Entropy in Image Analysis II

Image analysis is a fundamental task for any application where extracting information from images is required. The analysis requires highly sophisticated numerical and analytical methods, particularly for those applications in medicine, security, and other fields where the results of the processing consist of data of vital importance. This fact is evident from all the articles composing the Special Issue "Entropy in Image Analysis II", in which the authors used widely tested methods to verify their results. In the process of reading the present volume, the reader will appreciate the richness of their methods and applications, in particular for medical imaging and image security, and a remarkable cross-fertilization among the proposed research areas

Characteristics of a detail preserving nonlinear filter.

by Lai Wai Kuen.Thesis (M.Phil.)--Chinese University of Hong Kong, 1993.Includes bibliographical references (leaves [119-125]).Abstract --- p.iAcknowledgement --- p.iiTable of Contents --- p.iiiChapter Chapter 1 --- IntroductionChapter 1.1 --- Background - The Need for Nonlinear Filtering --- p.1.1Chapter 1.2 --- Nonlinear Filtering --- p.1.2Chapter 1.3 --- Goal of the Work --- p.1.4Chapter 1.4 --- Organization of the Thesis --- p.1.5Chapter Chapter 2 --- An Overview of Robust Estimator Based Filters Morphological FiltersChapter 2.1 --- Introduction --- p.2.1Chapter 2.2 --- Signal Representation by Sets --- p.2.2Chapter 2.3 --- Robust Estimator Based Filters --- p.2.4Chapter 2.3.1 --- Filters based on the L-estimators --- p.2.4Chapter 2.3.1.1 --- The Median Filter and its Derivations --- p.2.5Chapter 2.3.1.2 --- Rank Order Filters and Derivations --- p.2.9Chapter 2.3.2 --- Filters based on the M-estimators (M-Filters) --- p.2.11Chapter 2.3.3 --- Filter based on the R-estimators --- p.2.13Chapter 2.4 --- Filters based on Mathematical Morphology --- p.2.14Chapter 2.4.1 --- Basic Morphological Operators --- p.2.14Chapter 2.4.2 --- Morphological Filters --- p.2.18Chapter 2.5 --- Chapter Summary --- p.2.20Chapter Chapter 3 --- Multi-Structuring Element Erosion FilterChapter 3.1 --- Introduction --- p.3.1Chapter 3.2 --- Problem Formulation --- p.3.1Chapter 3.3 --- Description of Multi-Structuring Element Erosion Filter --- p.3.3Chapter 3.3.1 --- Definition of Structuring Element for Multi-Structuring Element Erosion Filter --- p.3.4Chapter 3.3.2 --- Binary multi-Structuring Element Erosion Filter --- p.3.9Chapter 3.3.3 --- Selective Threshold Decomposition --- p.3.10Chapter 3.3.4 --- Multilevel Multi-Structuring Element Erosion Filter --- p.3.15Chapter 3.3.5 --- A Combination of Multilevel Multi-Structuring Element Erosion Filter and its Dual --- p.3.21Chapter 3.4 --- Chapter Summary --- p.3.21Chapter Chapter 4 --- Properties of Multi-Structuring Element Erosion FilterChapter 4.1 --- Introduction --- p.4.1Chapter 4.2 --- Deterministic Properties --- p.4.2Chapter 4.2.1 --- Shape of Invariant Signal --- p.4.3Chapter 4.2.1.1 --- Binary Multi-Structuring Element Erosion Filter --- p.4.5Chapter 4.2.1.2 --- Multilevel Multi-Structuring Element Erosion Filter --- p.4.16Chapter 4.2.2 --- Rate of Convergence of Multi-Structuring Element Erosion Filter --- p.4.25Chapter 4.2.2.1 --- Convergent Rate of Binary Multi-Structuring Element Erosion Filter --- p.4.25Chapter 4.2.2.2 --- Convergent Rate of Multilevel Multi-Structuring Element Erosion Filter --- p.4.28Chapter 4.3 --- Statistical Properties --- p.4.30Chapter 4.3.1 --- Output Distribution of Multi-Structuring Element Erosion Filter --- p.4.30Chapter 4.3.1.1 --- One-Dimensional Statistical Analysis of Multilevel Multi-Structuring Element Erosion Filter --- p.4.31Chapter 4.3.1.2 --- Two-Dimensional Statistical Analysis of Multilevel Multi-Structuring Element Erosion Filter --- p.4.32Chapter 4.3.2 --- Discussions on Statistical Properties --- p.4.36Chapter 4.4 --- Chapter Summary --- p.4.40Chapter Chapter 5 --- Performance EvaluationChapter 5.1 --- Introduction --- p.5.1Chapter 5.2 --- Performance Criteria --- p.5.2Chapter 5.2.1 --- Noise Suppression --- p.5.5Chapter 5.2.2 --- Subjective Criterion --- p.5.16Chapter 5.2.3 --- Computational Requirement --- p.5.20Chapter 5.3 --- Chapter Summary --- p.5.23Chapter Chapter 6 --- Recapitulation and Suggestions for Further WorkChapter 6.1 --- Recapitulation --- p.6.1Chapter 6.2 --- Suggestions for Further Work --- p.6.4Chapter 6.2.1 --- Probability Measure Function for the Two-Dimensional Filter --- p.6.4Chapter 6.2.2 --- Hardware Implementation --- p.6.5ReferencesAppendice

Application and Theory of Multimedia Signal Processing Using Machine Learning or Advanced Methods

This Special Issue is a book composed by collecting documents published through peer review on the research of various advanced technologies related to applications and theories of signal processing for multimedia systems using ML or advanced methods. Multimedia signals include image, video, audio, character recognition and optimization of communication channels for networks. The specific contents included in this book are data hiding, encryption, object detection, image classification, and character recognition. Academics and colleagues who are interested in these topics will find it interesting to read

LSST Science Book, Version 2.0

A survey that can cover the sky in optical bands over wide fields to faint magnitudes with a fast cadence will enable many of the exciting science opportunities of the next decade. The Large Synoptic Survey Telescope (LSST) will have an effective aperture of 6.7 meters and an imaging camera with field of view of 9.6 deg^2, and will be devoted to a ten-year imaging survey over 20,000 deg^2 south of +15 deg. Each pointing will be imaged 2000 times with fifteen second exposures in six broad bands from 0.35 to 1.1 microns, to a total point-source depth of r~27.5. The LSST Science Book describes the basic parameters of the LSST hardware, software, and observing plans. The book discusses educational and outreach opportunities, then goes on to describe a broad range of science that LSST will revolutionize: mapping the inner and outer Solar System, stellar populations in the Milky Way and nearby galaxies, the structure of the Milky Way disk and halo and other objects in the Local Volume, transient and variable objects both at low and high redshift, and the properties of normal and active galaxies at low and high redshift. It then turns to far-field cosmological topics, exploring properties of supernovae to z~1, strong and weak lensing, the large-scale distribution of galaxies and baryon oscillations, and how these different probes may be combined to constrain cosmological models and the physics of dark energy.Comment: 596 pages. Also available at full resolution at http://www.lsst.org/lsst/sciboo

A Novel Multi-Symbol Curve Fit based CABAC Framework for Hybrid Video Codec's with Improved Coding Efficiency and Throughput

Video compression is an essential component of present-day applications and a decisive factor between the success or failure of a business model. There is an ever increasing demand to transmit larger number of superior-quality video channels into the available transmission bandwidth. Consumers are increasingly discerning about the quality and performance of video-based products and there is therefore a strong incentive for continuous improvement in video coding technology for companies to have market edge over its competitors. Even though processor speeds and network bandwidths continue to increase, a better video compression results in a more competitive product. This drive to improve video compression technology has led to a revolution in the last decade. In this thesis we addresses some of these data compression problems in a practical multimedia system that employ Hybrid video coding schemes. Typically Real life video signals show non-stationary statistical behavior. The statistics of these signals largely depend on the video content and the acquisition process. Hybrid video coding schemes like H264/AVC exploits some of the non-stationary characteristics but certainly not all of it. Moreover, higher order statistical dependencies on a syntax element level are mostly neglected in existing video coding schemes. Designing a video coding scheme for a video coder by taking into consideration these typically observed statistical properties, however, offers room for significant improvements in coding efficiency.In this thesis work a new frequency domain curve-fitting compression framework is proposed as an extension to H264 Context Adaptive Binary Arithmetic Coder (CABAC) that achieves better compression efficiency at reduced complexity. The proposed Curve-Fitting extension to H264 CABAC, henceforth called as CF-CABAC, is modularly designed to conveniently fit into existing block based H264 Hybrid video Entropy coding algorithms. Traditionally there have been many proposals in the literature to fuse surfaces/curve fitting with Block-based, Region based, Training-based (VQ, fractals) compression algorithms primarily to exploiting pixel- domain redundancies. Though the compression efficiency of these are expectantly better than DCT transform based compression, but their main drawback is the high computational demand which make the former techniques non-competitive for real-time applications over the latter. The curve fitting techniques proposed so far have been on the pixel domain. The video characteristic on the pixel domain are highly non-stationary making curve fitting techniques not very efficient in terms of video quality, compression ratio and complexity. In this thesis, we explore using curve fitting techniques to Quantized frequency domain coefficients. we fuse this powerful technique to H264 CABAC Entropy coding. Based on some predictable characteristics of Quantized DCT coefficients, a computationally in-expensive curve fitting technique is explored that fits into the existing H264 CABAC framework. Also Due to the lossy nature of video compression and the strong demand for bandwidth and computation resources in a multimedia system, one of the key design issues for video coding is to optimize trade-off among quality (distortion) vs compression (rate) vs complexity. This thesis also briefly studies the existing rate distortion (RD) optimization approaches proposed to video coding for exploring the best RD performance of a video codec. Further, we propose a graph based algorithm for Rate-distortion. optimization of quantized coefficient indices for the proposed CF-CABAC entropy coding