Man-machine interface (MMI) based wood image texture analyzing and identifying method

Abstract

The invention relates to a template matching image (MMI) based wood image texture analyzing and identifying method. The method comprises the following steps: selecting a sub-area from a part of a give wood image with remarkable characteristics to acquire a binary image of the sub-area; obtaining a matching image of the binary image by using a higher-order local auto-correlation (HLAC) template; extracting connected region characteristics, histogram characteristics and region moment characteristics from the matching image; repeating the steps from 1 to 3 to form a training sample set; and carrying out the steps from 1 to 3 on a wood image to be identified again, classifying by adopting a support vector machine (SVM) on the basis of the extracted characteristic set and the training sample set to acquire an identification result. The template matching image contains all information of images under the template, therefore the characteristics extracted on the basis of the matching image contain plentiful texture characteristics (including statistic and geometric characteristics) of the wood image; based on the integration of the extracted characteristics, the texture structure and the space relation of images can be expressed effectively, and the texture analysis on the image can be effectively performed, therefore better facilitating the application of subsequent classification and identification.

Description

technical field [0001] The invention relates to a wood image texture analysis and recognition method, in particular to a wood image texture analysis and recognition method based on a template matching image (MMI). Background technique [0002] Texture is an important visual cue and a ubiquitous but difficult to describe feature in images. Texture analysis technology is one of the basic research fields of computer vision, image processing, image analysis, image retrieval and other applications. Its research contents mainly include: texture classification and segmentation, texture synthesis, texture retrieval and shape restoration from texture. One of the most fundamental issues in these research contents is texture feature extraction. The goal of texture feature extraction is: the extracted texture features have small dimensions, strong discrimination ability, good stability, and the calculation amount is small during the extraction process, which can guide practical applica...

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Problems solved by technology

However, this method also has defects. For example, the features extracted by it can only reflect the local structural features of the image, and only record the number of times the template appears or the gray value conforming to the template for simple cumulative summation, which cannot be obtained. More and more effective expression of image features, especially the structural features of spatial relationships

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