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.

Mean shift: a robust approach toward feature space analysis

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.

Digital Watermarking

/pdf/a-break-in-the-clouds-towards-a-cloud-definition-svovsw1r5a.pdf

A Break in the Clouds: Towards a Cloud Definition

A morphable model for the synthesis of 3D faces

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.

PORs: Proofs of Retrievability for Large Files

In this paper, a novel halftoning technique for image is designed with genetic algorithm (GA). It is structured on the error diffusion kernel and the resulted halftone image is compared to the one which is originated from the standard error diffusion kernel with the same method by PSNR (peak signal-to-noise ratio).

A GA-Based Image Halftoning Technique

Polarity inversion based speech watermarking scheme hide data in speech by modification of the speech polarity. This paper build a statistical model of the polarity detection problem, based on this model, the original polarity detection scheme and the optimal detection scheme are analyzed and compared. The theoretical analysis results are validated by Monte Carlo simulation, the optimal polarity detector shows significant performance gain compared with the original polarity detection algorithm.

/pdf/statistical-analysis-of-two-polarity-detection-schemes-in-3i10p92kw1.pdf

Statistical Analysis of Two Polarity Detection Schemes in Speech Watermarking

Contourlet and Nearest Feature Line Based Feature Extraction Approach for One Prototype Sample Problem.

Multi-group Discrete Symbiotic Organisms Search Algorithm Based Clustering Method for Large-Scale Traveling Salesman Problem

Nonlinear information processing algorithms can be designed by means of linear techniques in implicit feature spaces induced by kernel functions Kernel methods are algorithms that, by replacing the inner product with an appropriate positive definite function, implicitly perform a nonlinear mapping of the input data to a high-dimensional feature space

Jeng-Shyang Pan

Papers

A GA-Based Image Halftoning Technique

Statistical Analysis of Two Polarity Detection Schemes in Speech Watermarking

Contourlet and Nearest Feature Line Based Feature Extraction Approach for One Prototype Sample Problem.

Multi-group Discrete Symbiotic Organisms Search Algorithm Based Clustering Method for Large-Scale Traveling Salesman Problem

Kernel Learning Foundation