Images are always contaminated by noise, increasing uncertainty. Fuzzy set (FS) theory is a useful tool for dealing with uncertainty in images. When comparing the FS, an intuitionistic fuzzy (IFS) can better describe blurred characteristic images due to membership, nonmembership, and hesitation degrees. However, when applied image segmentation, IFS cannot completely overcome influence of noise. With aim performing noisy segmentation under several criteria, this paper defines noise robust...

The Possibilistic c-means clustering (PCM) is an important unsupervised pattern recognition method. However, it still faced with huge challenges in multidimensional data multiple characteristics, such as imbalanced sample sizes, feature components, noise and outlier corruption, the sparse distribution of small targets space caused by "curse dimensionality". In view this, this paper proposes a possibilistic algorithm based on Mahalanobis-Kernel Distance suppressed competitive learning...

Multiobjective evolutionary algorithms (MOEAs) are effective optimization methods. To improve the segmentation performance and time efficiency of MOEAs-based fuzzy clustering for color images, a semisupervised surrogate-assisted multiobjective kernel intuitionistic (S <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> MKIFC) algorithm is proposed in this article. The main contributions S MKIFC can be summarized as follows: 1) objective...

Fuzzy number type data is a typical class of fuzzy data, and it can be regarded as general form the interval crisp data. This paper studies clustering algorithm for triangular numbers. First all, we give novel distance between numbers by using three parameters number, prove that proposed complete metric on set And then, based this distance, propose two c-means algorithms dealing with Finally, some numerical examples are provided to illustrate algorithm's effectiveness.

Multilevel thresholding is one of the effective image segmentation methods. However, it faces three big challenges: (1) how to adaptively determine number multiple thresholds; (2) overcome sensitivity noise; (3) perform multilevel under several segment ation requirements. In order solve these problems, an adaptive algorithm based on multiobjective artificial bee colony optimization (AMT-MABCO) presented for noisy in this paper. To improve robustness AMT-MABCO noise, a line intercept...

Due to the poor filling effect of video image defect commonly used in stabilization field, is seemed still unstable after process, which seriously affects visual effect. To solve this problem, we improve a method based on time-series network prediction and pyramid fusion restoration proposed optimize stabilization. The flow as follows: First, it adaptive determine whether corresponding frame at current time needs padding inpainting. Then, for that be inpainting, frames generated before...

To solve the problems of image threshold segmentation based on weak continuous constraint theory, running time is long, and two parameters need to be selected manually, therefore a fast single-parameter energy function thresholding for region information (FSEFTISRI) proposed in this paper. The FSEFTISRI algorithm uses simple linear iterative clustering (SLIC) technology pre-block image, extract super-pixels, then map super-pixels interval type-2 fuzzy set (IT2FS), so as construct search...

Although the evidence c-means clustering (ECM) has capability to process uncertain information, it is not suitable for noisy image segmentation, because spatial information of pixels considered. To solve problem, an adaptive kernelized combining segmentation algorithm proposed. Firstly, noise distance that can be iteratively updated constructed using local pixels. Secondly, improve classification performance, kernel function proposed measure between pixel and cluster center. Simultaneously,...

From the constitution of computation, main subjects computer science and technology have been summarized. Meanwhile, reasons why developments software technologies pushed computational be widely used were analyzed. Multiple computing models discussed base on different architectures. Finally, computation as a methodology, two important finance problems, effective financial supervision high-speed product pricing, solved by distributed parallel computing.

In a world of economic globalization, financial industry faces many new challenges. Optimizing IT infrastructure utilization is one way to advance organizations' competitiveness. Web service and grid computing appear be increasing useful tools realize the aim. Using existing resources provide innovative subject paper. Grid-based (American) stock option trading brokerage as an example, system architecture its main issues, high-performance pricing platform designed. Java technologies, test set...