A Fast Threshold Segmentation Method for Shoe Print Images under Bad Exposure Conditions

Abstract

The invention discloses a fast threshold segmentation method suitable for shoe print images with poor exposure quality. Firstly, the image is pre-segmented, and the structural elements used in the morphological operation are adaptively determined according to the distribution characteristics of its 4-connected regions. Radius; secondly, use morphological opening operation to eliminate the influence of poor exposure or equipment artifacts, and then perform grayscale transformation to enhance image contrast; finally, use morphological dilation to remove residual non-shoe print pixels to obtain the final shoe print image. This method does not require human interaction, and can segment shoe print images in large batches and fully automatically. It is significantly better than manual segmentation methods and iterative segmentation methods in terms of time efficiency, and the segmentation accuracy is higher than traditional global threshold segmentation methods and iterative segmentation methods.

Description

technical field [0001] The invention relates to the field of digital image processing, in particular to a shoe print image under bad exposure conditions with high segmentation accuracy and good real-time performance, which can effectively improve the mis-segmentation phenomenon existing in traditional global threshold segmentation under the condition of low exposure quality. Fast Thresholding Segmentation Method. Background technique [0002] Shoe prints are one of the most commonly encountered traces at crime scenes, and are also one of the important bases for investigators to discover clues to solve crimes, reveal and prove crimes. In shoe print image processing, people often only need to extract a specific part of the shoe print, and hardly pay attention to its background area. In this case, it is necessary to segment the shoe print part from the image. [0003] A case will be exposed to two types of shoe print images during the detection process: The image of the sho...

AI Technical Summary

Problems solved by technology

Conversely, for a class of shoe print images with small grayscale differences, little difference between the grayscale values ​​of the target and the background, and complex lighting conditions, the segmentation results are prone to missegmentation

Especially when the stamper imaging equipment is aging or used too many times, the shoe print image will be underexposed, overexposed or imprinted, and the typical threshold segmentation algorithm cannot accurately separate the shoe print pattern from the background

[0011] Although scholars at home and abroad have proposed many of the image segmentation algorithms mentioned above, there is still no general image segmentation algorithm suitable for poor exposure quality, especially the lack of an automatic shoe print with high accuracy, good real-time performance, and no human interaction. Image Segmentation Algorithm

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