Illumination-classification-based adaptive image segmentation method

Abstract

The invention discloses an illumination-classification-based adaptive image segmentation method which is used for accurately segmenting a target object under different illumination conditions. The illumination conditions are divided into two types, namely a frontlighting type and a backlighting type, by extracting color characteristics of an image to be processed in a red, green and blue (RGB) space and a hue, saturation and value (HSV) space and adopting a minimum euclidean distance classifier; a proper color characteristic quantity serving as a segmenting parameter is extracted from the image in the two illumination types and imported into a two-dimensional histogram; neighbor information of each pixel point is increased, so the interference resistance capacity is improved; and the acquired image is subjected to intelligent illumination judgment and precise segmentation. In the illumination-classification-based adaptive image segmentation method, a mode of judging the illumination condition first and then selecting a segmenting algorithm is adopted, so the algorithm has higher pertinence and the effectiveness of the algorithm is improved; meanwhile, illumination correction is not required, so the computing cost is reduced greatly; and a favorable condition is created for the subsequent image processing and analysis.

Description

technical field [0001] The invention relates to a digital image processing technology, in particular to an adaptive image segmentation technology under changing illumination conditions. Background technique [0002] Image preprocessing is a very critical link in machine vision technology. The accuracy and practicability of subsequent processing algorithms are based on the good preprocessing of the collected images. Illumination is one of the main factors affecting the segmentation effect of image preprocessing. Changes in the illumination environment make the images generated at different times even in the face of the same scene different, even very different. However, changes caused by illumination always exist in reality. Under different lighting conditions for the same image, the effect of the segmentation algorithm often deviates greatly, which seriously affects the subsequent analysis and research. At present, there are mainly the following methods to solve the lighti...

AI Technical Summary

Problems solved by technology

Under different lighting conditions for the same image, the effect of the segmentation algorithm often deviates greatly, which seriously affects the subsequent analysis and research

At present, there are mainly the following methods to solve the lighting problem: (1) use histogram equalization, logarithmic transformation and other methods to correct the lighting, but these methods involve complex calculations and a large amount of calculation, which is not suitable for real-time systems; (2) choose the appropriate Color feature space, weakening the influence of lighting, this method can only be used for specific scenes, once the lighting conditions change, the effect is poor

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