Real-Time Assessment of Edge Detection Techniques in Image Processing: A Performance Comparison | Chapter 7 | Contemporary Perspective on Science, Technology and Research Vol. 8

This study illustrates the performance analysis of the most commonly used edge detection techniques including Canny, Sobel and Prewitt, highlighting their advantages and disadvantages with respect to different types of datasets.  One of the most important stages in image processing to find and detect discontinuities in intensity change is edge detection. It is a useful tool for identifying various aspects of a picture, including shape, contrast, color, scene analysis, and image segmentation. The technique is very important to recognize all the edges accurately. It helps in object recognition, pattern recognition, medical image processing, motion analysis etc. There are many edge detection operators available in image processing.  After analyzing various parameters like Accuracy, Mean Square Error (MSE), Peak Signal to Noise Ratio (PSNR), Edge Detection Processing Time and Qualitative Human Visual Perception on two diverse type of datasets, varied results are found with respect to the techniques used. Among them, the most accurate and fast computed edge detection technique which gives better results on both type of datasets is concluded. Although the Sobel edge detection technique gives relatively poor result and weak performance of detection of edges, however it can be modified and further improved with respect to future work. The entire analyzing process was done under Scilab software. Canny works well also but it can be used for detecting very thin edges with the disadvantage of it cannot detect object very precisely because of detecting small amount of intensity variation. Sobel gives a very bad performance for small objects because it is used for detecting thick edges so sobel is best fit for detecting satellite images of large geographical area images. Future work can also be done for video edge detection and an improved sobel edge detection technique can be proposed which can detect thin edges also to overcome the disadvantage of limitation of geographical area. In future work the platform of comparison can also be change.

Author(s) Details:

Rajshree Kumari,
Department of Computer Engineering, G.B. Pant University of Agriculture and Technology, Pantnagar, Uttarakhand, India.

Divyanshu Chandra,
M.C.A. Programme, G.B. Pant University of Agriculture and Technology, Pantnagar, Uttarakhand, India.

Please see the link here: https://stm.bookpi.org/CPSTR-V8/article/view/14054