Image Segmentation of Thermal Waving Inspection based on Particle Swarm Optimization Fuzzy Clustering Algorithm

Abstract

The Fuzzy C-Mean clustering (FCM) algorithm is an effective image segmentation algorithm which combines the clustering of non-supervised and the idea of the blurry aggregate, it is widely applied to image segmentation, but it has many problems, such as great amount of calculation, being sensitive to initial data values and noise in images, and being vulnerable to fall into the shortcoming of local optimization. To conquer the problems of FCM, the algorithm of fuzzy clustering based on Particle Swarm Optimization (PSO) was proposed, this article first uses the PSO algorithm of a powerful global search capability to optimize FCM centers, and then uses this center to partition the images, the speed of the image segmentation was boosted and the segmentation accuracy was improved. The results of the experiments show that the PSO-FCM algorithm can effectively avoid the disadvantage of FCM, boost the speed and get a better image segmentation result.

Bio-Inspired Algorithms for Color Image Segmentation

“…In [13], results indicated that ant colony optimization and PSO can improve the K-means and the simple competitive learning SCL algorithm for image segmentation. In [14], the speed of the Fuzzy C-means (FCM) image segmentation was enhanced and its accuracy was improved based on PSO as FCM centers optimizer. This work [15] proved that the ABC-based image segmentation has the best results compared to K-means, FCM and PSO-based segmentation.…”

CGCI-SIFT: A More Efficient and Compact Representation of Local Descriptor

“…From the left sub-figure of Fig.2., we can see that its inner region is separated into four nonoverlapping sub-regions 0 1 2 3 , , , R R R R , and its peripheral region is separated into sixteen non-overlapping subregions 4 , , ... R R R R . Hence, there are 4×8=32 entries in the inner descriptor, and 2×16=32 entries in the peripheral descriptor.…”

“…All these properties make local descriptor algorithms be widely applied in many fields, such as, content-based large-scale retrieval [2], video analysis, copy detection, object recognition, photo tourism, and 3D reconstruction [3]. A good local descriptor algorithm should have following characteristics: no necessity of pre-segmenting images [4], high repeatability of feature detector, low dimension of feature descriptor, robustness to partial occlusion, and invariance against image transformations, such as, illumination, rotation, scale, blur, and affine.…”

A Heuristic Group Intelligence Control System Based on Hybrid Particle Swarm Gravitational Search Algorithm

“…In the queue robot make decisions independently, through the coordination and cooperation between each other from the current position to determine their next position [12]. This paper proposed a new methodology to Group Intelligence Control by using a distributed control algorithm and a Swarm Intelligence algorithm to optimize the real-time control of each object [6]. In the first part of this paper, we introduced the appearance and the development of Group Intelligence Control system.…”