab give almost similar result. The algorithm is also tested on conventional fuzzy C means algorithm to show the efficacy of the new algorithm.

A combined GIS and stereo vision approach to identify building pixels in images and determine appropriate color terms

Color information is a useful attribute to include in a building's description to assist the listener in identifying the intended target. Often this information is only avail- able as image data, and not readily accessible for use in constructing referring expressions for verbal communication. The method presented uses a GIS building polygon layer in conjunction with street-level captured imagery to provide a method to automatically filter foreground objects and select pixels which correspond to building facades. These selected pixels are then used to define the most appropriate color term for the building, and cor- responding fuzzy color term histogram. The technique uses a single camera capturing images at a high frame rate, with the baseline distance between frames calculated from a GPS speed log. The expected distance from the camera to the building is measured from the polygon layer and refined from the calculated depth map, after which building pixels are selected. In addition significant foreground planar surfaces between the known road edge and building facade are identified as possible boundary walls and hedges. The output is a dataset of the most appropriate color terms for both the building and boundary walls. Initial trials demonstrate the usefulness of the technique in automatically capturing color terms for buildings in urban regions.

Fuzzy Set Theoretical Approach to the Tone Triangular System

The present study considers a fuzzy color system in which three input fuzzy sets are constructed on the tone triangle. This system can process a fuzzy input to a tone triangular system and output to a color on the RGB triangular system. Three input fuzzy sets (not black, white, and light) are applied to the tone triangle relationship. By treating three attributes of chromaticness, whiteness, and blackness on the tone triangle, a target color can be easily obtained as the center of gravity of the output fuzzy set. In the present paper, the differences between fuzzy inputs and inference outputs are described, and the relationship between inference outputs for crisp inputs and for fuzzy inputs on the RGB triangular system are shown by the input-output characteristics between chromaticness, whiteness, and blackness as the inputs and redness (as one of the outputs).

Image-to-MIDI mapping based on dynamic fuzzy color segmentation for visually impaired people

In this paper, the RGB ratio is defined according to a reference color so that an image can be transformed from a conventional color space to the RGB ratio space. Different to traditional distance measurement, a road color model is determined by an ellipse area in the RGB ratio space enclosed by the estimated boundaries. The proposed dynamic fuzzy logic, where fuzzy membership functions are defined according to estimated boundaries, is introduced to implement clustering rules, such that each pixel will have its own fuzzy membership function corresponding to its intensity. A basic neural network is trained and used to achieve parameter optimization. Experimental results for road detection demonstrate the robustness of the proposed approach to variations in intensity. To provide obstacle information, especially for visually impaired people, Musical Instrument Digital Interface (MIDI) is introduced as the sound generator, and image-to-MIDI mapping algorithm is proposed. Experimental results show that the proposed method can adapt to various road types, and the resulting audio information successfully indicates the position and size of obstacles.

영상기반 지능형 무인 화재감시 시스템

본 논문은 퍼지 칼라모델을 이용한 영상기반의 지능형 무인 화재감시 시스템을 제안한다. 일반적으로 화재 감시를 위해 열이나 연기를 감지하는 별도의 장치를 사용하지만, 널리 보급된 폐쇄회로를 이용하면 별도의 장치와 추가적인 비용 없이 화재를 감시할 수 있다. 이와 같이 영상만으로 화재를 감시하는 시스템은 주로 연기나 불꽃을 추출하는 방법을 사용한다. 그러나 연기검출 방식은 야간에 회색계열의 연기를 검출하기 곤란하고, 불꽃검출 방식은 온도, 인화물질, 화재규모 등에 따른 불꽃색상의 변화에 대응하지 못하는 문제점을 가지고 있다. 본 논문은 무인환경 특히 야간 및 다양한 불꽃색상의 변화에 대응할 수 있는 강인한 화재감시 시스템을 다룬다. 이를 위해 폐쇄회로의 입력영상으로부터 움직임 영역을 추출하고, 퍼지 칼라모델을 이용한 색상과 히스토그램을 이용한 모양을 통해 불꽃 여부을 판단하고, 이것의 확산이 확인될 경우, 화재경보를 발령하는 시스템을 구현한다. 마지막으로, 통제된 실제 화재 실험을 통해 제안하는 방법의 유효성을 검증한다. In this paper, we propose a video-based intelligent unmanned fire surveillance system using fuzzy color models. In general, to detect heat or smoke, a separate device is required for a fire surveillance system, this system, however, can be implemented by using widely used CCTV, which does not need separate devices and extra cost. The systems called video-based fire surveillance systems use mainly a method extracting smoke or flame from an input image only. The smoke is difficult to extract at night because of its gray-scale color, and the flame color depends on the temperature, the inflammable, the size of flame, etc, which makes it hard to extract the flame region from the input image. This paper deals with a intelligent fire surveillance system which is robust against the variation of the flame color, especially at night. The proposed system extracts the moving object from the input image, makes a decision whether the object is the flame or not by means of the color obtained by fuzzy color model and the shape obtained by histogram, and issues a fire alarm when the flame is spread. Finally, we verify the efficiency of the proposed system through the experiment of the controlled real fire.

Adaptive fuzzy color segmentation with neural network for road detections

In this paper a new color space, called the RGB color ratio space, is proposed and defined according to a reference color such that an image can be transformed from a conventional color space to the RGB color ratio space. Because a color in the RGB color ratio space is represented as three color ratios and intensity, the chrominance can be completely reserved (three color ratios) and the luminance can be de-correlated with the chrominance. Different from traditional distance measurement, a road color model is determined by an ellipse area in the RGB ratio space enclosed by the estimated boundaries. A proposed adaptive fuzzy logic in which fuzzy membership functions are defined according to estimated boundaries is introduced to implement clustering rules. Therefore, each pixel will have its own fuzzy membership function corresponding to its intensity. A basic neural network is trained and used to achieve parameters optimization. The low computation cost of the proposed segmentation method shows the feasibility for real time application. Experimental results for road detection demonstrate the robustness to intensity variation of the proposed approach.

Classification and Tracking of Hand Region Using Deformable Template and Condensation

In this paper, we propose the classification and tracking method of the hand region using deformable template and condensation. To do this, first, we extract the hand region by using the fuzzy color filter and HCbCr color model. Second, we extract the edge of hand by applying the Canny edge algorithm. Third, we find the first template by calculating the conditional probability between the extracted edge and the model edge. If the accurate template of the first object is decided, the condensation algorithm tries to track it. Finally, we demonstrate the effectiveness and feasibility of the proposed method through some experiments.

Automatic License Plate Recognition System

This paper proposed an algorithm for license plate recognition system(LPRS). The vertical edge was first detected by sobel color edge detector. Then, the invalid edge was removed regarding edge density. Next, the license plate(LP) image was converted into HSV color model, and by edge density template and fuzzy color information judgement, the LP region was located. Then, color-reversing judgement and tilt correction was conducted. Afterward, characters were segmented by means of vertical projection and convolution, by which character width and position can be exactly confirmed, and character recognition was conducted based on radial basis function (RBF) neural network. With a lot of samples verified in night hours and daytime under real conditions, the experimental results show that the proposed method can achieve accuracy and effectiveness in LPRS.