Home

About

Advanced Search

Browse by Discipline

Scientific Societies

E-print Alerts

Add E-prints

E-print Network
FAQHELPSITE MAPCONTACT US


  Advanced Search  

 
n this article, we present a review of techniques for the detection and classification of edges in color images. Edge detection is one of the most important tasks in image processing and
 

Summary: I
n this article, we present a review of techniques for the detection and classification of edges
in color images. Edge detection is one of the most important tasks in image processing and
scene analysis systems. It denotes the procedure of detecting meaningful discontinuities
(edges) of the image function (see Figure 1 for an example of edge detection in color and
gray-level image). The accuracy in detecting these discontinuities (edge detection) and the
efficiency in implementing these operations are important criteria for using an algorithm in the
area of computer vision. Inaccuracies in edge detection directly influence the results of a subse-
quent feature-based image processing technique, such as region segmentation, stereo analysis,
data coding, image retrieval, data hiding, watermarking, or recognition and tracking of objects
in image sequences.
Edges in gray-level images can be thought of as pixel locations of abrupt gray-level
change. A change in the image function can be described by a gradient that points in the
direction of the largest growth of the image function. Therefore, one edge detection tech-
nique is to measure the gradient vector magnitude at pixel locations. This method works best
when the gray-level transition is quite abrupt, like a step function. As the transition region
gets wider, it is more advantageous to apply second-order derivatives like the Laplacian. The
potential edge pixel locations can then be described by zero-crossings in the results.
While edge detection in gray-level images is a well-established area, edge detection in color
images has not received the same attention. The fundamental difference between color images

  

Source: Abidi, Mongi A. - Department of Electrical and Computer Engineering, University of Tennessee

 

Collections: Computer Technologies and Information Sciences