Target saliency detection method for improving anti-interference performance
A detection method, a remarkable technology, applied in the direction of instruments, calculations, character and pattern recognition, etc., can solve the problems of small targets, inaccurate target detection, small target detection, etc., to achieve detection, accurate and significant detection effect, suppress background interference effect
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no. 1 Embodiment approach
[0078] The first specific embodiment is a target saliency detection method for improving anti-interference performance, characterized in that the method comprises the following steps:
[0079] Step 1, obtain the color image of target (area) by camera, carry out grayscale conversion to the color image that detection target obtains, obtain grayscale image I gray :
[0080] Step 2: Obtain the target saliency image related to the gray-scale scarcity; including the following steps:
[0081] S4, set the gray level as m, select M values for m, and for each gray level m, for the gray image I gray Quantize, that is, the grayscale image I gray Divide each pixel in (256 / m), respectively, to obtain a grayscale image corresponding to each m grayscale, a total of M quantized images; M is 4 or 8;
[0082] S5. For each quantized image, the gray value with a smaller number is assigned a larger significant value, that is, for each quantized image, the weight of the pixel with a larger numb...
no. 2 Embodiment approach
[0084] The second specific embodiment, a target saliency detection method for improving anti-interference performance, that is, a target saliency detection method combining grayscale stability and grayscale scarcity, the main steps are:
[0085] 1) Perform equal interval threshold segmentation on the grayscale image, and obtain multiple MSER regions by setting different judgment thresholds;
[0086] 2) Screen the number of pixels and aspect ratio of each MSER region, and superimpose the screened MSER regions to form an MSER significance map;
[0087] 3) Quantify the grayscale image by grades to obtain multiple quantized images;
[0088] 4) Count the grayscale histogram of each quantized image, and assign a larger significant value to the grayscale value with a smaller frequency in the histogram (corresponding to the scarce grayscale value in the image);
[0089] 5) Superimpose the saliency values of different quantized images to form a gray-scale scarcity saliency map;
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