An Adaptive Algorithm Based on Image Segmentation

2009 ◽  
Author(s):  
Lang Liu ◽  
Yong Liu ◽  
Ying Lin
Keyword(s):  
Image Segmentation ◽  
Adaptive Algorithm

An Infrared Image Segmentation Algorithm Based on Spatial Correlative Information

2010 ◽  
Vol 44-47 ◽  
pp. 3274-3278
Author(s):  
Ke Yong Wang ◽  
Cheng Tian Song ◽  
Jia Hao Deng
Keyword(s):  
Image Processing ◽  
Computer Vision ◽  
Image Segmentation ◽  
Adaptive Algorithm ◽  
Infrared Image ◽  
Infrared Images ◽  
Background Condition ◽  
Complex Background ◽  
Infrared Image Segmentation ◽  
Image Segmentation Algorithm

Image segmentation is an important technique for image processing and computer vision. The principles of 1-D Otsu’s algorithm and thresholding through index of fuzziness are described. Since the infrared images of tank have low object-background contrasts and blurred boundaries in the complex background condition, an adaptive algorithm for image thresholding through index of fuzziness, which is combined with the spatial correlative information, is proposed. The new method makes full use of the spatial correlation of pixels, so that it can extract the detail of the image from the complex background effectively, and improve the accuracy of the segmentation. The results of experiments prove that the presented algorithm has better performance and better robustness against noise.

An Image Segmentation Method Using Image Enhancement and PCNN with Adaptive Parameters

2012 ◽  
Vol 490-495 ◽  
pp. 1251-1255 ◽  
Author(s):  
Hong Cai ◽  
Xue Yuan Zhang ◽  
Hai Tao Dai ◽  
Dong Ming Zhou
Keyword(s):  
Image Processing ◽  
Image Segmentation ◽  
Image Enhancement ◽  
Adaptive Algorithm ◽  
Experimental Results ◽  
Edge Extraction ◽  
Segmentation Method ◽  
Adaptive Parameters ◽  
Selection Of ◽  
Better Than

PCNN model is particularly suitable for image segmentation and edge extraction, but its effect depends on the selection of parameters in PCNN model and network iteration settings, which needs for a large number of artificial interaction and has limited PCNN image processing practicality. In this paper, through combining statistical properties of images and PCNN model, we present an adaptive algorithm based on the distribution of pixels to replace the artificial interaction. Experimental results show that image segmentation using image enhancement and PCNN with adaptive parameters is significantly better than the traditional PCNN image segmentation and verify the effectiveness of the method.

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