Edge detection and texture segmentation based on independent component analysis

2003 ◽  
Author(s):  
Yen-Wei Chen ◽  
Xiang-Yan Zeng ◽  
Hanqing Lu
Keyword(s):  
Independent Component Analysis ◽  
Edge Detection ◽  
Component Analysis ◽  
Texture Segmentation ◽  
Independent Component

scholarly journals Edge-Detection in Noisy Images Using Independent Component Analysis

2011 ◽  
Vol 2011 ◽  
pp. 1-9 ◽  
Author(s):  
Kaustubha Mendhurwar ◽  
Shivaji Patil ◽  
Harsh Sundani ◽  
Priyanka Aggarwal ◽  
Vijay Devabhaktuni
Keyword(s):  
Independent Component Analysis ◽  
Edge Detection ◽  
Component Analysis ◽  
Independent Component ◽  
Training Phase ◽  
Noisy Image ◽  
New Approach ◽  
Detection Techniques ◽  
Noisy Images ◽  
Two Phases

Edges in a digital image provide important information about the objects contained within the image since they constitute boundaries between objects in the image. This paper proposes a new approach based on independent component analysis (ICA) for edge-detection in noisy images. The proposed approach works in two phases—the training phase and the edge-detection phase. The training phase is carried out only once to determine parameters for the ICA. Once calculated, these ICA parameters can be employed for edge-detection in any number of noisy images. The edge-detection phase deals with transitioning in and out of ICA domain and recovering the original image from a noisy image. Both gray scale as well as colored images corrupted with Gaussian noise are studied using the proposed approach, and remarkably improved results, compared to the existing edge-detection techniques, are achieved. Performance evaluation of the proposed approach using both subjective as well as objective methods is presented.

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