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.

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.

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

scholarly journals Color Image Segmentation without any Information Loss

2019 ◽  
Vol 9 (1) ◽  
pp. 1919-1923
Keyword(s):  
Image Segmentation ◽  
Edge Detection ◽  
Color Image ◽  
Information Loss ◽  
Color Image Segmentation ◽  
Gray Scale ◽  
Gray Scale Image ◽  
The Relationship ◽  
Grayscale Images

We can partition the background from foreground and locate the objects of interest using image segmentation techniques. In other words we can say image segmentation is the process of grouping adjacent pixels in to segments. In this research we proposed a model which can differentiate maximum and minimum frequencies for both color and grayscale images without any information loss. After getting the result of both images, we will check which (gray scale image or color image) gives better performance to the image segmentation techniques. So, here we will take the two techniques edge detection and threshold. This research gives better result of segmentation by using the relationship discontinuous and similar pixel values

Quantitative Evaluation for New Bone Growth of Bone Repairing Biomaterial Based on Color Image Segmentation

2007 ◽  
Vol 336-338 ◽  
pp. 2493-2496 ◽  
Author(s):  
Ya Hui Hua ◽  
Tian Fu Wang ◽  
Da Li Zhou ◽  
De Yu Li ◽  
Jiang Li Lin ◽  
...  
Keyword(s):  
Image Segmentation ◽  
Quantitative Evaluation ◽  
Bone Grafting ◽  
Bone Growth ◽  
Color Image ◽  
Vision System ◽  
Color Image Segmentation ◽  
Bone Repairing ◽  
Repairing Effect ◽  
In Virtue Of

Quantitative evaluation of repairing effect of bone grafting material is one of the essential studying subjects. However, traditional evaluation methods are subjective and qualitative. In this paper, the region of new bone from a bone repairing biomaterial planted image is extracted based on color image segmentation and then statistically analyzed to evaluate the property of bone grafting material quantitatively. HSI color model, which corresponds with people’s vision system for color is used to achieve ideal segmental results and effective utilization of color information. The S and I variable are used as thresholding condition for image segmentation, thereby obtaining the area of new bone. The effect of BMP contained in BMP/α-TCP is estimated furthermore. Experimental results show that the composite BMP/α-TCP induce more bone than pure α-TCP in virtue of BMP. This study provides theoretical and experimental suggestions for clinical applications of BMP/α-TCP.

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