Edge detection is an important image processing task which has been used extensively in object detection and recognition. Over the years, many edge detection algorithms have been established, with most algorithms largely based around linear convolution operations. In such methods, smaller kernel sizes have generally been used to extract fine edge detail, but suffer from low noise tolerance. The use of higher dimension kernels is known to have good implications for edge detection, as higher dimension kernels generate coarser scale edges. This suppresses noise and proves to be particularly important for detection and recognition systems. This paper presents a generalized set of kernels for edge and line detection which are orthogonal to each other to yield nxn kernels for any odd dimension n. Some of the kernels can also be generalized to form mxn rectangular kernels. In doing so, it unifies small and large kernel approaches in order to reap the benefits of both. It is also seen that the Frei and Chen orthogonal kernel set is a single instance of this new generalization. Experimental results show that the new generalized set of kernels can improve edge detection results by combining the usefulness of both lower and higher dimension kernels.

Date Published: 10 February 2009
PDF: 12 pages
Proc. SPIE 7245, Image Processing: Algorithms and Systems VII, 72450U (10 February 2009); doi: 10.1117/12.805517

Published in SPIE Proceedings Vol. 7245:
Image Processing: Algorithms and Systems VII
Nasser M. Nasrabadi; Jaakko T. Astola; Karen O. Egiazarian; Syed A. Rizvi, Editor(s)