TY - GEN
T1 - A novel edge detection method based on image energy and skewness with application to intramuscular fat recognition
AU - Hussein, W. B.
AU - Moaty, A. A.
AU - Hussein, M. A.
AU - Becker, T.
PY - 2010
Y1 - 2010
N2 - Effective edge detection algorithms are important in image segmentation processes, because they increase the success in identifying objects and save the computational time in the further processing steps. One drawback of the common gradient edge detectors is related to the smoothing step of the original image. These detectors whether ignore this step, producing noise- sensitive detection results, such the detection with Roberts, Prewitt, and Sobel detectors, or apply a Gaussian smoothing filter, at which the detection results are dependent on the chosen filter size, such the detection with Canny detector. In this paper, a novel edge detection method is presented based on image energy and skewness as two smoothed versions of the original image. The method has been tested for various types of real world images. The experimental results gave at least 6.451% increment in the signal to noise ratio, and 1.667% reduction in the root mean square error, in comparison to the results of Canny edge detector. Consequently, the method had been applied efficiently to detect the intramuscular fat contents in a meat slice image, which is a quite complex problem due to the difficulty of the meat-fat structures interference.
AB - Effective edge detection algorithms are important in image segmentation processes, because they increase the success in identifying objects and save the computational time in the further processing steps. One drawback of the common gradient edge detectors is related to the smoothing step of the original image. These detectors whether ignore this step, producing noise- sensitive detection results, such the detection with Roberts, Prewitt, and Sobel detectors, or apply a Gaussian smoothing filter, at which the detection results are dependent on the chosen filter size, such the detection with Canny detector. In this paper, a novel edge detection method is presented based on image energy and skewness as two smoothed versions of the original image. The method has been tested for various types of real world images. The experimental results gave at least 6.451% increment in the signal to noise ratio, and 1.667% reduction in the root mean square error, in comparison to the results of Canny edge detector. Consequently, the method had been applied efficiently to detect the intramuscular fat contents in a meat slice image, which is a quite complex problem due to the difficulty of the meat-fat structures interference.
KW - Edge detection
KW - Feature extraction
KW - Image processing
KW - Intramuscular fat recognition
KW - Non-maximum suppression
UR - http://www.scopus.com/inward/record.url?scp=79955138591&partnerID=8YFLogxK
M3 - Conference contribution
AN - SCOPUS:79955138591
SN - 9789728939229
T3 - Proc. of the IADIS Int. Conf. - Computer Graphics, Visualization, Computer Vision and Image Processing, CGVCVIP 2010, Visual Commun., VC 2010, Web3DW 2010, Part of the MCCSIS 2010
SP - 93
EP - 100
BT - Proc. of the IADIS Int. Conf. - Computer Graphics, Visualization, Computer Vision and Image Processing, CGVCVIP 2010, Visual Commun., VC 2010, Web3DW 2010, Part of the MCCSIS 2010
T2 - IADIS Int. Conf. - Computer Graphics, Visualization, Computer Vision and Image Processing, CGVCVIP 2010, Visual Commun., VC 2010, Web Virtual Reality and Three-Dimensional Worlds, Web3DW 2010, Part of the MCCSIS 2010
Y2 - 27 July 2010 through 29 July 2010
ER -