Log end surface image partitioning algorithm for improving active contour model based on circle constraint

Abstract

A log end surface image partitioning algorithm for improving an active contour model based on circle constraint comprises the following steps: initializing a contour line level set phi (x, y, t=0)=0, using an improved active contour model to control an evolution level set function, so as to complete precutting to log end surface images, then craking a single level set phi corresponding to the a pre-cut profile curve into n level set functions <phi>i (i=1,..., n, and n refers to a to-be-cut target number), using the <phi>i as an initial contour line, initializing into a signed distance function again, setting a proper circle constraint factor Tau, then using the improved active contour model based on circle constraint to divide the level set again so as to obtain a final log end surface contour line, and finishing cutting. The method provided by the invention has the advantages of an improved CV model and an LIF model at the same time, can effectively avoid image non-goal region and noise interferences through the combination of image overall and local information, the calculating is simple, the speed is fast, and the cutting effect on the log end surface is good. Great significance to accurate measuring and processing of the log end surface is achieved.

Description

Technical field [0001] The invention relates to an image segmentation algorithm, in particular to a log end face image segmentation algorithm based on circular constraint improved active contour model. Background technique [0002] In the past, in the process of timber measurement, processing and production, the use of manual rulers to detect the end faces of logs is not only low in efficiency and high in risk factors, but also has large errors due to human factors. With the development of digital image processing technology, the task of ruler inspection can be completed automatically, accurately and efficiently by using the collected log end face images. Among them, the accurate segmentation of log end face is the basis of log detection and automatic processing. Huang Yonglin et al. proposed a Hough transform circle detection method in Document 1 "A New Fast Hough Transform Circle Detection Method" (Journal of Electronic Measurement and Instrumentation, 2010, 24(9)), which...

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Problems solved by technology

At the same time, adding shape prior knowledge to the level set method can effectively eliminate the interference of non-target shape noise. ,2008,13(6)) proposed a CV model based on circular constraints, which can effectively segment the circular target of the target image and exclude the interference of the non-circular target contour, but it only uses the global information of the image, At the same time, during the curve evolution process, the center coordinates and radius of the constrained circle need to be updated continuously, and the calculation efficiency is low.

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