Dictionary based Approach to Edge Detection
This work addresses edge detection for image processing tasks, offering a method that adapts to image characteristics, but it appears incremental as it builds on dictionary-based approaches without a clear paradigm shift.
The authors tackled edge detection in image processing by developing a self-learning technique using a dictionary of eigenfilters derived from image features, which eliminates the need for pre- or post-processing and handles noise, blur, and illumination variations. They demonstrated its application across various image classes, claiming it detects edges more accurately and captures greater detail than existing algorithms like Sobel and Canny.
Edge detection is a very essential part of image processing, as quality and accuracy of detection determines the success of further processing. We have developed a new self learning technique for edge detection using dictionary comprised of eigenfilters constructed using features of the input image. The dictionary based method eliminates the need of pre or post processing of the image and accounts for noise, blurriness, class of image and variation of illumination during the detection process itself. Since, this method depends on the characteristics of the image, the new technique can detect edges more accurately and capture greater detail than existing algorithms such as Sobel, Prewitt Laplacian of Gaussian, Canny method etc which use generic filters and operators. We have demonstrated its application on various classes of images such as text, face, barcodes, traffic and cell images. An application of this technique to cell counting in a microscopic image is also presented.