TY - GEN
T1 - An approach for color edge detection with automatic threshold detection
AU - Arpitha, M. D.
AU - Arakeri, Megha P.
AU - Reddy, G. Ram Mohan
PY - 2012
Y1 - 2012
N2 - Edge is an important feature for image segmentation and object detection. 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. 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 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.
AB - Edge is an important feature for image segmentation and object detection. 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. 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 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.
UR - https://www.scopus.com/pages/publications/84859595321
UR - https://www.scopus.com/pages/publications/84859595321#tab=citedBy
U2 - 10.1007/978-3-642-29280-4_13
DO - 10.1007/978-3-642-29280-4_13
M3 - Conference contribution
AN - SCOPUS:84859595321
SN - 9783642292798
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 117
EP - 124
BT - Advanced Computing, Networking and Security - International Conference, ADCONS 2011, Revised Selected Papers
T2 - International Conference on Advanced Computing, Networking and Security, ADCONS 2011
Y2 - 16 December 2011 through 18 December 2011
ER -