Watershed texture imaging segmenting method based on morphology Haar small wave texture gradient extraction

Abstract

The invention discloses a segmentation method of a watershed texture image on the basis of morphological Haar wavelet gradient extraction, which relates to the technical filed of image processing and aims at solving the excessive segmentation problem of watershed texture image segmentation. The method not only can reduce excessive segmentation, but also is much better than other post processing methods solving the problem of excessive segmentation in arithmetic speed. The steps for realizing the method are as follows: (1) a texture gradient image TG is extracted by carrying out morphological Haar wavelet conversion to the original texture image; (2) after circumrotating by 90 degrees, the original image is used for extracting a texture gradient image RTG with a morphological Haar wavelet; (3) the texture gradient image RTG and the texture gradient image TG are subjected to circumrotation weighting to obtain a texture gradient image G; (4) morphological filtering is carried out to the texture gradient image G to obtain a texture gradient image OCG after being smoothed; and (5) marker-watershed segmentation is carried out to the texture gradient image OCG to obtain the final texture segmentation result. Compared with the image segmentation standards, the image segmentation result of the method is validated to basically meet standards.

Description

technical field [0001] The invention belongs to the technical field of image processing, and relates to an application in the field of texture image segmentation, in particular to a watershed texture image segmentation method based on morphological Haar wavelet texture gradient extraction. Background technique [0002] Image segmentation is a very important step in image processing, which divides an image into subregions or objects with strong correlations. Image segmentation is usually used for further image analysis, recognition, compression coding, etc. The accuracy of segmentation directly affects the effectiveness of subsequent tasks, so it is of great significance and is a very important step in image processing. Image segmentation algorithms generally fall into the following categories: threshold-based segmentation, edge-based segmentation, and region-based segmentation. The advantage of threshold-based segmentation is that the algorithm is simple, and closed and con...

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Problems solved by technology

[0005] The purpose of the present invention is to provide a texture segmentation image algorithm based on morphological Haar wavelet and watershed, which solves the over-segmentation problem of watershed. A texture gradient reflecting the boundary of the texture region, using this gradient as the input image to mark the watershed can well suppress over-segmentation

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