Color Edge Detection in RGB Color Space using Automatic Threshold Detection
Anil Kamboj1, Kavita Grewal2, Ruchi Mittal3
1Kavita Grewal, Department of Electronics and Communication Engineering (Mtech Scholar), Kurukshetra University, jmit Radaur, Haryana, India.
2Anil Kamboj, Assistant Prof., Department of Electronics and Communication Engineering, jmit Radaur, Haryana, India.
3Ruchi Mittal, Department of Electronics and Communication Engineering (Mtech Scholar), Kurukshetra University, jmit Radaur, Haryana, India.
Manuscript received on July 29, 2012. | Revised Manuscript received on August 01, 2012. | Manuscript published on August 10, 2012. | PP: 41-45 | Volume-1 Issue-3, August 2012. | Retrieval Number: C0213071312/2012©BEIESP
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Edge detection is one of the most commonly used operations in image processing and pattern recognition, the reason for this is that edges form the outline of an object. An edge is the boundary between an object and the background, and indicates the boundary between overlapping objects. Edge detection reduces the amount of data needed to process by removing unnecessary features. Edge detection in color images is more challenging than edge detection in gray-level images. Compared with gray image, color image provides more edge information of objects. However, the current color edge detection algorithms acquired so much time to compute and they are hardly used in real-time system. In order to improve the efficiency and the performance of the color edge detection. This paper proposes a method for edge detection of color images with automatic threshold detection. The proposed algorithm extracts the edge information of color images in RGB color space with fixed threshold value. The algorithm works on three channels individually and the output is fused to produce one edge map. The algorithm uses the improved Kuwahara filter to smoothen the image, sobel operator is used for detecting the edge. A new automatic threshold detection method based on histogram data is used for estimating the threshold value. The method is applied for large number of images and the result shows that the algorithm produces effective results when compared to some of the existing edge detection methods.
Keywords: RGB color space Kuwahara filter, Sobel Operator, Histogram, Edge Thinning, Threshold.