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Jeng-Shyang Pan

Bio: Jeng-Shyang Pan is an academic researcher from Shandong University of Science and Technology. The author has contributed to research in topics: Digital watermarking & Watermark. The author has an hindex of 50, co-authored 789 publications receiving 11645 citations. Previous affiliations of Jeng-Shyang Pan include National Kaohsiung Normal University & Technical University of Ostrava.


Papers
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Proceedings ArticleDOI
25 Aug 2012
TL;DR: The proposed basic strategy is to divide such an image into many sub images firstly and then detect the blurred sub image by the gradient distribution and the maximum of cepstrum.
Abstract: This paper presents a real-time restoration method for linear local motion-blur image. for an image in which only the fast moving-object is blurred but the background is clear, the proposed basic strategy is to divide such an image into many sub images firstly and then detect the blurred sub image by the gradient distribution and the maximum of cepstrum. for a blurred sub image, the blur direction and blur length are estimated to calculate the parameters of point spread function (PSF) and Lucy-Richardson deconvolution algorithm is employed to restore this blurred sub image. Using many artificial and real blurred images, the experimental results show that the proposed approach is more accurate and robust than other methods.

4 citations

Proceedings ArticleDOI
16 Oct 2013
TL;DR: This method can reduce the computational complexity for feature extraction using nearest feature line and compressive sensing and its average recognition rate is very close to that of NDNFLA.
Abstract: -In this paper, a novel feature extraction algorithmbased on nearest feature line and compressive sensing is proposed.The prototype samples are transformed to compressivesensing domain and then are performed Neighborhood discriminantnearest feature line analysis (NDNFLA) in the proposedalgorithm. This method can reduce the computational complexityfor feature extraction using nearest feature line. At the same time.its average recognition rate is very close to that of NDNFLA. Theproposed algorithm is applied to image classification on AR faceDatabase. The experimental results demonstrate the effectivenessof the proposed algorithm

4 citations

Book ChapterDOI
TL;DR: Wang et al. as mentioned in this paper proposed a new swarm intelligence optimization algorithm named Tumbleweed Algorithm (TA) which simulates the two processes of tumbleweed from seedling to adulthood and the propagation of tumble weed seeds after adulthood.
Abstract: In this paper, a new swarm intelligence optimization algorithm named Tumbleweed Algorithm (TA) is proposed. The TA algorithm simulates the two processes of tumbleweed from seedling to adulthood and the propagation of tumbleweed seeds after adulthood. And by introducing the concept of growth cycle, the two stages are combined. In order to verify the effectiveness of the new algorithm proposed to solve the problems, this paper uses the CEC2013 function set to test, and compares the 10D, 30D and 50D dimensions with six swarm intelligence optimization algorithms. By comparing the experimental results under different dimensions, the TA algorithm proposed in this paper is generally superior to other intelligent optimization algorithms compared, and has strong optimization ability and competitiveness. Finally, the TA algorithm is applied to the location problem of logistics distribution center to verify the practicability of the algorithm. In solving this problem, the TA algorithm can also obtain better optimization results.

4 citations

Proceedings Article
01 Jan 1998
TL;DR: VQ is a widely used technique for datacompression and the index allocation algorithm proposed by Wu and Barba is the fastest method but the channel distortion is the worst one.
Abstract: Vector quantization is a popular technique in low bit rate codingof speech signal. The transmission index of the codevector ishighly sensitive to channel noise. The channel distortion canbe reduced by organizing the codevector indices suitably.Several index assignment algorithms are studied comparatively.Among them, the index allocation algorithm proposed by Wuand Barba is the fastest method but the channel distortion is theworst one. The proposed parallel tabu search algorithm reachthe best performance of channel distortion. 1.INTRODUCTION Vector quantization (VQ) [1] is a widely used technique for datacompression. The binary indices of the optimally chosencodevectors are sent to the destination. A vectorXxx x={, , , } 12  k consisting of k samples of informationsource in the k-dimensional Euclidean space R k is sent to thevector quantizer. The k-dimensional vector quantizer with thenumber of codevectors N is defined as follows by using thereproduction alphabet consisting of N codevectors,Ccc c={,,, }

3 citations

Book ChapterDOI
01 Jan 2014
TL;DR: The quantification-based ACS (QACS) is proposed to reduce the space complexity and the convergence rate can be improved with less memory space for solving Traveling Salesman Problem (TSP).
Abstract: Ant Colony Optimization (ACO) is one of the swarm intelligent methods for solving computational problems, especially in finding the optimal paths through graphs In the past, floating point is widely used to represent the pheromone in ACO, thus requiring large amounts of memory to find the optimal solutions In this paper, the quantification-based ACS (QACS) is thus proposed to reduce the space complexity New updating rules of pheromone with no decay parameters are also designed in the proposed QACS for simplifying the updating processing of pheromone Based on the experimental results of proposed QACS, the convergence rate can be improved with less memory space for solving Traveling Salesman Problem (TSP)

3 citations


Cited by
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Journal ArticleDOI
TL;DR: It is proved the convergence of a recursive mean shift procedure to the nearest stationary point of the underlying density function and, thus, its utility in detecting the modes of the density.
Abstract: A general non-parametric technique is proposed for the analysis of a complex multimodal feature space and to delineate arbitrarily shaped clusters in it. The basic computational module of the technique is an old pattern recognition procedure: the mean shift. For discrete data, we prove the convergence of a recursive mean shift procedure to the nearest stationary point of the underlying density function and, thus, its utility in detecting the modes of the density. The relation of the mean shift procedure to the Nadaraya-Watson estimator from kernel regression and the robust M-estimators; of location is also established. Algorithms for two low-level vision tasks discontinuity-preserving smoothing and image segmentation - are described as applications. In these algorithms, the only user-set parameter is the resolution of the analysis, and either gray-level or color images are accepted as input. Extensive experimental results illustrate their excellent performance.

11,727 citations

Book
24 Oct 2001
TL;DR: Digital Watermarking covers the crucial research findings in the field and explains the principles underlying digital watermarking technologies, describes the requirements that have given rise to them, and discusses the diverse ends to which these technologies are being applied.
Abstract: Digital watermarking is a key ingredient to copyright protection. It provides a solution to illegal copying of digital material and has many other useful applications such as broadcast monitoring and the recording of electronic transactions. Now, for the first time, there is a book that focuses exclusively on this exciting technology. Digital Watermarking covers the crucial research findings in the field: it explains the principles underlying digital watermarking technologies, describes the requirements that have given rise to them, and discusses the diverse ends to which these technologies are being applied. As a result, additional groundwork is laid for future developments in this field, helping the reader understand and anticipate new approaches and applications.

2,849 citations

Proceedings Article
01 Jan 1999

2,010 citations

Posted Content
TL;DR: This paper defines and explores proofs of retrievability (PORs), a POR scheme that enables an archive or back-up service to produce a concise proof that a user can retrieve a target file F, that is, that the archive retains and reliably transmits file data sufficient for the user to recover F in its entirety.
Abstract: In this paper, we define and explore proofs of retrievability (PORs). A POR scheme enables an archive or back-up service (prover) to produce a concise proof that a user (verifier) can retrieve a target file F, that is, that the archive retains and reliably transmits file data sufficient for the user to recover F in its entirety.A POR may be viewed as a kind of cryptographic proof of knowledge (POK), but one specially designed to handle a large file (or bitstring) F. We explore POR protocols here in which the communication costs, number of memory accesses for the prover, and storage requirements of the user (verifier) are small parameters essentially independent of the length of F. In addition to proposing new, practical POR constructions, we explore implementation considerations and optimizations that bear on previously explored, related schemes.In a POR, unlike a POK, neither the prover nor the verifier need actually have knowledge of F. PORs give rise to a new and unusual security definition whose formulation is another contribution of our work.We view PORs as an important tool for semi-trusted online archives. Existing cryptographic techniques help users ensure the privacy and integrity of files they retrieve. It is also natural, however, for users to want to verify that archives do not delete or modify files prior to retrieval. The goal of a POR is to accomplish these checks without users having to download the files themselves. A POR can also provide quality-of-service guarantees, i.e., show that a file is retrievable within a certain time bound.

1,783 citations