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

Employing the concept of visual cryptography to design a watermarking scheme is introduced in this chapter. A modified visual cryptography is applied to split the genuine watermark into two shadow watermarks. The gain-shape vector quantisation (GSVQ) procedure is then performed to encode the cover image. Afterwards, the shadow watermarks and the VQ indices obtained are processed to generate two user-keys.

Watermarking with Visual Cryptography and Gain-Shape VQ

We propose an effective scheme for protecting and recovering sensitive information based on reversible data hiding techniques in this paper. It is commonly seen that confidential information within an image is deliberately removed, marked or mosaicked before being delivered to the public, including displaying the marked images on the TV screens or newspapers. However, in some cases such as the plate number of VIP's car, the information need be retained by some means. With our scheme, confidential information in an image can be protected, and then adaptively hidden, based on the characteristics of original image, with reversible data hiding techniques. It is done by first mosaicking the region with confidential information, and then delivering the modified image to the public. Only those who have the right to view the confidential information can recover original image with the secret key. Simulation results demonstrate their applicability and usefulness.

Information Protection and Recovery with Reversible Data Hiding

Modulo operations are often used in reversible watermarking scheme.However, the modulo operations would cause an annoying visual artifact similar to the “salt-and-pepper” noise when pixel value close to the maximally allowed value are flipped to zero and vice versa. To prevent the “salt-and-pepper” noise, the paper presents reversible watermarking scheme using improved patchwork and an improved modulo operation. The improved modulo operation called symmetric modulo operation effectively avoids “salt-and-pepper” noise by the reasonably reducing the flipping distance. Experimental results show that correctly watermark bits can be retrieved by the patchwork algorithm without the original image and that the original image can be restored perfectly while the perceptual degradation of the watermarked signal remained acceptable.

Reversible watermarking based on improved patchwork algorithm and symmetric modulo operation

Data Mining (DM) is a series of processes, which analyses the data and sieves some useful information or interesting knowledge out from real-world large and complex data sets (Ghosh & Jain, 2005). Various statistics, analysis, and modeling methods are employed to find patterns and relationships in DM. The process of Knowledge Discovery from Data (KDD) is the key, which makes the outcome of DM being meaningful. Nevertheless, the fast development of computer science, the database management system (DBMS) and Data Warehouse (DW) pushes the size of the datasets increases forward with an astounding speed. It results in that precisely extracting the knowledge or finding the relationships and patterns become more difficult. Hence, the need for powerful tools to assist DM is clear. To build such ABSTRACT

Cat Swarm Optimization Supported Data Mining

Facial landmarks encode critical information about face, which plays an important role in human communications. Accurate detecting and tracking facial landmarks have great potential value in intelligent user interfaces such as human-computer interactions. However, for face images with sever occlusions which may happen in real life such as hand occlusion, gesture occlusion and etc, detecting the facial landmarks is still a challenging problem. In this paper, we present a robust facial landmark detection method for image with occlusions based on Restricted Boltzmann Machine (RBM). We first present a face shape prior model which is constructed based on RBM to model the spatial shape patterns of the face. The detection process is accomplished by combining the prior shape model with the image measurements of facial landmarks. The low accuracy image measurements can be refined by the shape information embedded in the prior model. For the landmarks with severe occlusions, we firstly evaluate and determine the facial landmark occlusions, and replace their image measurements. The new image measurements are then fed into the prior model as evidence to predict the true locations. Evaluation on 3 databases demonstrates that the proposed method can detect facial landmarks accurately under severe occlusion, and achieved significant improvement over the current state of the art methods.

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Jeng-Shyang Pan

Papers

Watermarking with Visual Cryptography and Gain-Shape VQ

Information Protection and Recovery with Reversible Data Hiding

Reversible watermarking based on improved patchwork algorithm and symmetric modulo operation

Cat Swarm Optimization Supported Data Mining

Facial Landmarks Detection under Occlusions via Extended Restricted Boltzmann Machine