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

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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 K-Nearest Neighbor algorithm is one of the commonly used methods for classification in machine learning and computational intelligence. A new research method and its improvement for the sleepy lizards based on the K-Nearest Neighbor algorithm and the traditional social network algorithms are proposed in this chapter. The famous paired living habit of sleepy lizards is verified based on our proposed algorithm. In addition, some common population characteristics of the lizards are also introduced by using the traditional social net work algorithms. Good performance of the experimental results shows efficiency of the new research method.

A Novel Approach on Behavior of Sleepy Lizards Based on K-Nearest Neighbor Algorithm

In this paper, we have proposed a novel evolutionary random interval fingerprint for active RFID and ZigBee systems. This new approach can enable more secure communication in a multi-party communication, if the packet is forged by another communication party, the interval fingerprint can provide another way to detect the spoofing packet. Moreover, the evolutionary random algorithms - genetic and memetic random algorithms are also proposed to generate the random interval fingerprint. Comparing with the conventional random generator, our approach is flexible to generate uniform random numbers, and more robust for the cracking. The forged party is difficult to produce the fake random intervals. Finally, we provide an application example, completed related work survey, pseudo-code and analysis result to prove our concept is feasible for the pervasive communication.

Evolutionary Interval Fingerprint for Wireless Network

The smart home is a crucial embodiment of the internet of things (IoT), which can facilitate users to access smart home services anytime and anywhere. Due to the limited resources of cloud computing, it cannot meet users’ real-time needs. Therefore, edge computing emerges as the times require, providing users with better real-time access and storage. The application of edge computing in the smart home environment can enable users to enjoy smart home services. However, users and smart devices communicate through public channels, and malicious attackers may intercept information transmitted through public channels, resulting in user privacy disclosure. Therefore, it is a critical issue to protect the secure communication between users and smart devices in the smart home environment. Furthermore, authentication protocols in smart home environments also have some security challenges. In this paper, we propose an anonymous authentication protocol that applies edge computing to the smart home environment to protect communication security between entities. To protect the security of smart devices, we embed physical unclonable functions (PUF) into each smart device. Real-or-random model, informal security analysis, and ProVerif are adopted to verify the security of our protocol. Finally, we compare our protocol with existing protocols regarding security and performance. The comparison results demonstrate that our protocol has higher security and slightly better performance.

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Toward Smart Home Authentication Using PUF and Edge-Computing Paradigm

A novel watermarking scheme is proposed for up-sampling based multiple description coding frame in this paper. Secret information is embedded in the DWT image. Up-sampling algorithm is applied on transformed image to introduce some redundancy between different channels. Good performance of the new frame to against noise and to resist the compression attacks is shown in the experimental results.

Novel Up-Sampling Based Watermarking Multiple Description Coding Frame

In traditional prediction error expansion (PEE) based reversible watermarking (RW), the watermark bits are embedded into the two peaks of the global prediction error histogram. This scheme ignores some bins in local histogram. To improve the utilization of prediction error, a method based on locally adaptive PEE is proposed. The original image is divided into two regions. In the first region, the image is divided into several blocks and the local prediction error histogram of each block is obtained after checkerboard prediction. Then, the two peaks in the local histogram of each block are used for watermark embedding. In the second region, the least significant bit (LSB) replacement is used to embed the auxiliary information and compressed positioning map. It is verified that, under the same image distortion, this method provides higher embedding capacity.

Jeng-Shyang Pan

Papers

A Novel Approach on Behavior of Sleepy Lizards Based on K-Nearest Neighbor Algorithm

Evolutionary Interval Fingerprint for Wireless Network

Toward Smart Home Authentication Using PUF and Edge-Computing Paradigm

Novel Up-Sampling Based Watermarking Multiple Description Coding Frame

Reversible Watermarking Based on Adaptive Prediction Error Expansion.