Image sample library feature representing method based on grayscale distribution statistical information

Abstract

The invention discloses an image sample library feature representing method based on grayscale distribution statistical information. The image sample library feature representing method based on the grayscale distribution statistical information comprises the steps of selecting a certain number of a position dot pair collection according to an image size and a feature of a certain type of samples, then confirming mutual relations among position spot pairs to gray level average value in a field of all sample calculation position points of the type according to two gray level average values of the position spot pairs in the samples, confirming reliability and relevancy of mutual relations of the position dot pairs between the same type of samples and the type of samples and other types of samples, and finally selecting parts of position dot pairs which are high in reliability and small in relevancy and mutual relations of the position dot pairs from the initial position dot pair collection, wherein the position dot pairs and the mutual relations of the position dot pairs are used as character representations of the types of the samples. The image sample library feature representing method based on the grayscale distribution statistical information is especially suitable for an image sample library such as a feature extraction and representation of auto logos and road signs, wherein the image sample library is low in resolution, and an image structure feature of the image sample library is obvious,.

Description

technical field [0001] The invention relates to the technical field of image processing, in particular to a feature representation method of an image sample library based on gray distribution statistical information. Background technique [0002] With the development of machine vision theory and technology, the recognition and understanding of image content has become a research hotspot and has a broad application market. Especially in the field of intelligent transportation, there is an increasingly strong demand for image recognition of vehicle signs, road signs and signal signs. At present, image recognition is generally based on supervised learning methods, and it is necessary to establish a corresponding image sample library for training and learning according to application requirements. The key to building a sample library is to describe, extract and represent the sample features. Feature description uses image elements such as points, edges, colors, and textures to...

AI Technical Summary

Problems solved by technology

By establishing the feature representation method required by the sample library for a specific image, it solves the problem of feature extraction and representation in the case of sample images with low resolution, easy to be stained, and large lighting effects

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