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

A parallel compact Differential Evolution (pcDE) algorithm is proposed in this paper. The population is separated into multiple groups and the individual is run by using the method of compact Differential Evolution. The communication is implemented after predefined iterations. Two communication strategies are proposed in this paper. The first one is to replace the local optimal solution by global optimal solution in all groups, which is called optimal elite strategy (oe); the second one is to replace the local optimal solution by mean value of the local optimal solution in all groups, which is called mean elite strategy (me). Considering that the pcDE algorithm does not need to store a large number of solutions, the algorithm can adapt to the environment with weak computing power. In order to prove the feasibility of pcDE, several groups of comparative experiments are carried out. Simulation results based on the 25 test functions demonstrate the efficacy of the proposed two communication strategies for the pcDE. Finally, the proposed pcDE is applied to image segmentation and experimental results also demonstrate the superior quality of the pcDE compared with some existing methods.

Parallel compact differential evolution for optimization applied to image segmentation

A novel reversible data hiding scheme based on the companding technique and an improved difference expansion (DE) method is presented in this article. The companding technique is employed to embed payload into high frequency bands in the integer wavelet transform (IWT) domain. A refined method based on histogram modification is proposed as a remedy to the potential problem on overflow/underflow in pixel values after reverse IWT. To be truly reversible, additional information incurred by the histogram modification process is ingeniously embedded into the carrier image by using an improved DE method. A series of experiments is conducted to verify the feasibility and effectiveness of the proposed approach. As revealed in the experimental results, the proposed scheme outperforms existing approaches, in terms of embedding capacity and visual quality, as expected.

Reversible Data Hiding Using the Companding Technique and Improved DE Method

Advances in Intelligent Information Hiding and Multimedia Signal Processing

This paper applies an improved Cuckoo Search algorithm, named Chaotic CS algorithm, to solve Unmanned Combat Aerial Vehicle (UCAV) path planning problems. A circle-type chaotic map for generating chaotic sequences is used to specify the scaling factor (() of step size and fraction probability (pa) of abandonment for host nests formulated in the Original CS algorithm. The advantage of using Chaotic CS algorithm can dynamically change the parameters of ( and pa by using the chaotic sequences over the course of iterations, resulting in an improvement for searching performance to find out the global best solution. The enhanced CS algorithm shows flexible and robust capabilities to optimize complex and multimodal objective functions by evaluating standard benchmark functions. Furthermore, the Chaotic CS algorithm is applied to solve complex design problem. Two scenarios of UCAV path planning problems are carried out for the practical applications. The simulation results indicate that the Chaotic CS algorithm can efficiently be used for computing optimal flight path of UCAV.

Chaotic Cuckoo Search Algorithm for Solving Unmanned Combat Aerial Vehicle Path Planning Problems

A novel classifier based on feature line, called the nearest feature centre (NFC) classifier, is proposed for face recognition NFC uses the new metric, called the feature centre metric, which is different from the classical feature line metric of the nearest feature line (NFL) The experimental results on the ORL face database show that NFC outperforms NFL and the other classifiers based on feature line

Jeng-Shyang Pan

Papers

Parallel compact differential evolution for optimization applied to image segmentation

Reversible Data Hiding Using the Companding Technique and Improved DE Method

Advances in Intelligent Information Hiding and Multimedia Signal Processing

Chaotic Cuckoo Search Algorithm for Solving Unmanned Combat Aerial Vehicle Path Planning Problems

Nearest feature centre classifier for face recognition