Color segmentation robust to brightness variations by using B-spline curve modeling

Color segmentation takes a great attention because color is an effective and robust visual cue for characterizing an object from the others. However, color segmentation suffers from color variations incurred by irregular illumination changes. We propose a reliable color modeling approach in hue–saturation–intensity (HSI) color space while considering intensity information by adopting the B-spline curve fitting to make a mathematical model for statistical characteristics of a color with respect to intensity. It is based on the fact that color distribution of a single-colored object is not invariant with respect to brightness variations even in the HS (hue–saturation) plane. The statistical characteristics contain the mean and standard deviation of hue and saturation with respect to intensity. They are mathematically expressed as four bar graphs. In order to fit the bar graphs to continuous curves, we use B-spline curve fitting procedure. From several experimental results, we verify that the proposed algorithm is successfully applied to color segmentation under various illumination conditions.     

Hidden Markov model-based ensemble methods for offline handwritten text line recognition

This paper investigates various ensemble methods for offline handwritten text line recognition. To obtain ensembles of recognisers, we implement bagging, random feature subspace, and language model variation methods. For the combination, the word sequences returned by the individual ensemble members are first aligned. Then a confidence-based voting strategy determines the final word sequence. A number of confidence measures based on normalised likelihoods and alternative candidates are evaluated. Experiments show that the proposed ensemble methods can improve the recognition accuracy over an optimised single reference recogniser.     

上下文感知服务器中基于预测的负载均衡算法

在上下文感知服务器中,需要收集大量的上下文信息并对其进行处理。必须通过服务器集群并采用负载均衡技术才能保证服务器的正常运行。该文提出基于预测负载进行自适应负载均衡的算法,采用支持向量机预测系统运行下一个阶段的负载量,结合当前系统各节点处理情况进行动态负载分配过程,在任务执行过程中,不进行任务的迁移以减少迁移所带来的系统消耗。通过比较表明,在负载变化较大的情况下,算法能够有效地提高服务器性能。     

维吾尔语词首音节元音声学分析

该文利用“维吾尔语语音声学参数数据库”,统计分析和归纳了维吾尔语词首音节元音的共振峰模式及其分布格局。声学元音图有多种画法,该文采用JOOS型声学元音图。这种元音图的特点是以F1为纵坐标,以F2为横坐标。这样绘制成的声学元音图与元音舌位图有很好的对应性。维语标准音词首音节中有[y, i, e, O, u, o, ;, A]等8个元音。舌位前后的分布特点是[u, o, A]为后元音,[y, i, e, O, ;]为前元音;开口度(舌位高低)分布特点是[y, i, u]为高元音,[e, O, o]为次高元音,[;]为次低元音, [A]为低元音.     

HELOP: Multi-target tracking based on heuristic empirical learning algorithm and occlusion processing

Multi-target tracking is one of the important fields in computer vision, which aims to solve the problem of matching and correlating targets between adjacent frames. In this paper, we propose a fine-grained track recoverable (FGTR) matching strategy and a heuristic empirical learning (HEL) algorithm. The FGTR matching strategy divides the detected targets into two different sets according to the distance between them, and adopts different matching strategies respectively, in order to reduce false matching, we evaluated the trust degree of the target’s appearance feature information and location feature information, adjusted the proportion of the two reasonably, and improved the accuracy of target matching. In order to solve the problem of trajectory drift caused by the cumulative increase of Kalman filter error during the occlusion process, the HEL algorithm predicts the position information of the target in the next few frames based on the effective information of other previous target trajectories and the motion characteristics of related targets. Make the predicted trajectory closer to the real trajectory. Our proposed method is tested on MOT16 and MOT17, and the experimental results verify the effectiveness of each module, which can effectively solve the occlusion problem and make the tracking more accurate and stable.     

A blockchain-based framework for data quality in edge-computing-enabled crowdsensing

With the rapid development of mobile technology and smart devices, crowdsensing has shown its large potential to collect massive data. Considering the limitation of calculation power, edge computing is introduced to release unnecessary data transmission. In edge-computing-enabled crowdsensing, massive data is required to be preliminary processed by edge computing devices (ECDs). Compared with the traditional central platform, these ECDs are limited by their own capability so they may only obtain part of relative factors and they can’t process data synthetically. ECDs involved in one task are required to cooperate to process the task data. The privacy of participants is important in crowdsensing, so blockchain is used due to its decentralization and tamper-resistance. In crowdsensing tasks, it is usually difficult to obtain the assessment criteria in advance so reinforcement learning is introduced. As mentioned before, ECDs can’t process task data comprehensively and they are required to cooperate quality assessment. Therefore, a blockchain-based framework for data quality in edge-computing-enabled crowdsensing (BFEC) is proposed in this paper. DPoR (Delegated Proof of Reputation), which is proposed in our previous work, is improved to be suitable in BFEC. Iteratively, the final result is calculated without revealing the privacy of participants. Experiments on the open datasets Adult, Blog, and Wine Quality show that our new framework outperforms existing methods in executing sensing tasks.     

Extending $$\tau $$ τ -Lop to model MPI blocking primitives on shared memory

The Journal of Supercomputing - MPI communication optimization is essential for high-performance applications. The communication performance models have made some achievements in improving the...     

计算机网络技术与应用课程改革方案

针对非计算机专业的普通高校学生开设计算机网络技术与应用课程的改革与实践,提出有别于计算机专业的计算机网络课程的教学重点——以企业信息基础为核心构建知识体系结构。     

Fast feature-based mode decision for 4×4 intra prediction in H.264/AVC

Intra coding in H.264/AVC can significantly improve the compression efficiency at the cost of high computational complexity due to the use of the rate-distortion optimization technique.To reduce the complexity of intra prediction,we propose a feature-based mode decision algorithm for 4×4 intra prediction.This algorithm is motivated by the fact that a good prediction mode usually has a small residue block,which is the difference between the original block and predicted block.The sum of the absolute transformed coefficients and deviation information of residue block are used to measure the distortion of prediction mode and the smoothness of residue block,respectively.According to the ranking of these two features of all possible modes,and together with the most probable mode,small numbers of candidate modes can be determined.Moreover,the candidate modes are further reduced to only one by using two early termination rules based on the correlation of rank and most probable mode.Simulation results demonstrate that the proposed mode decision method reduces about 60% of total encoding time of intra coding with negligible loss of coding performance.