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 paper describes a subsampling and PSK (Phase Shift Keying) based watermarking scheme which can resist the permutation attacking. In recent years, a series of subsampling based watermarking schemes have been proposed, but in 2005, Lu etc. proposed a permutation attacking algorithm which can effectively destroy this kind of subsampling based watermarking schemes. In this paper, we proposed a subsampling and PSK based watermarking scheme which can resist the permutation attacking. In our method, the watermark information is embedded into the average values of the phase of the DFT coefficients which are in the different subimages. Experimental results show that our scheme can resist the permutation attacking effectively, and it can also resist some common signal processing attacking successfully.

A Subsampling and PSK Based Watermarking Scheme Resisted the Permutation Attacking

We present a new method to day and night image stitching and rendering for exploring the space-time continuum within a two-dimensional still photograph Our method is based on a two-scale decomposition of the input images Homography matrix is calculated by matching feature points of detail layer pairs, which can avoid impact of illumination Then detail layers and base layers are stitched together, respectively We mapped the stitched base layer to a radiance map and then rendered it as time-lapse effect based on human vision system Compared with previous method, our method is easier to implement and has a larger viewing angle

Day and Night Image Stitching and Rendering

Abstract Magnetic Resonance Super-resolution Imaging Measurement (MRIM) is an effective way of measuring materials. MRIM has wide applications in physics, chemistry, biology, geology, medical and material science, especially in medical diagnosis. It is feasible to improve the resolution of MR imaging through increasing radiation intensity, but the high radiation intensity and the longtime of magnetic field harm the human body. Thus, in the practical applications the resolution of hardware imaging reaches the limitation of resolution. Software-based super-resolution technology is effective to improve the resolution of image. This work proposes a framework of dictionary-optimized sparse learning based MR super-resolution method. The framework is to solve the problem of sample selection for dictionary learning of sparse reconstruction. The textural complexity-based image quality representation is proposed to choose the optimal samples for dictionary learning. Comprehensive experiments show that the dictionary-optimized sparse learning improves the performance of sparse representation.

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Magnetic Resonance Super-resolution Imaging Measurement with Dictionary-optimized Sparse Learning

Feature extraction is an important step and essential process in many data analysis areas, such as face recognition, handwriting recognition, human facial expression analysis, speech recognition.

Jeng-Shyang Pan

Papers

A Subsampling and PSK Based Watermarking Scheme Resisted the Permutation Attacking

Day and Night Image Stitching and Rendering

Magnetic Resonance Super-resolution Imaging Measurement with Dictionary-optimized Sparse Learning

Kernel-Optimization-Based Face Recognition

Corrigendum to "A parallel compact cuckoo search algorithm for three-dimensional path planning" [Appl. Soft Comput. 94 (2020) 106443]