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Self-adaptive image segmentation method based on Otsu method and K-means method

A technology of image segmentation and K-means, which is applied in the field of fast adaptive image segmentation, can solve problems such as unsatisfactory fast segmentation conditions, deviation, and huge calculation amount, and achieve the effect of reducing the number of iterations and calculation time

Active Publication Date: 2020-06-26
UNIV OF ELECTRONIC SCI & TECH OF CHINA
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Problems solved by technology

Since the multi-threshold Otsu method is to search for the best threshold value in the corresponding gray level range of each segmented area, the calculation is huge and cannot meet the fast segmentation conditions; There is a certain deviation

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  • Self-adaptive image segmentation method based on Otsu method and K-means method
  • Self-adaptive image segmentation method based on Otsu method and K-means method
  • Self-adaptive image segmentation method based on Otsu method and K-means method

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Embodiment Construction

[0066] Below in conjunction with accompanying drawing, technical scheme of the present invention is described in further detail:

[0067] The invention discloses an adaptive image segmentation method based on the combination of OTSU and K-means. The main process of the method is as follows figure 1 As shown, including median filter denoising, grayscale, Otsu algorithm to automatically determine multiple thresholds, K-means method to adaptively segment images.

[0068] The detailed process of adaptive image segmentation method is as follows: figure 2 As shown, the specific steps are as follows:

[0069] Step 1: Target image denoising and grayscale

[0070] 1.1) Use the median filter to denoise the collected face image, sort the pixels in the sliding window of the original image, and replace the original image pixels with the middle pixels.

[0071] g(x,y)=median{f(m,n),(m,n)∈S}

[0072] 1.2) Image grayscale:

[0073] Linearly weighted calculations are performed on each ch...

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Abstract

The invention relates to the field of image segmentation, in particular to a self-adaptive image segmentation method based on an Otsu method and a K-means method. According to the method, the slack variable is introduced, the threshold value in the variable range is used as the local threshold value, the new threshold value search method is cyclically called by using the queue, and the plurality of local optimal threshold values are quickly and adaptively obtained, so that the problem of high calculation complexity of a multi-threshold Otsu method in an existing adaptive K-means image segmentation method is solved; the plurality of obtained thresholds are taken as the initial centroid of the K-means method, and the number of iterations of the K-means method is reduced; and the threshold obtained by clustering through the K-means method is used as the global optimal threshold, so that the image can be segmented accurately. According to the method, image illumination preprocessing servesas the purpose, the image can be segmented rapidly, accurately and adaptively, then the segmented area is corrected, and therefore information, lost due to illumination influence, of the image is recovered.

Description

technical field [0001] The invention relates to the field of image segmentation, in particular to an adaptive image segmentation method based on the Otsu method and the K-means method, and is a fast adaptive image segmentation method. Background technique [0002] In the face recognition system, the face images collected on the spot are easily affected by the lighting factors. Complex lighting causes the loss of part of the information of the face image, and then the loss of feature extraction, which affects the accuracy of face recognition. [0003] The K-means algorithm segmentation algorithm clusters similar pixel values ​​in the image, and the number of clusters formed is used as the number of image segmentation, but the algorithm generally sets the number of segmentation manually, so it cannot well meet the adaptive segmentation conditions; And it is sensitive to the initial centroid. If the initial centroid is not selected properly, the segmentation effect will be dete...

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Application Information

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IPC IPC(8): G06T7/10G06T7/136G06K9/62
CPCG06T7/10G06T7/136G06T2207/20032G06T2207/30201G06F18/23213
Inventor 李波李俊廷刘民岷
Owner UNIV OF ELECTRONIC SCI & TECH OF CHINA
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