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

/pdf/correction-to-improved-dv-hop-based-on-parallel-and-compact-3jarxmv7.pdf

Correction to: Improved DV-Hop based on parallel and compact whale optimization algorithm for localization in wireless sensor networks

In this letter, we propose a novel subsampling based image watermark sequentially embedding scheme to reduce the risk of common permutation attack. The image is still perceptual after watermarking, and experimental results also show its effectiveness and robustness.

A More Robust Subsampling-Based Image Watermarking

A novel Bilinear Feature Line Analysis (BFLA) is proposed for image feature extraction in this letter. Neaerest feature line (NFL) is a powerful classifier. Some NFL based subspace algorithms have been introduced recently. In most of the classical NFL-based subspace learning approaches, the input samples are vectors. For face recognition, face samples should be transformed to vectors firstly. This process induces a high computational complexity and also may lead to the loss of the geometric feature of samples. The proposed BFLA is a matrix-based algorithm. It aims to minimize the within class scatter based on two-dimensional NFL. The experimental results on Yale face databases confirm its effectiveness.

Bilinear Feature Line Analysis for Face Recognition

In this paper, a novel classifier based nearest feature line, called as restricted nearest feature line with angle, is proposed. NFL was originally used in face recognition, and later began to be used in many other applications. Though successful in improving the classification ability, there are still some drawbacks in NFL that limit their further application in practice. To improve the miss-classification of nearest feature line when the prototypes in NFL are far away from the query sample, restricted nearest feature line with angle uses the angle between two lines to restrict the feature lines. The two lines are composed by query sample and both prototype samples. A large number of experiments are executed on ORL and AR face database. And detailed comparison result is given.

A Novel Classifier Based on Nearest Feature Line

There have been many papers written about methods used to determine the health of batteries in critical standby applications. In this paper intends to produce a critical on-line analysis of trace resistance method and trace voltage method through the SCADA system application. Yet this management system can be proposed to find the early faults of the batteries. Results indicated that the SCADA system with online battery monitoring systems has been highly willing to manufacture industry in the UPS power management quality.

Jeng-Shyang Pan

Papers

Correction to: Improved DV-Hop based on parallel and compact whale optimization algorithm for localization in wireless sensor networks

A More Robust Subsampling-Based Image Watermarking

Bilinear Feature Line Analysis for Face Recognition

A Novel Classifier Based on Nearest Feature Line

A SCADA system for on-line battery early faults precaution