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Edge detection and Cellular Neural Boolean Filtering

Cellular neural Boolean filtering is nonlinear spatial-domain filtering reduced to direct processing of the image binary planes by Boolean functions. The most impressive representative of this family of filters is the Precise Edge Detection filter.

In contrast to other edge detection algorithms, the Precise Edge Detection algorithm, which is based on the concept of cellular neural Boolean filtering, ensures accurate detection of all the edges, without being dependent on the orientation, values of brightness jumps, size of details, or structure of objects.

The classical edge detection algorithms (such as Sobel, Prewitt, Laplace, and others) have two common disadvantages:

  1. They detect the edges corresponding to the large brightness jumps but usually miss the edges corresponding to the smaller brightness jumps;
  2. They do not differentiate between edges that correspond to upward and downward brightness jumps, and end up detecting the different edges together, as one combined edge.

As a result, the edges corresponding to the small brightness jumps often can not be detected, while those edges that are detected may be bold and inaccurate because of “double” detection of the edges corresponding to both upward and downward brightness jumps.

The Precise edge detection algorithm implements edge detection using three different detectors: the first one detects edges corresponding to upward brightness jumps; the second detects edges corresponding to downward brightness jumps; the third detector is used for the joint detection of upward and downward brightness jumps. This approach is objective, since it depends neither on a partial image, nor on the image statistical characteristics, nor on contrast, nor on dynamic range. Since the Precise Edge Detection algorithm is operating via separate processing of the image binary planes, it is possible to detect edges on the noisy images by excluding the processing of those binary planes where noise is primarily concentrated.

Using the same approach, the Precise Edge Detection algorithm may be used for edge detection by narrow direction. This kind of detection is far more effective than corresponding classical methods and it is very important for detection of the specifically oriented details in the image.

Another important application of the Precise Edge Detection algorithm is the Edged Segmentation of the image. This operation makes it possible to extract the regions with values of brightness (or levels of colors) that are equal or close to each other. As a result, the details and the textures with a complicated configuration may be extracted.

To date the Precise Edge Detection algorithm has produced the best results for the solution of the edge detection problems.


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Edged Segmentation Example

Original Image Edged Segmentation

Precise Edge Detection Example

Original Radar Satellite Image Precise Edge Detection Upward Precise Edge Detection Downward


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Last Updated
Mon, November 01, 2009 13:18