A Method for Extracting Image Texture Eigenvalues ​​Based on Four-Point Binary Model

Abstract

The invention provides an image texture characteristic value extraction method based on a four-point binary model. The method comprises the following steps of firstly calculating an average value of pixel values of four pixel points of 2*2 pixel blocks; secondarily calculating the difference between each pixel point and the average value, setting a location code value of the pixel block to be 1 if the difference is greater than or equal to a given threshold value, and otherwise setting the location code value of the pixel block to be 0; and finally multiplying the location code values of four pixel blocks by a corresponding weight, and summing the four products to obtain the texture characteristic value of the 2*2 pixel blocks. Due to the adoption of the method, the difference between each of four pixel point and the average value is calculated to obtain a 4bit binary code, so that the computation is reduced by 50 percent, the complexity is low, the local texture information of a face can be effectively reflected, and the method can be used for a face detection, identification and target tracking system.

Description

technical field [0001] The invention relates to the field of machine vision, in particular to a method for extracting image texture feature values ​​based on a four-point binary model, which can be applied to face detection, recognition and target tracking systems. Background technique [0002] Face detection and tracking technology is a key link in face recognition and a hot research topic in the field of pattern recognition. However, due to the existence of interference such as illumination changes, expression changes, occlusions, and complex backgrounds, face detection and tracking become difficult. In recent years, a large number of feature-based face detection and tracking methods have emerged, and the accuracy and real-time performance of these algorithms largely depend on the features used to characterize the face. [0003] Local Binary Pattern (LBP) is a relatively successful texture feature extraction method in the field of face recognition and texture analysis. How...

AI Technical Summary

Problems solved by technology

Since the pixel value of the center point itself also contains very important structural information, doing so will reduce the ability of LBP to distinguish texture features

In addition, the original LBP is defined on a 3x3 area. By calculating the difference between the pixel values ​​​​of the 8 neighborhoods around the center point and the pixel value of the center point, an 8-bit binary code is obtained, which has high computational complexity for real-time applications. , which is not conducive to building a faster and more robust face tracker

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