Kernel Regression Based Technique For False Edge Elimination

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Date
2014-07
Authors
Abu Samah, Hafizi
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Publisher
Universiti Sains Malaysia
Abstract
Edge detection is the front end processing step in many computer vision applications. It is used to detect edges corresponding to the boundaries of objects contained in an image. Edge detection simplifies the image by filtering out the unwanted information and remained the significant information. The main challenge in edge detection is to reduce the effect of false edge detection. A false edge is usually detected on texture or noise. The current edge detection techniques are bound to certain limitations which lead to unsatisfactory result of edge detection. Furthermore, the current edge detection techniques can be considered as pre-processing techniques because the efforts of reducing the false edge detection are conducted prior to the edge detection. The pre-processing techniques are sensitive toward the changes of the original image or image gradient which can affect the locality of the detected edge. Thus, a post-processing technique of edge detection is proposed in this study. The proposed method is a technique of discriminating the true and false edges in an already detected edge image. The proposed method employs locally adaptive regression kernel (LARK) as feature descriptors and applies the concept of the LARK object and face detection algorithm to detect false edges. By using LARK, false edges are identified by comparing the detected edges’ LARK with the reference flat region kernel descriptor. False edges are detected if the edges’ LARK is similar to the reference flat region kernel descriptor. The detected false edges will be eliminated while the remaining edges are considered as the true edges. Three types of reference flat region kernel descriptor: Small, Medium and Large were proposed in this thesis. Each type of the reference flat region kernel descriptor is capable to eliminate false edges on different types of textured region. False edge elimination is optimized by combining the proposed reference flat region kernel descriptors. The reference flat region kernel descriptors were combined into three combinations: Small and Medium; Small and Large; Medium and Large. The proposed method was tested on images obtained from Berkeley segmentation dataset (BSDS) and University of Gronigen (RuG) dataset. The proposed method was evaluated qualitatively and quantitatively and the results were compared with other edge detection techniques. The qualitative evaluation result shows that the proposed method with Small and Medium combination produced edge images which contain few false edges and the images are better in comparison to other edge detection techniques. For qualitative evaluation, the proposed method has also produced the best results with average Baddeley’s delta metric and average F-measure, 23.37 and 0.2209, respectively.
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Keywords
Kernel regression based technique , for false edge elimination
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