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.

Finite-time boundedness of Markovain jump nonlinear systems with incomplete information

Abstract: This paper deals with the finite-time H∞ bounded control problem of a class of uncertain Markovian jump nonlinear systems with partially unknown transition probabilities and norm-bounded disturbanc...

Image Watermarking Robust to Print and Generation Copy

Abstract: In general, after a watermarked image is printed, copied several times and scanned, the watermark usually cannot be extracted rightly, as well as distorted by noise. To solve this problem, in this paper, two watermarks were embedded into the image. The first one is a periodic watermark. The second one is the real watermark message. These two watermarks are embedded into the image by modifying the gray value of each pixel. The cover image is not needed when the watermark is detected. Both of these two watermarks are estimated exploiting the properties of HVS (Human Visual System) and extracted through the same key-dependent 2D sequences. Experiments results show that the scheme can resist print, generation copy and scan process.

No-Reference Image Quality Assessment in Spatial Domain

Abstract: With the development of computer vision, there has been an increasing need to develop objective quality measurement techniques that can predict image quality automatically. In this paper, we present a complex No-reference image quality assessment (NR IQA) algorithm, which mainly consists of two steps. The first step uses Gabor filters to obtain the feature images with different frequencies and orientations, so as to extract the energy and entropy features of each sub-image. The second step uses the Linear least squares to obtain the parameters for IQA. We conduct experiments in LIVE IQA Database to verify our method. The experimental results show that the proposed method is much more competitive than other state of the art Full-reference (FR) or NR algorithms.

Mean shift: a robust approach toward feature space analysis

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.

Digital Watermarking

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.

PORs: Proofs of Retrievability for Large Files

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.