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作者： KOK CHI WEE National University of Singapore

学位级别：硕士

wavelets play a major role in many applications like data compression, signal and image processing, and resolution enhancement. Many studies have been con- ducted on wavelets and here we consider interpolatory periodic wavelets, a class of wavelets that provides certain advantage over other wavelets. This thesis continues the study of interpolatory periodic wavelets done in the honours thesis [7]. Periodic multiresolutions with dilation 2 were considered in the honours thesis and extension is being made by considering a general dilation M that is greater than or equal to 2. Chapter 1 contains a detailed study of periodic multiresolutions with general dilation, together with their corresponding scaling functions and wavelets. From this analysis of general periodic scaling functions and wavelets, the extended interpolatory theory is developed in Chapter 2. Examples of interpolatory periodic scaling functions and wavelets are given in Chapter 3. All results in Chapters 2 and 3 are new. The paper [4] supplied part of the material in Chapter 1 on which the remaining new material in the chapter is built upon. Results in Section 1.2, which is about periodic multiresolutions, are found in [4]. Some of these have been formulated or proved differently so as to follow the presentation sequence here. In contrast, the development of periodic wavelets in Section 1.3 takes a different approach. This section contains necessary and sufficient conditions for the existence of various wavelets, some of which are innovated from [4] and are cornerstones of the enhanced interpolatory theory. This section also encompasses new material on wavelets that form Riesz bases for L 2 [0, 2π. For results which have analogous versions in the honours thesis, their proofs are omitted to avoid duplication whenever there is no significant change in technique. Besides the consideration of a general dilation, the generalization of the inter- polating height of both scaling functions and wavelets add consi

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Estimation of multiple local orientations in image signals

Estimation of multiple local orientations in image signals

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IEEE International Conference on Acoustics, Speech, and signal processing

作者： Aach, T Stuke, I Mota, C Barth, E Univ Lubeck Inst Signal Proc D-23538 Lubeck Germany

Local orientation estimation can be posed as the problem of finding the minimum grey level variance axis within a local neighbourhood. In 2D image signals, this corresponds to the eigensystem analysis of a 2 x 2-tensor, which yields valid results for single orientations. We describe extensions to multiple overlaid orientations, which may be caused by transparent objects, crossings, bifurcations, corners etc. Multiple orientation detection is based on the eigensystem analysis of an appropriately extended tensor, yielding so-called mixed orientation parameters. These mixed orientation parameters can be regarded as another tensor built from the sought individual orientation parameters. We show how the mixed orientation tensor can be decomposed into the individual orientations by finding the roots of a polynomial. applications are, e.g., in directional filtering and interpolation, feature extraction for corners or crossings, and signal separation.

关键词： image analysis

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Background modeling and target segmentation via modified Kalman filtering

Background modeling and target segmentation via modified Kal...

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16th Conference on signal and Data processing of Small Targets

作者： Searle, SJ Univ Adelaide Sch Elect & Elect Engn CSSIP Adelaide SA 5005 Australia

ISBN: (纸本)081945351X

In target detection and tracking applications with imagery data taken from a moving camera platform, it is necessary to segment potential targets in each image frame. This is typically done by preprocessing individual images to exploit some known attribute about the data. Often these methods make many false detections, particularly in the presence of additive noise, and the results thus require significant post-processing. A means of estimating the background in the imagery sequence under the formalism of the Kalman filter is suggested. This background estimate is then used to recast the segmentation problem as one of outlier detection, and the result of segmentation is used to modify the filter update. Ways of making the technique computationally benign are discussed. The technique is used to analyse a simulated image sequence, and the performance is compared to that of a single-frame back-round-estimation technique. The feasibility of target segmentation via background tracking is thus demonstrated.

关键词： image segmentation Filtering (signal processing) Target detection Matrices Cameras image filtering Digital filtering Electronic filtering Optical filters image analysis

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Recognition by symmetry derivatives and the generalized structure tensor

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IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE 2004年第12期26卷 1590-1605页

作者： Bigun, J Bigun, T Nilsson, K Halmstad Univ SE-30118 Halmstad Sweden TietoEnator AB S-58223 Linkoping Sweden

We suggest a set of complex differential operators that can be used to produce and filter dense orientation ( tensor) fields for feature extraction, matching, and pattern recognition. We present results on the invariance properties of these operators, that we call symmetry derivatives. These show that, in contrast to ordinary derivatives, all orders of symmetry derivatives of Gaussians yield a remarkable invariance: They are obtained by replacing the original differential polynomial with the same polynomial, but using ordinary coordinates x and y corresponding to partial derivatives. Moreover, the symmetry derivatives of Gaussians are closed under the convolution operator and they are invariant to the Fourier transform. The equivalent of the structure tensor, representing and extracting orientations of curve patterns, had previously been shown to hold in harmonic coordinates in a nearly identical manner. As a result, positions, orientations, and certainties of intricate patterns, e. g., spirals, crosses, parabolic shapes, can be modeled by use of symmetry derivatives of Gaussians with greater analytical precision as well as computational efficiency. Since Gaussians and their derivatives are utilized extensively in image processing, the revealed properties have practical consequences for local orientation based feature extraction. The usefulness of these results is demonstrated by two applications: 1) tracking cross markers in long image sequences from vehicle crash tests and 2) alignment of noisy fingerprints.

关键词： Gaussians orientation fields structure tensor differential invariants cross detection fingerprints tensor voting tracking filtering feature measurement wavelets and fractals moments invariants vision and scene understanding representations shape tracking registration alignment

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Robust automatic clustering of hyperspectral imagery using non-Gaussian mixtures

Robust automatic clustering of hyperspectral imagery using n...

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Conference on image and signal processing for Remote Sensing x

作者： Farrell, MD Mersereau, RM Georgia Inst Technol Ctr Signal & Image Proc Atlanta GA 30332 USA

ISBN: (纸本)0819455202

This paper addresses the utility of robust automatic clustering of hyperspectral image data. Such clustering is possible only when the background in a scene is accurately modeled. Mixtures of non-Gaussian densities have been discussed recently, and here we move further down this path. We derive a t mixture model for the background in hyperspectral images, using two techniques for estimating parameters based on the Expectation-Maximization algorithm. Visual and statistical evaluation of these techniques are made with AVIRIS data. Dealing with the data's inhomogeneity by developing proper models of the background (i.e. clutter) in a hyperspectral image is important in target detection applications, especially for accurate performance prediction and detector analysis.

关键词： hyperspectral imaging clustering mixture models EM algorithm AVIRIS data

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Extended locally linear embedding with Gabor wavelets for face recognition

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17th Annual Australian Conference on Artificial Intelligence

作者： Zheng, ZL Yang, J Qing, x Shanghai Jiao Tong Univ Inst Image Proc & Pattern Recognit Shanghai 200030 Peoples R China

ISBN: (纸本)3540240594

Many current face recognition algorithms are based on face representations found by unsupervised statistical methods. One of the fundamental problems of face recognition is dimensionality reduction. Principal component analysis is a well-known linear method for reducing dimension. Recently, locally linear embedding (LLE) is proposed as an unsupervised procedure for mapping higher-dimensional data nonlinearly to a lower-dimensional space. This method, when combined with fisher linear discriminant models, is called extended LLE (ELLE) in this paper. Furthermore, the ELLE yields good classification results in the experiments. Also, we apply the Gabor wavelets as a pre-processing method which contributes a lot to the final results because it deals with the detailed signal of an image and is robust to light variation. Numerous experiments on ORL and AR face data sets have shown that our algorithm is more effective than the original LLE and is insensitive to light variation.

关键词： Face recognition

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Elements of design of an object-oriented framework prototype for wavelet-based image processing using design patterns

Elements of design of an object-oriented framework prototype...

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作者： Roy, Yves Concordia University (Canada)

学位级别：M.Comp.Sc.

Design patterns and frameworks are increasingly popular techniques for addressing key aspects of the design of complex software systems. Patterns support the reuse of design expertise by articulating the aspects of successful solutions to design problems in a particular context. Frameworks are concrete realizations of groups of patterns that enable code reuse and design reuse by capturing the common abstractions of an application domain while leaving control of application-specific structures and behaviors to application developers. Application frameworks encapsulate expertise applicable to a wide range of programs and aim to provide a full range of functionality typically needed by an application thus encompassing a horizontal slice of functionality that can be applied across client domains. Domain frameworks encapsulate expertise in a particular domain, thus encompassing a vertical slice of functionality for a specific problem domain reducing the amount of work that needs to be done to implement domain-specific applications. wavelets and wavelet transform concepts originated from a synthesis of ideas developed during the last thirty years in engineering, physics, and pure mathematics. wavelets have been very successful in many scientific and engineering fields and they have led to many successful applications in signal analysis and image processing. In this thesis we are presenting design and implementation elements for the development of an object-oriented application and domain framework prototype for wavelet-based image processing applications using design patterns.

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Pixel level stochastic arithmetic for intelligent image capture

Pixel level stochastic arithmetic for intelligent image capt...

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Conference on Sensors and Camera Systems for Scientific, Industrial, and Digital Photography applications V

作者： Hammadou, T DigiSens Technol Sydney NSW Australia

ISBN: (纸本)0819452041

The design and implementation of a smart image sensor to provide high dynamic range and pixel level digital image processing is described. In this article we investigate important issues in the development of digital intelligent pixel CMOS image sensors. We have developed a high density imager with pixel level stochastic arithmetic and a high dynamic range exceeding 90 db. The ASIC prototype named PIKASSO includes a 96 x 64 pixel array, each pixel has a fill factor of 15% in an area of 29 x 29 mum(2). The average power consumption per pixel at a frequency of 150 kHz is 78 muW.

关键词： Stochastic processes Photodiodes Imaging systems CMOS sensors image processing image sensors Sensors signal to noise ratio Charge-coupled devices Prototyping

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The fast parametric slantlet transform with applications

The fast parametric slantlet transform with applications

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Conference on image processing - Algorithms and Systems III

作者： Agaian, S Tourshan, K Noonan, JP Univ Texas Coll Engn Dept Elect & Comp Engn San Antonio TX 78249 USA

ISBN: (纸本)0819452017

Transform methods have played an important role in signal and image processing applications. Recently, Selesnick has constructed the new orthogonal discrete wavelet transform, called the slantlet wavelet, with two zero moments and with improved time localization. The discrete slantlet wavelet transform is carried out by an existing filterbank which lacks a tree structure and has a complexity problem. The slantlet wavelet has been successfully applied in compression and denoising. In this paper, we present a new class of orthogonal parametric fast Haar slantlet transform system where the slantlet wavelet and Haar transforms are special cases of it. We propose designing the slantlet wavelet transform using Haar slantlet transform matrix. A new class of parametric filterbanks is developed. The behavior of the parametric Haar slantlet transforms in signal and image denoising is presented. We show that the new technique performs better than the slantlet wavelet transform in denoising for piecewise constant signals. We also show that the parametric Haar slantlet transform performs better than the cosine and Fourier transforms for grey level images.

关键词： parametric slantlet transform image/signal denoising

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Impulsive noise removal with the use of local adaptive non-linear filter

Impulsive noise removal with the use of local adaptive non-l...

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Conference on applications of Digital image processing xxVII

作者： Mozerov, M Kober, V Russian Acad Sci Inst Informat Transmiss Problems Moscow Russia

ISBN: (纸本)0819454966

The goal of many image processing tasks is to recover an ideal high-quality signal from data that are degraded by impulsive noise, because the human visual system is very sensitive to the high amplitude of noise signals, thus noise in an image can result in a subjective loss of information. This work presents an elegant solution to the impulsive noise removal problem. The proposed technique takes into account three important factors for image filtering, i.e. noise attenuation, edge preservation, as well as detail retention. The conventional filtering schemes utilize a fixed shape of the moving window such as rectangle and circle. In contrast, the proposed spatially connected filter works with the moving window of signal-dependent shape. Experimental results show the superior performance of the proposed filtering algorithm compared to the conventional schemes in terms of both subjective and objective evaluations.

关键词： image processing nonlinear adaptive filtering impulsive noise spatially adaptive neighborhood

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