11-08-2012, 04:07 PM
Study and Comparison of Various Image Edge Detection Techniques
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INTRODUCTION
Edge detection refers to the process of identifying and locating sharp discontinuities in an image. The
discontinuities are abrupt changes in pixel intensity which characterize boundaries of objects in a scene.
Classical methods of edge detection involve convolving the image with an operator (a 2-D filter), which is
constructed to be sensitive to large gradients in the image while returning values of zero in uniform regions.
There are an extremely large number of edge detection operators available, each designed to be sensitive to
certain types of edges. Variables involved in the selection of an edge detection operator include Edge
orientation, Noise environment and Edge structure. The geometry of the operator determines a characteristic
direction in which it is most sensitive to edges. Operators can be optimized to look for horizontal, vertical, or
diagonal edges. Edge detection is difficult in noisy images, since both the noise and the edges contain highfrequency
content. Attempts to reduce the noise result in blurred and distorted edges. Operators used on noisy
images are typically larger in scope, so they can average enough data to discount localized noisy pixels.
PROBLEM DEFINITION
There are problems of false edge detection, missing true edges, producing thin or thick lines and problems due
to noise etc. In this paper we analyzed and did the visual comparison of the most commonly used Gradient and
Laplacian based Edge Detection techniques for problems of inaccurate edge detection, missing true edges,
producing thin or thick lines and problems due to noise etc.
Canny Edge Detection Algorithm
The Canny edge detection algorithm is known to many as the optimal edge detector. Canny's intentions were to
enhance the many edge detectors already out at the time he started his work. He was very successful in
achieving his goal and his ideas and methods can be found in his paper, "A Computational Approach to Edge
Detection"[11]. In his paper, he followed a list of criteria to improve current methods of edge detection. The first
and most obvious is low error rate. It is important that edges occurring in images should not be missed and that
there be no responses to non-edges. The second criterion is that the edge points be well localized. In other
words, the distance between the edge pixels as found by the detector and the actual edge is to be at a
minimum. A third criterion is to have only one response to a single edge. This was implemented because the
first two were not substantial enough to completely eliminate the possibility of multiple responses to an edge.
Based on these criteria, the canny edge detector first smoothes the image to eliminate and noise. It then finds
the image gradient to highlight regions with high spatial derivatives. The algorithm then tracks along these
regions and suppresses any pixel that is not at the maximum (nonmaximum suppression). The gradient array is
now further reduced by hysteresis. Hysteresis is used to track along the remaining pixels that have not been
suppressed. Hysteresis uses two thresholds and if the magnitude is below the first threshold, it is set to zero
(made a non edge).
CONCLUSIONS
Since edge detection is the initial step in object recognition, it is important to know the differences between edge
detection techniques. In this paper we studied the most commonly used edge detection techniques of
Gradient-based and Laplacian based Edge Detection. The software is developed using MATLAB 7.0.
Gradient-based algorithms such as the Prewitt filter have a major drawback of being very sensitive to noise.
The size of the kernel filter and coefficients are fixed and cannot be adapted to a given image. An adaptive
edge-detection algorithm is necessary to provide a robust solution that is adaptable to the varying noise
levels of these images to help distinguish valid image contents from visual artifacts introduced by noise.
The performance of the Canny algorithm depends heavily on the adjustable parameters, , which is the
standard deviation for the Gaussian filter, and the threshold values, ‘T1’ and ‘T2’. also controls the size of the
Gaussian filter. The bigger the value for , the larger the size of the Gaussian filter becomes. This implies more
blurring, necessary for noisy images, as well as detecting larger edges. As expected, however, the larger the
scale of the Gaussian, the less accurate is the localization of the edge.