Geometric graphic feature point-based method for establishing shape descriptor

Abstract

The invention relates to a new geometric invariant (namely feature number)-based shape descriptor, and belongs to the field of computer vision. The shape descriptor is an outline-based shape descriptor which combines a global feature and a local feature. A geometric graphic feature point-based method for establishing the shape descriptor comprises the following steps of: sampling convex hulls of an image uniformly to obtain a series of sample points; and selecting three points sequentially among the sample points according to the definition of feature numbers to obtain a series of feather numerical values, so that a feature vector, namely the shape descriptor of the image is formed. According to the shape descriptor, the information of an internal outline of a shape is utilized fully; the content which is described by each component of the descriptor can cover a certain area of the shape, so that the capacity and accuracy degree of describing the shape are improved; the descriptor is used as a projection invariant, so that the shape descriptor can be suitable for various geometric transformations; and for the interference of noise of different degrees, the shape descriptor is high in stability. In the matching stage, a point-to-point dynamic matching mode is replaced by a first-point matching mode, so that recognition time is prolonged greatly. The feature number-based shape descriptor has high compactness and stability and high practical value.

Description

technical field [0001] The invention relates to the field of digital image processing, in particular to a method based on geometric feature point shape descriptors. Background technique [0002] Shape descriptors are a very important means in object recognition. Shape descriptors characterize the shape of objects and can be widely used in various situations in the field of computer vision, such as robot navigation, feature recognition, image retrieval, event detection, etc. Shape descriptors often reduce the accuracy of describing shapes due to different degrees of geometric transformation and noise, and the accuracy of image recognition decreases. In the past few decades, in order to get richer information from the shape and find a more stable shape description method for various geometric transformations and noise interference, people have been working on the research of shape descriptors. [0003] Shape descriptors can be divided into two categories: region-based method...

AI Technical Summary

Problems solved by technology

However, the cross ratio spectrum fails to make full use of the information of the internal contour of the shape and the internal area of ​​​​the image involved in each component of the descriptor is only limited to the vicinity of the line between points on the boundary. At the same time, point-to-point dynamics must be used in the matching stage. Matching algorithms, especially when dealing with complex shapes, have high computational complexity,

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