Method of extracting infrared pedestrian object by utilizing improved fuzzy clustering algorithm

Abstract

Provided is a method of extracting an infrared pedestrian object by utilizing an improved fuzzy clustering algorithm, comprising the steps of: step 1, detecting the position of a pedestrian object symmetric axis; first, employing a significance algorithm to obtain an area where a target is located, then utilizing a fuzzy C mean value algorithm to perform primary classification on the area where a target is located, summing each column of a primary classification result image to obtain a column summarization curve, and then solving the extreme value of the curve, the position of the extreme value being the position of the pedestrian object symmetric axis; step 2, in order to reduce the influence of intensity inhomogeneity in an infrared image to a clustering process, employing morphological close operation to preprocess an original image; step 3, employing an improved fuzzy clustering algorithm to perform clustering segmentation on the image after preprocessing; and steps 4, in order to obtain a segmentation result with a complete outline and smooth rim, performing subsequent processing on the clustering result: rejecting a non pedestrian target region and a small area region, and smoothening the rim.

Description

【Technical field】 [0001] The invention relates to a method for extracting infrared pedestrian targets by using an improved fuzzy clustering algorithm, especially fuzzy clustering technology and image segmentation technology, which have broad application prospects in various image-based application systems and belong to the field of digital image processing. 【Background technique】 [0002] Image segmentation is the process of dividing the image into several specific regions with special properties and extracting the required objects of interest. It is a key step from image processing to image analysis. Image segmentation is also for further image analysis, recognition, tracking, understanding, etc. The quality of the segmentation results will directly affect the accuracy of subsequent tasks. How to quickly and effectively segment the target of interest from the complex background has very important. The existing image segmentation methods are mainly divided into the followi...

AI Technical Summary

Problems solved by technology

However, the fuzzy C-means algorithm has a slow convergence speed and considers that the pixels are isolated. The algorithm does not consider any spatial information, which makes the algorithm sensitive to noise.

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