scholarly journals Fuzzy Magnetic Color Image Segmentation and Advanced Edge Detection

2009 ◽  
Author(s):  
Christy George
Keyword(s):  
Fuzzy Logic ◽  
Image Segmentation ◽  
Edge Detection ◽  
Color Image ◽  
Color Images ◽  
Color Image Segmentation ◽  
Color Information ◽  
Sobel Operator ◽  
Logic Inference ◽  
The Given

In this paper, color image segmentation based on fuzzy logic has been studied. Anefficient Fuzzy logic inference engine based on magnetic fields has been implemented in this project so as to intelligently extract color information in the given image and classify it into the predominant color pyramid with the help of artificial color magnets. This also forms the basis for the priority based edge detection. Sobel operator is used in this project which is intelligently fused with the result of the above mentioned method to enhance the output so as to obtain a priority based enhanced edge detection output. Experimental results have demonstrated the effectiveness and superiority of the proposed method after extensive set of color images was tested.

Research of Object Recognition Algorithm Based on Variable Illumination

2011 ◽  
Vol 255-260 ◽  
pp. 2096-2100
Author(s):  
Song Hao Piao ◽  
Qiu Bo Zhong ◽  
Shu Ai Wang ◽  
Xian Feng Wang
Keyword(s):  
Image Segmentation ◽  
Object Recognition ◽  
Edge Detection ◽  
Color Image ◽  
Vision System ◽  
Recognition Algorithm ◽  
Color Image Segmentation ◽  
Color Information ◽  
Shape Information ◽  
Variable Illumination

The robot vision system is the critical component of the soccer robot, in football competition, robot perceive the most of the information from the vision system. Because of the variable illumination conditions, the traditional image segmentation method based on color information is not satisfactory. Based on the color information and shape information of the object, this paper proposes a object recognition algorithm that combine color image segmentation with edge detection. This algorithm implement image segmentation use color information in the HSV color space obtain the pixel of the object, then use this pixel implement edge detection to recognize the object. Experiments show that this algorithm can recognize the object exactly in the different illumination conditions, satisfy the requirement of the competition.

The Study of Forest Fire Color Image Segmentation

2011 ◽  
Vol 474-476 ◽  
pp. 2140-2145
Author(s):  
Si Li ◽  
Hong E Ren
Keyword(s):  
Image Segmentation ◽  
Forest Fire ◽  
Color Image ◽  
Color Space ◽  
Color Images ◽  
Color Image Segmentation ◽  
Hsv Color Space ◽  
Segmentation Methods ◽  
Time Periods ◽  
Night And Day

Combined with the composition characteristics of forest fire image background when the forest fire occurred during different time periods of night and day, different image segmentation methods were applied to the forest fire color images of different time periods respectively, which could improve the efficiency of image processing. Meanwhile, application of H and S components from HSV color space, the strategy on color image segmentation which processed the segmentation processing to forest fire color images with complicated background was proposed combined with Otsu algorithm. The results of simulation experiment showed that the above-mentioned segmentation methods were obtained satisfactory segmentation effects when the segmentation on forest fire color images during different time periods of night and day were processed respectively. And also application of Otsu algorithm based on HSV color model, the forest fire image segmentation occurred in the daytime was processed, which overcame the interference factors of light and smoke, as well as the shortage of noise sensibility due to Otsu algorithm.

scholarly journals Visual Sensor Based Image Segmentation by Fuzzy Classification and Subregion Merge

2017 ◽  
Vol 2017 ◽  
pp. 1-15
Author(s):  
Huidong He ◽  
Xiaoqian Mao ◽  
Wei Li ◽  
Linwei Niu ◽  
Genshe Chen
Keyword(s):  
Image Segmentation ◽  
Edge Detection ◽  
Color Image ◽  
Color Image Segmentation ◽  
Fuzzy Classification ◽  
Spatial Proximity ◽  
Visual Sensor ◽  
Detection Algorithms ◽  
Color Similarity ◽  
Different Color

The extraction and tracking of targets in an image shot by visual sensors have been studied extensively. The technology of image segmentation plays an important role in such tracking systems. This paper presents a new approach to color image segmentation based on fuzzy color extractor (FCE). Different from many existing methods, the proposed approach provides a new classification of pixels in a source color image which usually classifies an individual pixel into several subimages by fuzzy sets. This approach shows two unique features: the spatial proximity and color similarity, and it mainly consists of two algorithms: CreateSubImage and MergeSubImage. We apply the FCE to segment colors of the test images from the database at UC Berkeley in the RGB, HSV, and YUV, the three different color spaces. The comparative studies show that the FCE applied in the RGB space is superior to the HSV and YUV spaces. Finally, we compare the segmentation effect with Canny edge detection and Log edge detection algorithms. The results show that the FCE-based approach performs best in the color image segmentation.

The combination of edge detection and region extraction in nonparametric color image segmentation

1996 ◽  
Vol 92 (1-4) ◽  
pp. 277-294 ◽  
Author(s):  
Naoko Ito ◽  
Ryu Kamekura ◽  
Yoshihisa Shimazu ◽  
Teruo Yokoyama ◽  
Yutaka Matsushita
Keyword(s):  
Image Segmentation ◽  
Edge Detection ◽  
Color Image ◽  
Color Image Segmentation ◽  
Region Extraction
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