  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.
