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	1. A Light Eyetracking System for Webpage Designing
	Chen Daiwu,Zhang Honggang,Guo Jie,Zhang Nannan
	Computer Science and Technology 16 September 2014
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	Abstract:Eye-tracking technology has always being considered as one of the most important methods in various fields concerning human's behaviour and cognition. This paper targets on designing and implementing a cheap real-time eye-tracking system mainly depend on a web-camera and software processing for rising need of webpage designing. We will introduce new tracking pattern, new eye features sets and SVM(Support Vector Machine)in this system. And experiments are taken based on proposed method with satisfied precision.
	TO cite this article:Chen Daiwu,Zhang Honggang,Guo Jie, et al. A Light Eyetracking System for Webpage Designing[OL].[16 September 2014] http://en.paper.edu.cn/en_releasepaper/content/4610154


	2. Fuzzy Decision Based Algorithm for Classifying Incomplete Data
	Fudong Nian,Jun Wu,Teng Li,Feifeng Li
	Computer Science and Technology 15 September 2014
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	Abstract:Classification is a very important research topic. But in real world application the incomplete data usually exist. The incompleteness of data degrades the models learning quality in classification. The classification problem can be separated into two phases: learning phase and classification phase. Most previous methods dealing with incomplete data only focus on handling incomplete data in the learning phase. For the incomplete value appearing in the classification phase, most of the current approaches cannot work or perform badly. In this paper a novel classifier is proposed to solve the incomplete data classification problem. In contrast to the conventional boosting algorithm which uses a deterministic decision method during the iterations, without considering the noise in the data set sufficiently, we propose a new boosting algorithm using fuzzy decisions for every hypothesis at the iterations of the boosting scheme. It selects the data events from a dataset, and then combines them. The experimental results demonstrate the superories of the proposed strategies for solving incomplete data problem.
	TO cite this article:Fudong Nian,Jun Wu,Teng Li, et al. Fuzzy Decision Based Algorithm for Classifying Incomplete Data[OL].[15 September 2014] http://en.paper.edu.cn/en_releasepaper/content/4609074

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	3. Player Identification Based on Jersey Number Recognition In Sports Video
	Zhang Nannan,Zhang Honggang,Li Siyuan,Guo Jie
	Computer Science and Technology 09 September 2014
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	Abstract:Detecting the identity of the players would be highly valuable for sports video content analysis. Since a player's jersey number is unique during a game, it is feasible to recognize the jersey number for identification in sports videos. Jersey number is considered as scene text which is difficult to localize and recognize. To solve this problem, we present a method for jersey region localization using a mixed color model as the rough detector and a SVM classifier as the refined detector. Once the jersey region is determined, a jersey number recognition algorithm with a KNN classifier is applied to extract and recognize the number. In addition, we develop an interactive system, which users can interact with to improve the recognition rate. Experimental results are presented on various kinds of sports videos, such as football and basketball videos, and the detection rate is over 85% and recognition rate is over 80% even under the condition of complicated background.
	TO cite this article:Zhang Nannan,Zhang Honggang,Li Siyuan, et al. Player Identification Based on Jersey Number Recognition In Sports Video[OL].[ 9 September 2014] http://en.paper.edu.cn/en_releasepaper/content/4606751


	4. Optimizing Single-Trial EEG Classi?cation by Stationary Matrix Logistic Regression in Brain-Computer Interface
	ZENG Hong,SONG Ai Guo
	Computer Science and Technology 29 August 2014
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	Abstract:In addition to the noisy and limited spatial resolution characteristics of the EEG signal, the intrinsic non-stationarity in the EEG data makes the single-trial EEG classi?cation an even more challenging problem in brain-computer interface (BCI). Variations of the signal properties within a session often result in deteriorated classi?cation performance. This is mainly attributed to the reason that the routine feature extraction or classi?cation method does not take the changes in the signal into account. Although several extensions to the standard feature extraction method have been proposed to reduce the sensitivity to non-stationarity in data, they optimizes different objective functions from that of the subsequent classi?cation model, thereby the extracted features may not be optimized for the classi?cation. In this paper, we propose an approach that directly optimizes the classi?er's discriminativity and robustness against non-stationarity in the EEG data with a single optimization paradigm, and show that it can greatly improve the performance, in particular for the subjects who have dif?culty in controlling a BCI. Moreover, the experimental results on two benchmark data sets demonstrate that our approach signi?cantly outperforms the state-of-the-art approaches in reducing classi?cation error rates.
	TO cite this article:ZENG Hong,SONG Ai Guo. Optimizing Single-Trial EEG Classi?cation by Stationary Matrix Logistic Regression in Brain-Computer Interface[OL].[29 August 2014] http://en.paper.edu.cn/en_releasepaper/content/4607831


	5. An Improved Face Detection System Based On DPM-liked Framework
	Jianjie Yang,Yuan Dong
	Computer Science and Technology 23 May 2014
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	Abstract:In this paper, we present an advanced approach to improving the performance of face detection system. While the traditional voila-Jones detector can achieve relative high accuracy, it also generates lots of false negative results in some difficult datasets especially in wild-field. To boost the recall of face detection system, we used a two-step approach. First, we make use of classical ad-boost based system to acquire coarse results; then, filter out some false negatives by the DPM-liked system which consists of some part models for face landmarks and shape model to capture the property of deformable. To train the DPM-liked models, we used a technology named data-mining to increase the efficient of training. To speed up the detection, we adopted a dynamic programming method. We show excellent performance on a dataset we gathered by ourselves and also achieve state-of-the-art performance on the less challenging BioID dataset.
	TO cite this article:Jianjie Yang,Yuan Dong. An Improved Face Detection System Based On DPM-liked Framework[OL].[23 May 2014] http://en.paper.edu.cn/en_releasepaper/content/4597127


	6. Sparse representation based on manifold learning
	Yang Zheng, Liu Haifeng
	Computer Science and Technology 17 December 2013
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	Abstract:As a technology derived from the Human Visual System, sparse coding has attracted a lot of attentions in recent years. It aims to learn sparse coordinates in terms of the basis set, which is given directly or learned from the original data set. Because of the sparsity, the learned sparse representation can be used in further data processing( such as clustering and classifying) efficiently. But the canonical sparse coding methods are all ignored the intrinsic structure of the data. From the perspective of manifold learning, this paper propose a novel sparse coding method, called Sparse Coding based on Manifold learning (MSC). Inspired by LPP, MSC finds a basis set which can be used to represent the intrinsic manifold space of the data set, and then sparse representations will be learned in this space. The most obvious advantage of MSC compared with the algorithms which impose a manifold regularizer to the objective function directly is that MSC is nonparametric. In other words, MSC is more robust. A set of evaluations on real world applications demonstrate the effectiveness of this novel algorithm.
	TO cite this article:Yang Zheng, Liu Haifeng. Sparse representation based on manifold learning[OL].[17 December 2013] http://en.paper.edu.cn/en_releasepaper/content/4575298


	7. A Study of Kernel Select for the Relevance Vector Machine
	PENG Yang,LI Dehua
	Computer Science and Technology 13 December 2013
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	Abstract:In this paper a method of the kernel select for the relevance vector machine is proposed. Former researches of RVM usually focus on the advantages of the algorithm in sparsely for less opportunities of over learning, how to accelerate the algorithm in order for needs such as large scale sets, and how to improve the precision of the algorithm by the combination of other algorithms such as PCA or SVM. The parameters of the kernel functions are regarded more important than these functions themselves, so that how to select the appropriate kernel and its parameters is confused for the application of RVM. In this paper, a method for kernel select for the RVM is proposed. Firstly, by simulation experiment, the relationship between σ in Gaussian kernel function and the regression result is discovered. Secondly, by analysis the reason of the experimental result, a common principle for kernel select is presented. Finally, on the principle, a composite kernel and the relation between different parameters and the number of RVs and the error rate in regression are tested by simulation experiment.
	TO cite this article:PENG Yang,LI Dehua. A Study of Kernel Select for the Relevance Vector Machine[OL].[13 December 2013] http://en.paper.edu.cn/en_releasepaper/content/4574392


	8. Band-reweighed Gabor Kernel Embedding for Face Recognition
	Chuan-Xian Ren, Dao-Qing Dai
	Computer Science and Technology 07 December 2013
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	Abstract:Face recognition with illumination or pose variation is a challenging problem in image processing and pattern recognition. A novel algorithm using band-reweighed Gabor kernel embedding to deal with the problem is proposed in this paper. For a given image, it is firstly transformed by a group of Gabor filters, which output Gabor features using different orientation and scale parameters. Fisher scoring function is used to measure the importance of features in each band, and then the features with the largest scores are preserved for saving memory requirement. The reduced bands are combined by a vector, which is determined by a weighted kernel discriminant criterion and solved by a constrained quadratic programming method, and then the weighted sum of these nonlinear bands is defined as the similarity between two images. Compared with existing concatenation based Gabor feature representation and the uniformly weighted similarity calculation approaches, our method provides a new way to use Gabor features for face recognition, and presents a reasonable interpretation for highlighting discriminant orientations and scales. The minimum Mahalanobis distance considering the spatial correlations within the data is exploited for feature matching, and the graphical lasso is used therein for directly estimating the sparse inverse covariance matrix. Experiments using benchmark databases show that our new algorithm improves the recognition results and obtains competitive performance.
	TO cite this article:Chuan-Xian Ren, Dao-Qing Dai. Band-reweighed Gabor Kernel Embedding for Face Recognition[OL].[ 7 December 2013] http://en.paper.edu.cn/en_releasepaper/content/4571407


	9. A Mixed Integer Programming Approach for Gene Selection
	SHAO Lizhen,WANG Jieli,HU Guangda,LIU Jiwei
	Computer Science and Technology 09 September 2013
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	Abstract:%It is known that for most of gene expression data for cancer classification, the number of samples is quite small compared to the number of genes. Therefore, feature selection is an essential pre-processing step and a challenging problem to remove the irrelevant or redundant genes before classification.In this paper, we model the gene selection problem as a mixed integer programming problem based on 1-norm support vector machine (SVM). This problem is difficult to solve because the number of integer variables (usually tens of thousands or even hundreds of thousands) is very big compared to the desired number of genes. To solve this problem, we propose an iterative mixed integer optimization algorithm to gradually select a subset of genes. We test the proposed approach on colon cancer and leukemia cancer gene expression datasets. The results show that our proposed algorithm performs better than fisher criterion, T-statistics, standard 2-norm SVM and SVM recursive feature elimination (SVM-RFE) methods. The selected gene subset has better classification accuracy.
	TO cite this article:SHAO Lizhen,WANG Jieli,HU Guangda, et al. A Mixed Integer Programming Approach for Gene Selection[OL].[ 9 September 2013] http://en.paper.edu.cn/en_releasepaper/content/4559213


	10. A Dynamic Gesture Recognition Method Based on Computer Vision
	Xiao Jiang,Xiaobo Lu
	Computer Science and Technology 17 August 2013
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	Abstract:In recent years, the development of human-computer interaction (HCI) techniques is very fast and the typical application is the gesture interaction technology. This paper proposes a useful dynamic gesture recognition method which is robust to different background. This method consists of four parts: skin segmentation, hand feature extraction, hand tracking, trajectory rec-ognition. Hand region is segmented based on YCrCb color space. A new algo-rithm is proposed to extract hand feature by detecting the number of fingers. The hand tracking algorithm is based on ellipse fitting and motion feature. Some special tracking rules are designed to solve the problem of overlapping. A simple method is proposed to recognize the trajectory of hand waving obtained after tracking. The experimental results show that the proposed method is reli-able and robust.
	TO cite this article:Xiao Jiang,Xiaobo Lu. A Dynamic Gesture Recognition Method Based on Computer Vision[OL].[17 August 2013] http://en.paper.edu.cn/en_releasepaper/content/4555393

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