Showing papers on "Image processing published in 2000"

Content-based image retrieval at the end of the early years

Abstract: Presents a review of 200 references in content-based image retrieval. The paper starts with discussing the working conditions of content-based retrieval: patterns of use, types of pictures, the role of semantics, and the sensory gap. Subsequent sections discuss computational steps for image retrieval systems. Step one of the review is image processing for retrieval sorted by color, texture, and local geometry. Features for retrieval are discussed next, sorted by: accumulative and global features, salient points, object and shape features, signs, and structural combinations thereof. Similarity of pictures and objects in pictures is reviewed for each of the feature types, in close connection to the types and means of feedback the user of the systems is capable of giving by interaction. We briefly discuss aspects of system engineering: databases, system architecture, and evaluation. In the concluding section, we present our view on: the driving force of the field, the heritage from computer vision, the influence on computer vision, the role of similarity and of interaction, the need for databases, the problem of evaluation, and the role of the semantic gap.

High performance scalable image compression with EBCOT

Abstract: A new image compression algorithm is proposed, based on independent embedded block coding with optimized truncation of the embedded bit-streams (EBCOT). The algorithm exhibits state-of-the-art compression performance while producing a bit-stream with a rich set of features, including resolution and SNR scalability together with a "random access" property. The algorithm has modest complexity and is suitable for applications involving remote browsing of large compressed images. The algorithm lends itself to explicit optimization with respect to MSE as well as more realistic psychovisual metrics, capable of modeling the spatially varying visual masking phenomenon.

Handbook of Image and Video Processing

Abstract: 1.0 INTRODUCTION 1.1 Introduction to Image and Video Processing (Bovik) 2.0 BASIC IMAGE PROCESSING TECHNIQUES 2.1 Basic Gray-Level Image Processing (Bovik) 2.2 Basic Binary Image Processing (Desai/Bovik) 2.3 Basic Image Fourier Analysis and Convolution (Bovik) 3.0 IMAGE AND VIDEO PROCESSING Image and Video Enhancement and Restoration 3.1 Basic Linear Filtering for Image Enhancement (Acton/Bovik) 3.2 Nonlinear Filtering for Image Enhancement (Arce) 3.3 Morphological Filtering for Image Enhancement and Detection (Maragos) 3.4 Wavelet Denoising for Image Enhancement (Wei) 3.5 Basic Methods for Image Restoration and Identification (Biemond) 3.6 Regularization for Image Restoration and Reconstruction (Karl) 3.7 Multi-Channel Image Recovery (Galatsanos) 3.8 Multi-Frame Image Restoration (Schulz) 3.9 Iterative Image Restoration (Katsaggelos) 3.10 Motion Detection and Estimation (Konrad) 3.11 Video Enhancement and Restoration (Lagendijk) Reconstruction from Multiple Images 3.12 3-D Shape Reconstruction from Multiple Views (Aggarwal) 3.13 Image Stabilization and Mosaicking (Chellappa) 4.0 IMAGE AND VIDEO ANALYSIS Image Representations and Image Models 4.1 Computational Models of Early Human Vision (Cormack) 4.2 Multiscale Image Decomposition and Wavelets (Moulin) 4.3 Random Field Models (Zhang) 4.4 Modulation Models (Havlicek) 4.5 Image Noise Models (Boncelet) 4.6 Color and Multispectral Representations (Trussell) Image and Video Classification and Segmentation 4.7 Statistical Methods (Lakshmanan) 4.8 Multi-Band Techniques for Texture Classification and Segmentation (Manjunath) 4.9 Video Segmentation (Tekalp) 4.10 Adaptive and Neural Methods for Image Segmentation (Ghosh) Edge and Boundary Detection in Images 4.11 Gradient and Laplacian-Type Edge Detectors (Rodriguez) 4.12 Diffusion-Based Edge Detectors (Acton) Algorithms for Image Processing 4.13 Software for Image and Video Processing (Evans) 5.0 IMAGE COMPRESSION 5.1 Lossless Coding (Karam) 5.2 Block Truncation Coding (Delp) 5.3 Vector Quantization (Smith) 5.4 Wavelet Image Compression (Ramchandran) 5.5 The JPEG Lossy Standard (Ansari) 5.6 The JPEG Lossless Standard (Memon) 5.7 Multispectral Image Coding (Bouman) 6.0 VIDEO COMPRESSION 6.1 Basic Concepts and Techniques of Video Coding (Barnett/Bovik) 6.2 Spatiotemporal Subband/Wavelet Video Compression (Woods) 6.3 Object-Based Video Coding (Kunt) 6.4 MPEG-I and MPEG-II Video Standards (Ming-Ting Sun) 6.5 Emerging MPEG Standards: MPEG-IV and MPEG-VII (Kossentini) 7.0 IMAGE AND VIDEO ACQUISITION 7.1 Image Scanning, Sampling, and Interpolation (Allebach) 7.2 Video Sampling and Interpolation (Dubois) 8.0 IMAGE AND VIDEO RENDERING AND ASSESSMENT 8.1 Image Quantization, Halftoning, and Printing (Wong) 8.2 Perceptual Criteria for Image Quality Evaluation (Pappas) 9.0 IMAGE AND VIDEO STORAGE, RETRIEVAL AND COMMUNICATION 9.1 Image and Video Indexing and Retrieval (Tsuhan Chen) 9.2 A Unified Framework for Video Browsing and Retrieval (Huang) 9.3 Image and Video Communication Networks (Schonfeld) 9.4 Image Watermarking (Pitas) 10.0 APPLICATIONS OF IMAGE PROCESSING 10.1 Synthetic Aperture Radar Imaging (Goodman/Carrera) 10.2 Computed Tomography (Leahy) 10.3 Cardiac Imaging (Higgins) 10.4 Computer-Aided Detection for Screening Mammography (Bowyer) 10.5 Fingerprint Classification and Matching (Jain) 10.6 Probabilistic Models for Face Recognition (Pentland/Moghaddam) 10.7 Confocal Microscopy (Merchant/Bartels) 10.8 Automatic Target Recognition (Miller) Index

The LOCO-I lossless image compression algorithm: principles and standardization into JPEG-LS

Abstract: LOCO-I (LOw COmplexity LOssless COmpression for Images) is the algorithm at the core of the new ISO/ITU standard for lossless and near-lossless compression of continuous-tone images, JPEG-LS. It is conceived as a "low complexity projection" of the universal context modeling paradigm, matching its modeling unit to a simple coding unit. By combining simplicity with the compression potential of context models, the algorithm "enjoys the best of both worlds." It is based on a simple fixed context model, which approaches the capability of the more complex universal techniques for capturing high-order dependencies. The model is tuned for efficient performance in conjunction with an extended family of Golomb (1966) type codes, which are adaptively chosen, and an embedded alphabet extension for coding of low-entropy image regions. LOCO-I attains compression ratios similar or superior to those obtained with state-of-the-art schemes based on arithmetic coding. Moreover, it is within a few percentage points of the best available compression ratios, at a much lower complexity level. We discuss the principles underlying the design of LOCO-I, and its standardization into JPEC-LS.

Fast texture synthesis using tree-structured vector quantization

Abstract: Texture synthesis is important for many applications in computer graphics, vision, and image processing. However, it remains difficult to design an algorithm that is both efficient and capable of generating high quality results. In this paper, we present an efficient algorithm for realistic texture synthesis. The algorithm is easy to use and requires only a sample texture as input. It generates textures with perceived quality equal to or better than those produced by previous techniques, but runs two orders of magnitude faster. This permits us to apply texture synthesis to problems where it has traditionally been considered impractical. In particular, we have applied it to constrained synthesis for image editing and temporal texture generation. Our algorithm is derived from Markov Random Field texture models and generates textures through a deterministic searching process. We accelerate this synthesis process using tree-structured vector quantization.

Automatic analysis of the difference image for unsupervised change detection

Abstract: One of the main problems related to unsupervised change detection methods based on the "difference image" lies in the lack of efficient automatic techniques for discriminating between changed and unchanged pixels in the difference image. Such discrimination is usually performed by using empirical strategies or manual trial-and-error procedures, which affect both the accuracy and the reliability of the change-detection process. To overcome such drawbacks, in this paper, the authors propose two automatic techniques (based on the Bayes theory) for the analysis of the difference image. One allows an automatic selection of the decision threshold that minimizes the overall change detection error probability under the assumption that pixels in the difference image are independent of one another. The other analyzes the difference image by considering the spatial-contextual information included in the neighborhood of each pixel. In particular, an approach based on Markov Random Fields (MRFs) that exploits interpixel class dependency contexts is presented. Both proposed techniques require the knowledge of the statistical distributions of the changed and unchanged pixels in the difference image. To perform an unsupervised estimation of the statistical terms that characterize these distributions, they propose an iterative method based on the Expectation-Maximization (EM) algorithm. Experimental results confirm the effectiveness of both proposed techniques.

WFPC2 Stellar Photometry with HSTphot

Abstract: HSTphot, a photometry package designed to handle the undersampled PSFs found in WFPC2 images, is introduced and described, as well as some of the considerations that have to be made in order to obtain accurate PSF-fitting stellar photometry with WFPC2 data. Tests of HSTphot's internal reliability are made using multiple observations of the same field, and tests of external reliability are made by comparing with DoPHOT reductions of the same data. Subject headz'ngs: techniques: photometric

Adaptive image contrast enhancement using generalizations of histogram equalization

Abstract: This paper proposes a scheme for adaptive image-contrast enhancement based on a generalization of histogram equalization (HE). HE is a useful technique for improving image contrast, but its effect is too severe for many purposes. However, dramatically different results can be obtained with relatively minor modifications. A concise description of adaptive HE is set out, and this framework is used in a discussion of past suggestions for variations on HE. A key feature of this formalism is a "cumulation function," which is used to generate a grey level mapping from the local histogram. By choosing alternative forms of cumulation function one can achieve a wide variety of effects. A specific form is proposed. Through the variation of one or two parameters, the resulting process can produce a range of degrees of contrast enhancement, at one extreme leaving the image unchanged, at another yielding full adaptive equalization.

Color Image Processing and Applications

Abstract: This book claims to fill a niche in the provision of textbooks devoted to image processing by being devoted to colour aspects. It is aimed at researchers and practitioners working in the area of colour image processing, particularly graduates in Computer Science and Electrical and Computer Engineering. The book is structured in such a way that, after reading an introductory chapter on colour, readers can work their way through the following four chapters on image processing, or omit them and move directly to the final three chapters that deal with colour image analysis and coding. It is not immediately apparent from reading the preface that companion image processing software is available online from the publisher's website, and this is not made use of as an integral part of the book. Regrettably, the book shows much evidence of a lack of rigorous proof reading and editing. A number of errors can be found, particularly in the first chapter; this provides the fundamentals in colour science on which the book is based. For example, on the first page the visible spectrum is incorrectly reproduced with the wrong wavelength scale and photoreceptors are referred to as `roads'. A few pages on there is a complete mismatch between the explanatory text and diagrams concerning the CIE, XYZ and RGB colour matching functions. One diagram appears to have been reproduced twice, is incorrectly titled and not annotated. Also, the CIE chromaticity diagram lacks a wavelength scale and the colours of the diagram are incorrectly reproduced. Unfortunately, these fundamental errors appear in the first ten pages and have the unfortunate effect of detracting from the authoritative nature of the book as a whole. A further example of poor proof reading/editing can be found towards the end of the chapter in which photographic film is defined as follows: `The film which is used by conventional cameras contains three emulsion layers which are sensitive to red and blue light, which enters through the camera lens.' The chapters do improve, however, as one goes through the book, although in chapter 2 the description of the origins of photographic noise is incorrect and incomplete (`the noise is mainly due to the silver grains that precipitate during film exposure'). Also, the origins of noise in photoelectronic sensors are incompletely described. Each chapter is accompanied by a substantial number of references to the primary sources of information, many of which are to recent literature in the field, in very useful summary or conclusion sections. It is puzzling that in view of the significance of the Fourier transform in image processing, it is not included, other than a brief mention of the Discrete Fourier Transform in the chapter on image compression. Adaptive image filters are described at length in chapter 3, which is followed by chapters dedicated to colour edge detection, enhancement and restoration, image segmentation, image compression and emerging applications. The latter makes interesting reading but is based almost exclusively on the detection and automatic location of the human face. The index is not very exhaustive and as a consequence it is difficult to find many items that are discussed in the text but are not indexed. A few examples include: Wiener filter, Sobel, Prewitt and Robert's edge detection, all of which appear in the text and in the indices of most books on image processing but not in the index to this book. Also, most of the existing texts on image processing include many aspects of colour, which detracts somewhat from this book's claimed uniqueness, although it does contain more state-of-the-art aspects. Ralph Jacobson

Spatially adaptive wavelet thresholding with context modeling for image denoising

Abstract: The method of wavelet thresholding for removing noise, or denoising, has been researched extensively due to its effectiveness and simplicity. Much of the literature has focused on developing the best uniform threshold or best basis selection. However, not much has been done to make the threshold values adaptive to the spatially changing statistics of images. Such adaptivity can improve the wavelet thresholding performance because it allows additional local information of the image (such as the identification of smooth or edge regions) to be incorporated into the algorithm. This work proposes a spatially adaptive wavelet thresholding method based on context modeling, a common technique used in image compression to adapt the coder to changing image characteristics. Each wavelet coefficient is modeled as a random variable of a generalized Gaussian distribution with an unknown parameter. Context modeling is used to estimate the parameter for each coefficient, which is then used to adapt the thresholding strategy. This spatially adaptive thresholding is extended to the overcomplete wavelet expansion, which yields better results than the orthogonal transform. Experimental results show that spatially adaptive wavelet thresholding yields significantly superior image quality and lower MSE than the best uniform thresholding with the original image assumed known.

New variants of a method of MRI scale standardization

Abstract: One of the major drawbacks of magnetic resonance imaging (MRI) has been the lack of a standard and quantifiable interpretation of image intensities. Unlike in other modalities, such as X-ray computerized tomography, MR images taken for the same patient on the same scanner at different times may appear different from each other due to a variety of scanner-dependent variations and, therefore, the absolute intensity values do not have a fixed meaning. The authors have devised a two-step method wherein all images (independent of patients and the specific brand of the MR scanner used) can be transformed in such a may that for the same protocol and body region, in the transformed images similar intensities will have similar tissue meaning. Standardized images can be displayed with fixed windows without the need of per-case adjustment. More importantly, extraction of quantitative information about healthy organs or about abnormalities can be considerably simplified. This paper introduces and compares new variants of this standardizing method that can help to overcome some of the problems with the original method.

Fourth-order partial differential equations for noise removal

Abstract: A class of fourth-order partial differential equations (PDEs) are proposed to optimize the trade-off between noise removal and edge preservation. The time evolution of these PDEs seeks to minimize a cost functional which is an increasing function of the absolute value of the Laplacian of the image intensity function. Since the Laplacian of an image at a pixel is zero if the image is planar in its neighborhood, these PDEs attempt to remove noise and preserve edges by approximating an observed image with a piecewise planar image. Piecewise planar images look more natural than step images which anisotropic diffusion (second order PDEs) uses to approximate an observed image. So the proposed PDEs are able to avoid the blocky effects widely seen in images processed by anisotropic diffusion, while achieving the degree of noise removal and edge preservation comparable to anisotropic diffusion. Although both approaches seem to be comparable in removing speckles in the observed images, speckles are more visible in images processed by the proposed PDEs, because piecewise planar images are less likely to mask speckles than step images and anisotropic diffusion tends to generate multiple false edges. Speckles can be easily removed by simple algorithms such as the one presented in this paper.

DCT-domain watermarking techniques for still images: detector performance analysis and a new structure

Abstract: A spread-spectrum-like discrete cosine transform (DCT) domain watermarking technique for copyright protection of still digital images is analyzed. The DCT is applied in blocks of 8/spl times/8 pixels, as in the JPEG algorithm. The watermark can encode information to track illegal misuses. For flexibility purposes, the original image is not necessary during the ownership verification process, so it must be modeled by noise. Two tests are involved in the ownership verification stage: watermark decoding, in which the message carried by the watermark is extracted, and watermark detection, which decides whether a given image contains a watermark generated with a certain key. We apply generalized Gaussian distributions to statistically model the DCT coefficients of the original image and show how the resulting detector structures lead to considerable improvements in performance with respect to the correlation receiver, which has been widely considered in the literature and makes use of the Gaussian noise assumption. As a result of our work, analytical expressions for performance measures, such as the probability of errors in watermark decoding and the probabilities of false alarms and of detection in watermark detection, are derived and contrasted with experimental results.

Cardiac motion tracking using cine harmonic phase (harp) magnetic resonance imaging

Abstract: The present invention relates to a method of measuring motion of an object such as a heart by magnetic resonance imaging. A pulse sequence is applied to spatially modulate a region of interest of the object and at least one first spectral peak is acquired from the Fourier domain of the spatially modulated object. The inverse Fourier transform information of the acquired first spectral-peaks is computed and a computed first harmonic phase image is determined from each spectral peak. The process is repeated to create a second harmonic phase image from each second spectral peak and the strain is determined from the first and second harmonic phase images. In a preferred embodiment, the method is employed to determine strain within the myocardium and to determine change in position of a point at two different times which may result in an increased distance or reduced distance. The method may be employed to determine the path of motion of a point through a sequence of tag images depicting movement of the heart. The method may be employed to determine circumferential strain and radial strain.

High dynamic range imaging: spatially varying pixel exposures

Abstract: While real scenes produce a wide range of brightness variations, vision systems use low dynamic range image detectors that typically provide 8 bits of brightness data at each pixel. The resulting low quality images greatly limit what vision can accomplish today. This paper proposes a very simple method for significantly enhancing the dynamic range of virtually any imaging system. The basic principle is to simultaneously sample the spatial and exposure dimensions of image irradiance. One of several ways to achieve this is by placing an optical mask adjacent to a conventional image detector array. The mask has a pattern with spatially varying transmittance, thereby giving adjacent pixels on the detector different exposures to the scene. The captured image is mapped to a high dynamic range image using an efficient image reconstruction algorithm. The end result is an imaging system that can measure a very wide range of scene radiance and produce a substantially larger number of brightness levels, with a slight reduction in spatial resolution. We conclude with several examples of high dynamic range images computed using spatially varying pixel exposures.

Image registration by maximization of combined mutual information and gradient information

Abstract: Mutual information has developed into an accurate measure for rigid and affine monomodality and multimodality image registration. The robustness of the measure is questionable, however. A possible reason for this is the absence of spatial information in the measure. The present paper proposes to include spatial information by combining mutual information with a term based on the image gradient of the images to be registered. The gradient term not only seeks to align locations of high gradient magnitude, but also aims for a similar orientation of the gradients at these locations. Results of combining both standard mutual information as well as a normalized measure are presented for rigid registration of three-dimensional clinical images [magnetic resonance (MR), computed tomography (CT), and positron emission tomography (PET)]. The results indicate that the combined measures yield a better registration function does mutual information or normalized mutual information per se. The registration functions are less sensitive to low sampling resolution, do not contain incorrect global maxima that are sometimes found in the mutual information function, and interpolation-induced local minima can be reduced. These characteristics yield the promise of more robust registration measures. The accuracy of the combined measures is similar to that of mutual information-based methods.

Advances in iterative multigrid PIV image processing

Abstract: An image-processing technique is proposed, which performs iterative interrogation of particle image velocimetry (PIV) recordings. The method is based on cross-correlation, enhancing the matching performances by means of a relative transformation between the interrogation areas. On the basis of an iterative prediction of the tracers motion, window offset and deformation are applied, accounting for the local deformation of the fluid continuum. In addition, progressive grid refinement is applied in order to maximise the spatial resolution. The performances of the method are analysed and compared with the conventional cross correlation with and without the effect of a window discrete offset. The assessment of performance through synthetic PIV images shows that a remarkable improvement can be obtained in terms of precision and dynamic range. Moreover, peak-locking effects do not affect the method in practice. The velocity gradient range accessed with the application of a relative window deformation (linear approximation) is significantly enlarged, as confirmed in the experimental results.

Automatic text detection and tracking in digital video

Abstract: Text that appears in a scene or is graphically added to video can provide an important supplemental source of index information as well as clues for decoding the video's structure and for classification. In this work, we present algorithms for detecting and tracking text in digital video. Our system implements a scale-space feature extractor that feeds an artificial neural processor to detect text blocks. Our text tracking scheme consists of two modules: a sum of squared difference (SSD) based module to find the initial position and a contour-based module to refine the position. Experiments conducted with a variety of video sources show that our scheme can detect and track text robustly.

Imaging spectroscopy using tunable filters: a review

Abstract: Major spin-offs from NASA's multi- and hyper spectral imaging remote sensing technology developed for Earth resources monitoring, are creative techniques that combine and integrate spectral with spatial methods. Such techniques are finding use in medicine, agriculture, manufacturing, forensics, and an e er expanding list of other applications. Many such applications are easier to implement using a sensor design different from the pushbroom or whiskbroom air- or space-borne counterparts. This need is met by using a variety of electronically tunable filters that are mounted in front of a monochrome camera to produce a stack of images at a sequence of wavelengths, forming the familiar 'image cube'. The combined spectral/spatial analysis offered by such image cubes takes advantage of tools borrowed form spatial image processing, chemometrics and specifically spectroscopy, and new custom exploitation tools developed specifically for these applications. Imaging spectroscopy is particularly useful for non homogeneous samples or scenes. examples include spatial classification based on spectral signatures, use of spectral libraries for material identification, mixture composition analysis, plume detection, etc. This paper reviews available tunable filters ,system design considerations, general analysis techniques for retrieving the intrinsic scene properties from the measurements, and applications and examples.

Robust template matching for affine resistant image watermarks

Abstract: Digital watermarks have been proposed as a method for discouraging illicit copying and distribution of copyrighted material. This paper describes a method for the secure and robust copyright protection of digital images. We present an approach for embedding a digital watermark into an image using the Fourier transform. To this watermark is added a template in the Fourier transform domain to render the method robust against general linear transformations. We detail a new algorithm based on polar maps for the accurate and efficient recovery of the template in an image which has undergone a general affine transformation. We also present results which demonstrate the robustness of the method against some common image processing operations such as compression, rotation, scaling, and aspect ratio changes.

Watermarking digital image and video data

Abstract: A chuck has a chuck body rotatable about an axis and formed with a plurality of angularly spaced radially extending inner guides and a plurality of angularly spaced and radially extending outer guides. An inner jaw part wholly received within the chuck body is radially displaceable in each of the inner guides and an outer jaw part projecting axially from the chuck body is radially displaceable in each of the outer guides. An operating element is engaged with all of the inner jaw parts to jointly radially displace them. A coupling member is axially displaceable in each of the inner jaw parts between a coupling position in which each of the inner parts is locked for joint radial movement with the respective outer part and an axially offset decoupling position allowing relative radial displacement of the outer jaw parts and the respective inner jaw parts. These coupling members may be independently operable, or jointly operable by means of a cam ring.

Low bit-rate scalable video coding with 3-D set partitioning in hierarchical trees (3-D SPIHT)

Abstract: We propose a low bit-rate embedded video coding scheme that utilizes a 3-D extension of the set partitioning in hierarchical trees (SPIHT) algorithm which has proved so successful in still image coding. Three-dimensional spatio-temporal orientation trees coupled with powerful SPIHT sorting and refinement renders 3-D SPIHT video coder so efficient that it provides comparable performance to H.263 objectively and subjectively when operated at the bit rates of 30 to 60 kbits/s with minimal system complexity. Extension to color-embedded video coding is accomplished without explicit bit allocation, and can be used for any color plane representation. In addition to being rate scalable, the proposed video coder allows multiresolutional scalability in encoding and decoding in both time and space from one bit stream. This added functionality along with many desirable attributes, such as full embeddedness for progressive transmission, precise rate control for constant bit-rate traffic, and low complexity for possible software-only video applications, makes the proposed video coder an attractive candidate for multimedia applications.

Parametric estimate of intensity inhomogeneities applied to MRI

Abstract: Presents a new approach to the correction of intensity inhomogeneities in magnetic resonance imaging (MRI) that significantly improves intensity-based tissue segmentation. The distortion of the image brightness values by a low-frequency bias field impedes visual inspection and segmentation. The new correction method called parametric bias field correction (PABIC) is based on a simplified model of the imaging process, a parametric model of tissue class statistics, and a polynomial model of the inhomogeneity field. The authors assume that the image is composed of pixels assigned to a small number of categories with a priori known statistics. Further they assume that the image is corrupted by noise and a low-frequency inhomogeneity field. The estimation of the parametric bias field is formulated as a nonlinear energy minimization problem using an evolution strategy (ES). The resulting bias field is independent of the image region configurations and thus overcomes limitations of methods based on homomorphic filtering. Furthermore, PABIC can correct bias distortions much larger than the image contrast. Input parameters are the intensity statistics of the classes and the degree of the polynomial function. The polynomial approach combines bias correction with histogram adjustment, making it well suited for normalizing the intensity histogram of datasets from serial studies. The authors present simulations and a quantitative validation with phantom and test images. A large number of MR image data acquired with breast, surface, and head coils, both in two dimensions and three dimensions, have been processed and demonstrate the versatility and robustness of this new bias correction scheme.

PicToSeek: combining color and shape invariant features for image retrieval

Abstract: We aim at combining color and shape invariants for indexing and retrieving images. To this end, color models are proposed independent of the object geometry, object pose, and illumination. From these color models, color invariant edges are derived from which shape invariant features are computed. Computational methods are described to combine the color and shape invariants into a unified high-dimensional invariant feature set for discriminatory object retrieval. Experiments have been conducted on a database consisting of 500 images taken from multicolored man-made objects in real world scenes. From the theoretical and experimental results it is concluded that object retrieval based on composite color and shape invariant features provides excellent retrieval accuracy. Object retrieval based on color invariants provides very high retrieval accuracy whereas object retrieval based entirely on shape invariants yields poor discriminative power. Furthermore, the image retrieval scheme is highly robust to partial occlusion, object clutter and a change in the object's pose. Finally, the image retrieval scheme is integrated into the PicToSeek system on-line at http://www.wins.uva.nl/research/isis/PicToSeek/ for searching images on the World Wide Web.

Traffic monitoring and accident detection at intersections

Abstract: We have developed an algorithm, referred to as spatio-temporal Markov random field, for traffic images at intersections. This algorithm models a tracking problem by determining the state of each pixel in an image and its transit, and how such states transit along both the x-y image axes as well as the time axes. Our algorithm is sufficiently robust to segment and track occluded vehicles at a high success rate of 93%-96%. This success has led to the development of an extendable robust event recognition system based on the hidden Markov model (HMM). The system learns various event behavior patterns of each vehicle in the HMM chains and then, using the output from the tracking system, identifies current event chains. The current system can recognize bumping, passing, and jamming. However, by including other event patterns in the training set, the system can be extended to recognize those other events, e.g., illegal U-turns or reckless driving. We have implemented this system, evaluated it using the tracking results, and demonstrated its effectiveness.

The art of frame theory

Abstract: The theory of frames for a Hilbert space plays a fundamental role in signal processing, image processing, data compression, sampling theory and more, as well as being a fruitful area of research in abstract mathematics. In this “tutorial” on abstract frame theory, we will try to point out the major directions of research in abstract frame theory and give some sample techniques from each of the areas. We will also bring out some of the important open questions, discuss some of the limitations of the existing theory, and point to some new directions for research.

Digital Image Processing Algorithms and Applications

Abstract: From the Publisher: Digital data acquired by scanners, radar systems, and digital cameras are typically computer processed to produce images through digital image processing. Through various techniques employing image processing algorithms, digital images can be enhanced for viewing and human interpretation. This book provides readers with a complete library of algorithms for digital image processing, coding, and analysis. Reviewing all facets of the technology, it is supplemented with an ftp site containing detailed lab exercises, PDF transparencies, and source code (all algorithms are presented in C-code).

An information-theoretic approach to spectral variability, similarity, and discrimination for hyperspectral image analysis

Abstract: A hyperspectral image can be considered as an image cube where the third dimension is the spectral domain represented by hundreds of spectral wavelengths. As a result, a hyperspectral image pixel is actually a column vector with dimension equal to the number of spectral bands and contains valuable spectral information that can be used to account for pixel variability, similarity and discrimination. We present a new hyperspectral measure, the spectral information measure (SIM), to describe spectral variability and two criteria, spectral information divergence and spectral discriminatory probability for spectral similarity and discrimination, respectively. The spectral information measure is an information-theoretic measure which treats each pixel as a random variable using its spectral signature histogram as the desired probability distribution. Spectral information divergence (SID) compares the similarity between two pixels by measuring the probabilistic discrepancy between two corresponding spectral signatures. The spectral discriminatory probability calculates spectral probabilities of a spectral database (library) relative to a pixel to be identified so as to achieve material identification. In order to compare the discriminatory power of one spectral measure relative to another, a criterion is also introduced for performance evaluation, which is based on the power of discriminating one pixel from another relative to a reference pixel. The experimental results demonstrate that the new hyperspectral measure can characterize spectral variability more effectively than the commonly used spectral angle mapper (SAM).