A Logo Enhancement Method for Adaptive Color Threshold Segmentation

Abstract

The invention relates to a logo enhancement processing method for adaptive color threshold segmentation. Existing methods cannot effectively distinguish samples with various influencing factors, which will interfere with the detection results. The method of the present invention adopts the approximate maximum and minimum value normalization method based on the cumulative distribution of the histogram to obtain the adaptive threshold, and after the image to be detected is subjected to red and blue standardization and contrast stretching, it is mapped to a gray probability histogram to find the foreground The position of the starting value of the target color, by extracting and comparing the gray value of the foreground, find the specific interval of the target area, select the parameter with the best comprehensive separation effect for color segmentation, and approximate the maximum and minimum of the original pixel value according to the critical value The value is normalized, the invalid background is screened out, the foreground color block is highlighted, and the overbright area is smoothed to weaken the influence of contrast, brightness and other environmental factors on the detection effect. The method of the present invention preserves the sign color area while screening out the invalid background as much as possible.

Description

technical field [0001] The invention belongs to the technical field of image processing, and relates to a sign enhancement processing method for adaptive color threshold segmentation, in particular to an approximate maximum and minimum value normalization method based on the cumulative distribution of histograms to obtain adaptive thresholds, thereby improving traffic Flag detection method for color segmentation. Background technique [0002] With the progress of urbanization and the popularity of automobiles, including the continuous advancement of unmanned vehicle technology, problems such as traffic congestion, frequent traffic accidents, and road traffic safety have become increasingly prominent, and the research field of intelligent transportation systems has emerged as the times require and is widely accepted. Concerned, road traffic sign detection and recognition, as a research hotspot in intelligent transportation systems, is an important part of road environment per...

AI Technical Summary

Problems solved by technology

[0006] 1. The scene of traffic sign detection is complex and changeable, especially affected by natural factors such as different light intensity and contrast, and the fixed threshold method cannot effectively distinguish such samples, which will affect further shape analysis or neural network Learning causes unnecessary false positives and more computational overhead

[0007] 2. This type of algorithm often only binarizes the source image after threshold segmentation, and divides all pixels into two types: 0 value and 255 value, and simple binarization ignores the actual image noise. Obviously, Bringing noise into the detection process as part of the sign will undoubtedly interfere with the detection results

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