Real-time Comparison of Performance Analysis of Various Edge Detection Techniques Based on Imagery Data

Edge Detection is one of the most essential steps for image processing to identify and detect discontinuity in intensity variation. It is an effective and an efficient tool to recognize different properties of an image such as shape, contrast, color, scene analysis, image segmentation etc. 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. This paper 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. 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.