• Journal of the Korean Institute of Telematics and Electronics S
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• v.34S no.3
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• pp.97-107
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• 1997

Image segmentation is teh important step in image infromation extraction for computer vison sytems. Fuzzy clustering methods have been used extensively in color image segmentation. Most analytic fuzzy clustering approaches are derived from the fuzzy c-means (FCM) algorithm. The FCM algorithm uses th eprobabilistic constraint that the memberships of a data point across classes sum to 1. However, the memberships resulting from the FCM do not always correspond to the intuitive concept of degree of belongingor compatibility. moreover, the FCM algorithm has considerable trouble above under noisy environments in the feature space. Recently, the possibilistic C-mean (PCM) for solving growing for color image segmentation. In the PCM, the membersip values may be interpreted as degrees of possibility of the data points belonging to the classes. So, the problems in the FCM can be solved by the PCM. The clustering results by just PCM are not smoothly bounded, and they often have holes. So, the region growing was used as a postprocessing. In our experiments, we illustrated that the proposed method is reasonable than the FCM in noisy enviironments.

• Journal of information and communication convergence engineering
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• v.2 no.1
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• pp.32-35
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• 2004

Human ability to perceive colors is a very subjective matter. The task of measuring and analyzing appropriate colors from colored images, which matches human sensitivity for perceiving colors, has been a challenge to the research community. In this paper we propose a novel approach, which involves the use of fuzzy logic and reasoning to analyze the RGB color intensities extracted from sensory inputs to understand human sensitivity for various colors. Based on this approach, an intelligent system has been built to predict the subject's personality. The results of experiments conducted with this system are discussed in the paper.

• Science of Emotion and Sensibility
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• v.5 no.3
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• pp.11-20
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• 2002

There are Two new models developed for objective evaluation of fabric color patterns by applying a multiple regression analysis and an adaptive foray-rule-based system. The physical features of fabric color patterns are extracted through digital image processing and the emotional features are collected based on the psychological experiments of Soen[3, 4]. The principle physical features are hue, saturation, intensity and the texture of color patterns. The emotional features arc represented thirteen pairs of adverse adjectives. The multiple regression analyses and the adaptive fuzzy system are used as a tool to analyze the relations between physical and emotional features. As a result, both of the proposed models show competent performance for the approximation and the similar linguistic interpretation to the Soen's psychological experiments.

• Journal of the Korea Society of Computer and Information
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• v.12 no.3
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• pp.1-8
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• 2007

In recent, the issues of sensitivity and psychology of human have received much attention from researchers and practitioners. In this paper. we analyze the information of color and location in order to detect the sensitivity and psychology by means of human vision on color space organization in a presented picture. After this process, we propose a method to determine psychology states through the space organization by using a fuzzy membership function which can be used to analyze direction information for the sensitivity. The proposed method is applied to the psychology states based on the space organization of Alschuler and Hattcick's method and to the space organization of Gunnwald's method. As a result, we present that the proposed method is very similar to a pattern classification of Alschuler and Grunwald.

• Journal of Digital Contents Society
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• v.9 no.2
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• pp.245-250
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• 2008

Psychology of color is a research field of psychology for studying human's behavior connected with color. Color carries symbolism and image while sharing psychological consensus with human. Each color has a respective image such as hope, passion, love, life, death, and so on. Peculiar stimuli by colors on these images have great influence on human's emotion and psychology. We therefore proposed a method for understanding human's state of emotion based on colors in this paper. In order to understand human's state of emotion, we analyzed color information used to model a room by a user and then described frequencies of each color as percent using fuzzy inference rules by membership values of fuzzy membership functions for colors used for modeling the room. When we applied the proposed color-based emotion analysis method to emotional state based on colors of Alschuler and Hattwick, we could see the proposed method is efficient.

• Journal of Institute of Control, Robotics and Systems
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• v.10 no.7
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• pp.597-603
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• 2004

In retrieving large database of image data, the clustering is essential for fast retrieval. However, it is difficult to cluster a number of image data adequately. Moreover, current retrieval methods using similarities are uncertain of retrieval accuracy and take much retrieving time. In this paper, a suggested image retrieval system combines Fuzzy ART neural network algorithm to reinforce defects and to support them efficiently. This image retrieval system takes color and texture as specific feature required in retrieval system and normalizes each of them. We adapt Fuzzy ART algorithm as neural network which receive normalized input-vector and propose improved Fuzzy ART algorithm. The result of implementation with 200 image data shows approximately retrieval ratio of 83%.

• Journal of Biosystems Engineering
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• v.25 no.1
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• pp.55-62
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• 2000

Autonomous farm operation needs to be developed for safety, labor shortage problem, health etc. In this research, an autonomous tractor for tillage was investigated using machine vision and a fuzzy logic controller(FLC). Tractor heading and offset were determined by image processing and a geomagnetic sensor. The FLC took the tractor heading and offset as inputs and generated the steering angle for tractor guidance as output. A color CCD camera was used fro the image processing . The heading and offset were obtained using Hough transform of the G-value color images. 15 fuzzy rules were used for inferencing the tractor steering angle. The tractor was tested in the file and it was proved that the tillage operation could be done autonomously within 20 cm deviation with the machine vision and the FLC.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.18 no.7
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• pp.350-356
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• 2017

With increasing attention to real-time body detection, active research is being conducted on human body detection based on skin color. Despite this, most existing skin detection methods utilize static skin color models and have detection rates in images, in which colors are distorted. This study proposed a method of detecting the skin region using a fuzzy classification of the gradient map, saturation, and Cb and Cr in the YCbCr space. The proposed method, first, creates a gradient map, followed by a saturation map, CbCR map, fuzzy classification, and skin region binarization in that order. The focus of this method is to rigorously detect human skin regardless of the lighting, race, age, and individual differences, using features other than color. On the other hand,the borders between these features and non-skin regions are unclear. To solve this problem, the membership functions were defined by analyzing the relationship between the gradient, saturation, and color features and generate 108 fuzzy rules. The detection accuracy of the proposed method was 86.35%, which is 2~5% better than the conventional method.

• Journal of Korea Multimedia Society
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• v.15 no.6
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• pp.761-769
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• 2012

In this paper, a setting method of initial cluster centers and color image segmentation using superpixels and Fuzzy C-means(FCM) algorithm is proposed. Generally, the FCM can be widely used to segment color images, and an element is assigned to any cluster with each membership values in the FCM. However the algorithm has a problem of local convergence by determining the initial cluster centers. So the selection of initial cluster centers is very important, we proposed an effective method to determine the initial cluster centers using superpixels. The superpixels can be obtained by grouping of some pixels having similar characteristics from original image, and it is projected $La^*b^*$ feature space to obtain the initial cluster centers. The proposed method can be speeded up because number of superpixels are extremely smaller than pixels of original image. To evaluate the proposed method, several color images are used for computer simulation, and we know that the proposed method is superior to the conventional algorithm by the experimental results.

• Journal of the Korean Institute of Intelligent Systems
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• v.20 no.4
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• pp.516-521
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• 2010

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.