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

The vast majority of researches about the shortest path algorithm, nowadays, focus just on the case starting from the beginning point and ending at the ending point. If additional condition that the shortest path must go through some given nodes of which number is uncertain must be met, then most of the existing classic algorithms are not applicable. A general method based on the Virus Co-Evolutionary Genetic algorithm and greedy algorithm is presented to solve this kind of problem. The time complexity of the algorithm is given by theoretical analysis and the effectiveness of the algorithm is verified by programming calculation. Furthermore, this algorithm in HUAWEI Code Craft 2016 makes 17th score in preliminary to the semi-finals.

The middle of the specified node set of shortest path algorithm

A rapidly growing social interaction technology is the use of Quick Response (QR) codes as physical entrances (ie the encoded URL address) to Internet resources However, there exist various kind of security problems, for example, the URL may point to some phishing web sites A digital certificate based signature and verification QR code is proposed in this paper The public and private keys are generated by the authentication mechanism, which using the Public Key Infrastructure (PKI) technology The public key can be published with the designed scanning application (App) An authenticated QR code can be created based on the private key and the merchant information Clients can authenticate the codes by the public key and scanning App Test results prove that the security QR code designed by this system, which binds the digital certificate, can be verified by the decoding scanner

Digital Certificate Based Security Payment for QR Code Applications

The sparse feature sphere centroid (SFSC) classifier for face recognition is proposed. SFSC is based on nearest feature plane (NFP), sparse representation classification (SRC) and nearest feature centres (NFC), and it contains two stages. In the first stage, the SFSC classifier computes the feature sphere centroid metric. Then, SFSC obtains the sparse coefficients by solving an L 1-norm minimisation problem and uses the sparse coefficients to calculate the weighted feature sphere centroid distance, which will be utilised for classification. Experiments on the Georgia Tech (GT) face database and AR face database were conducted to evaluate the proposed classifier. The experimental results show that the proposed classifier yields better recognition rate over competing classifiers such as NFC, NFP and SRC.

http://ieeexplore.ieee.org/iel7/2220/6880194/06880209.pdf

Face recognition using sparse feature sphere centroid classifier

A novel perfect contrast XOR-based visual cryptography scheme for multiple secrets

A new algorithm (called CPMPSO for short), in which PSO with constraint-preserving mechanism is used as a global search algorithm and PSO itself is used as local search one, is proposed in this paper to solve mixed-variable optimization problems. The values of non-continuous variables are got according to the velocity of the particle, the constraint-preserving method is used as the mechanism for handling the constraint violations, and the particle swarm optimization itself is used as local search algorithm to obtain the consistent optimal results for mixed-variable optimization problems. The performance of CPMPSO is evaluated against two real-world mixed-variable optimization problems, and it is found to be highly competitive compared with other existing algorithms.

Jeng-Shyang Pan

Papers

The middle of the specified node set of shortest path algorithm

Digital Certificate Based Security Payment for QR Code Applications

Face recognition using sparse feature sphere centroid classifier

A novel perfect contrast XOR-based visual cryptography scheme for multiple secrets

PSO with Constraint-Preserving Mechanism for Mixed-Variable Optimization Problems