19-11-2012, 05:36 PM
Edge Detection by Maximum Entropy: Application to Omnidirectional and Perspective Images
Edge Detection by Maximum.pdf (Size: 266.55 KB / Downloads: 25)
ABSTRACT
In the edge detection, the classical operators based on the derivation are sensitive to noise which causes
detection errors. It is even more erroneous in the case of omnidirectional images, due to geometric distortions
caused by the used sensors. This paper proposes a statistical method of edge detection invariant to image
resolution applied to omnidirectional images without preliminary treatments. It is based on the entropy measure.
The authors compared its behavior with existing methods on omnidirectional images and perspectives
images. The criteria of comparisons are the parameters of Fram and Deutsch. For omnidirectional images,
the authors used two types of neighborhood: fixed and adapted to the parameters of the sensor. The authors
compared the results of detection visually. The tests are performed on grayscale images.
INTRODUCTION
The edge detection is an essential step in the
computer vision systems because it influences
the result of the treatments which follow: segmentation,
image registration, 3D reconstruction,
etc.
An edge is a transition zone separating two
different textures in which the local statistical
characteristics of the image may vary slightly
(Keskes et al., 1979).
In literature, several researches on edge
detection based on the derivation have been
presented. The proposed detectors can be divided
into two large families. The first family
is based on finding local maxima of the first
derivative. The gradient operator is often used.
The second one is based on the cancellation of
the second derivative. In this case, the Laplace
operator is commonly used.