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Search Results: 1 - 10 of 8556 matches for " Jieqing Tan "
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A Novel Model of Image Segmentation Based on Watershed Algorithm
Ali Abdullah Yahya,Jieqing Tan,Min Hu
Advances in Multimedia , 2013, DOI: 10.1155/2013/120798
Abstract: A novel model of image segmentation based on watershed method is proposed in this paper. To prevent the oversegmentation of traditional watershed, our proposed algorithm has five stages. Firstly, the morphological reconstruction is applied to smooth the flat area and preserve the edge of the image. Secondly, multiscale morphological gradient is used to avoid the thickening and merging of the edges. Thirdly, for contrast enhancement, the top/bottom hat transformation is used. Fourthly, the morphological gradient of an image is modified by imposing regional minima at the location of both the internal and the external markers. Finally, a weighted function is used to combine the top/bottom hat transformation algorithm and the markers algorithm to get the new algorithm. The experimental results show the superiority of the new algorithm in terms of suppression over-segmentation. 1. Introduction A segmentation divides an image into its constituent regions or objects, and the segmentation must be stopped when the objects of interest in an application have been isolated [1]. Image segmentation is based on three principal concepts: edge detection, thresholding, and region growing. The most common one is thresholding. Thresholding has a high speed of operation and ease of implementation. However its performance is relatively limited since image pixels with the same gray level value will invariably be segmented into the same class [2]. Segmentation by morphological watersheds [3–10] embodies many of the concepts of the other three approaches, which produces more stable segmentation results, as well as providing simple framework. A simple watershed transformation causes oversegmentation [11]. In order to prevent this oversegmentation, the watershed method passed through several stages of evolution. The original watershed method was developed by Lantuejoul [12] and was widely described together with its applications by Beucher and Meyer [13]. The authors in [3] used FIFO queues to extend the original evolution with gray scale images [11]. Shafarenko et al. [14] applied FIFO to color images. In this paper we enhance the contrast of the gradient image by top/bottom hat transformation, modify the result of the enhancement by imposing regional minima at the locations of both the internal and the external markers, combine the top/bottom hat transformation algorithm and the markers algorithm by using suitable weight function, and subject the combination to the watershed algorithm. The new algorithm has a capability to prevent oversegmentation of the simple watershed
Gender Difference of Unconscious Attentional Bias in High Trait Anxiety Individuals
Jieqing Tan,Zheng Ma,Xiaochao Gao,Yanhong Wu,Fang Fang
PLOS ONE , 2012, DOI: 10.1371/journal.pone.0020305
Abstract: By combining binocular suppression technique and a probe detection paradigm, we investigated attentional bias to invisible stimuli and its gender difference in both high trait anxiety (HTA) and low trait anxiety (LTA) individuals. As an attentional cue, happy or fearful face pictures were presented to HTAs and LTAs for 800 ms either consciously or unconsciously (through binocular suppression). Participants were asked to judge the orientation of a gabor patch following the face pictures. Their performance was used to measure attentional effect induced by the cue. We found gender differences of attentional effect only in the unconscious condition with HTAs. Female HTAs exhibited difficulty in disengaging attention from the location where fearful faces were presented, while male HTAs showed attentional avoidance of it. Our results suggested that the failure to find attentional avoidance of threatening stimuli in many previous studies might be attributed to consciously presented stimuli and data analysis regardless of participants' gender. These findings also contributed to our understanding of gender difference in anxiety disorder.

PENG Kaijun,TAN Jieqing,

系统科学与数学 , 2009,
Abstract: This paper presents the Pad\'{e}-type approximation for pseudo-multivariate functions. This approximation can simplify the calculation of the multivariate rational approximation, and accelerate the convergence of the function at the singular point. The properties and error analysis of the Pad\'{e}-type approximation for pseudo-multivariate functions are given.
Difference of Words Recognition in High and Low Obsessive Compulsive Symptom Individuals

Zhong Jie,Tan Jieqing,Yan Kuanghai,

心理学报 , 2005,
Abstract: The current study aims to compare the word recognition performance and the confidence of the memory in the high and low(obsess)ive compulsive Symptom Subjects(Ss).12 high obsessive compulsive Symptom(HOC) Ss and 12 low obsessive compulsive Symptom(LOC) Ss responded to a words recognition task,which totally has 60 obsessive compulsive sensitive words(OCSW),60 emotional sensitive words(ESW) and 60 neutral words(NW).The results indicate that,compared to LOC group,HOC group have relatively poorer memory on OCSW(t(22)=-2.161,p=0.042),because they judged more new words as learned(t(22)=1.832,p=0.081).Particularly on the basis of signal detection analysis for the neutral words,the HOC Ss tend to have the lower d' than the LOC Ss(t(22)=-2.037,p=0.054).And the(interaction) of memory confidence between the groups and words types was significant(F(1,22)=4.60,p<0.05).The results of the present study implicate that the disability to determine confound information is one of the causes for lower confidence of HOC Ss,but this relationship merits further study.
Adaptive efficient non-local image filtering

Xu Guangyu,Tan Jieqing,Zhong Jinqin,

中国图象图形学报 , 2012,
Abstract: Non-local Means Filtering (NLMF) has been a popular issue in the image filtering field.The existing NLMFs based pre-selections are analyzed,and it is pointed out that they all have deficiencies in terms of feature extraction from image patches.An adaptive and effeicient NLMF method is proposed using singular value decomposition (SVD) in the gradient domain.Our contributions to NLMF based pre-selection are:1)the robust pre-selection method based structure feature from image patch;2)the relation between size of the similar sets and filtering performance is analyzed;3)automatic selection of similar patches;4)local adaptive selection of the filtering parameter.In addition,the symmetry of the Euclidean distance is considered to accelerate the proposed method further.The experimental results show that the proposed method outperforms the original NLMF and other fast NLMFs on subjective and objective aspects,and has rapid running speed.The proposed method is an efficient filtering method.
Wavelet Based Image De-noising to Enhance the Face Recognition Rate
Isra'a Abdul-Ameer Abdul-Jabbad,Jieqing Tan,Zhengfeng Hou
International Journal of Computer Science Issues , 2013,
Abstract: In this paper a comparison between face recognition rate with noise and face recognition rate without noise is presented. In our work we assume that all the images in the ORL faces database are noisy images. We applied the wavelet based image de-noising methods to this database and created new databases, then the face recognition rate are calculated to them. Three experiments are given in our paper. In the first experiment different wavelet methods with different level of decomposition (up to ten decompositions) are used for de-noising the ORL database and the comparison is done when Principal Components Analysis (PCA) is applied to evaluate the verification rate. In the second experiment de-noising different sets of ORL database with methods that have best performance in levels (1, 2, 3, and 10) is done (as a result from experiment 1). In the third experiment we implement the proposed Haar10 method on PCA, Linear Discriminate Analysis (LDA), Kernel PCA, Fisher Analysis (FA) face recognition methods and the recognition rates are evaluated for both the noisy and de-noisy databases.
Directed Forgetting and Metamemory in High Obsessive-Compulsive Symptom Individuals

Tan Jieqing,Huang Rongliang,Hou Congjing,Wu Yanhong,

心理学报 , 2007,
Abstract: 将定向遗忘和FOK判断的范式相结合,探讨高强迫症状被试(HOC)和低强迫症状的控制组(LOC)在不同词语类型的条件下,线索回忆和元记忆判断的定向遗忘效应的差异。实验结果表明,HOC组在中性词语条件下比LOC组表现出更低的定向遗忘效应。FOK的结果表明HOC被试对于不同条件的元记忆的分辨能力比LOC组要差,他们在威胁性词语方面,对未来记忆任务的成绩也没有预测性。研究结果从一定程度上支持强迫症的一般记忆损伤模型。

Xie Jin,Tan Jieqing,Li Shengfeng,Deng Siqing,

计算数学 , 2010,
Abstract: A method of generating quadratic blending spline curves based on weighted trigonometric and hyperbolic polynomials is presented in this paper, which shares many important properties of quadratic non-uniform B-splines. Here weight coefficients are also shape parameters, which are called weight parameters. The interval 0,1] of weight parameter values can be extended to -2.6482 ,3.9412]. Taking different values of the weight parameter, one can not only totally or locally adjust the shape of the curves but also change the type of some segments of a curve among trigonometric or hyperbolic polynomials. Without using multiple knots or solving system of equations and letting one or several weight parameter be -2.6482, the curve can interpolate certain control points or control polygon edge directly. Moreover, it can represent ellipse (circle) and hyperbola exactly.
Design and Implementation of Topic-specific Personal Real-time Search Engine

Liu Jieqing Wu Jinghui,

现代图书情报技术 , 2006,
Abstract: This paper introduces a search engine,which is designed for personal user.By using heuristic real-time search algorithm,it can provide user with the newest topicspecific information.This system can meet user's need,and solve the problem such as topic fixation and data outdating which are ubiquitous phenomena in the general search engine.At the same time it provides theoretic and practical basis for the personalization of search engine.
Copy-and-paste operation of planar polygonal shapes

Yang Wenwu,Feng Jieqing,Huang Shengsheng,Jin Xiaogang,

自然科学进展 , 2007,
Abstract: A 2D polygonal shape copy-and-paste method is proposed which is based on a multiple planar shapes blending algorithm. First sub-shapes are specified and selected, which correspond to user-defined visual features on the input shapes. Then they are copied and pasted with contribution weights to generate new shapes via a modified intrinsic 2D shape blending algorithm. User can edit the generated shape intuitively and interactively by adjusting the contribution weights. The proposed method fills the gap in the object modeling methodology based on the copy-and-paste operation. Besides the static 2D copy-and-paste operation, the proposed method can also be applied to 2D metamorphosis among multiple planar shapes.
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