Wood counting method based on contour optimization

Abstract

The invention discloses a wood counting method based on contour optimization. The method comprises the steps that dimension conversion is carried out on a wood image; a wood target image is extracted from the conversed image through a color model, and edge information in the image is extracted through an edge operator; the extracted wood target image and the edge information are merged to acquire a pre-segmented image; the outline of a candidate target is extracted from the pre-segmented image, and extracted outer contour and inner contour are respectively corroded and expanded for optimization; and the above process is repeated until the number of extracted outer contours is invariant and the inner contour is not present. According to the optimization process, a false target and the adherence of targets are eliminated for the wood candidate target of the pre-segmented image; and the extracted wood target can be directly used for counting. According to the invention, the validity of the method is experimentally verified.

Description

technical field [0001] The invention belongs to the technical field of image processing, and in particular relates to a wood counting method based on contour optimization. Background technique [0002] The research on the automatic counting method of wood quantity has important significance to the effective use of forest and the control of deforestation. The traditional method is to manually count the amount of felled wood, which not only consumes a lot of manpower and material resources, but also is inefficient. [0003] There are not many existing technologies in the automatic counting algorithm of wood based on image processing; the existing typical automatic counting algorithm of wood based on image processing all uses the color model to extract the wood section, and then performs image pre-processing on this basis. Segmentation and denoising filtering to extract effective wood candidate targets, and finally count these extracted wood targets with algorithms such as are...

AI Technical Summary

Problems solved by technology

However, the wood candidate targets obtained after image pre-segmentation often have the problems of many false alarm targets and many adhesions between targets. These problems directly affect the accuracy of wood counting, and even make the extracted wood target images unable to be used directly. wood count

In the prior art, mathematical morphological operators are mostly used to globally process the entire image to solve the above-mentioned false alarm and adhesion problems after pre-segmentation of the image; this method is difficult to design a general structural operator applicable to the entire image, and to Dealing with various real wood targets and false alarm targets, it is also difficult to effectively deal with the complex adhesion between targets

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