Method for extracting human ear image edge combining multiple methods

Abstract

The invention provides a human ears image edge extraction method on the basis of combined universal gravitation field conversion, watershed algorithm and Canny arithmetic operators, comprising the steps of: 1, using the universal gravitation field conversion to pre-process original images in order to reduce the noise and irradiation as well as intensify edge information; 2, using the watershed algorithm to efficiently divide the images to obtain a minimum closed area comprising human ears only; then dividing out the image comprising the human ears inner side area only via the closing characteristic of the extracted edge; 3, extracting the edges of inner side part via the characteristic of the Canny arithmetic operators sensitive to small detail of the images; finally, synthesizing the outer and inner edges of the human ears image to eventually acquire the complete human ears edge curve. The human ears edge image obtained by the invention is comparatively clear and complete, having better effect than only using other methods and offering good basis for extraction and identification for the subsequent characteristics, so the efficiency stands a good chance of being improved.

Description

technical field [0001] The invention belongs to a human ear image edge extraction method based on the combination of gravitational field conversion, watershed algorithm and Canny operator. Especially for the processing of human ear images with noise pollution and edge interference, it is expected to achieve a higher recognition rate. Background technique [0002] As a new biometric identification technology, human ear recognition has attracted more and more attention. A complete human ear recognition system includes: collection of human ear images, image preprocessing, edge extraction and segmentation, feature extraction and recognition. However, the current research focuses more on the recognition features of the human ear, such as the main geometric methods at home and abroad, the principal component analysis method, the shape extraction method based on the long axis, the genetic local search method, and the recognition method using neural networks, etc. Wait. However, ...

AI Technical Summary

Problems solved by technology

[0004] When performing edge extraction on human ear images, due to the influence of shooting conditions, image noise pollution is often caused; at the same time, due to the influence of hair and other occlusions, false edges are often formed, which is very unfavorable for recognition

A large number of experiments have shown that it is difficult to effectively extract the complete edge curve of the human ear simply by using a certain edge extraction algorithm.

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