Abstract
This paper presents optimization methods for the K-means segmentation algorithm for a sequence of thermal images. Images of the sample response in the frequency domain to the thermal stimulation with a known spectrum were subjected to cluster segmentation, grouping pixels with similar frequency characteristics. Compared were all pixel characteristics in the function of the frame number and grouped using the minimal sum of deviations of the pixels from their segment mean for all the frames of the processed image sequence. A new initialization method for the K-means algorithm, using density information, was used. A K-means algorithm with a kd-tree structure C# implementation was tested for speed and accuracy. This algorithm divides the set of pixels to the subspaces in the hierarchy of a binary tree. This allows skipping the calculation of distances of pixels to some centroids and pruning a set of centroid clusters through the hierarchy tree. Results of the segmentation were compared with the K-means and FCM algorithm MATLAB implementations.
Highlights
As a result of thermal excitation, a solid gives us a temperature response that we can study in both time and frequency domains
This paper presents a method of optimizing a K-means algorithm for segmentation of thermal image sequences using a kd-tree structure, which divides a set of the pixels to subspaces in the hierarchy of a binary tree
In the experiments we used algorithms K-means and FCM in MATLAB, objectoriented algorithms developed in C# with the update of the segments at the end of an iteration or with a dynamic update and the K-means algorithm using the kd-tree structure
Summary
As a result of thermal excitation, a solid gives us a temperature response that we can study in both time and frequency domains. One can use cluster segmentation, which has a well-defined error and allows for an accurate quality comparison of various algorithms used for this purpose. This paper presents a method of optimizing a K-means algorithm for segmentation of thermal image sequences using a kd-tree structure, which divides a set of the pixels to subspaces in the hierarchy of a binary tree. This allows skipping of the pixels distance calculation to some centroids and pruning clusters from the set of clusters during a search of the hierarchy tree.
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