Showing papers on "Image processing published in 1999"

Splines: a perfect fit for signal and image processing

Abstract: The article provides arguments in favor of an alternative approach that uses splines, which is equally justifiable on a theoretical basis, and which offers many practical advantages. To reassure the reader who may be afraid to enter new territory, it is emphasized that one is not losing anything because the traditional theory is retained as a particular case (i.e., a spline of infinite degree). The basic computational tools are also familiar to a signal processing audience (filters and recursive algorithms), even though their use in the present context is less conventional. The article also brings out the connection with the multiresolution theory of the wavelet transform. This article attempts to fulfil three goals. The first is to provide a tutorial on splines that is geared to a signal processing audience. The second is to gather all their important properties and provide an overview of the mathematical and computational tools available; i.e., a road map for the practitioner with references to the appropriate literature. The third goal is to give a review of the primary applications of splines in signal and image processing.

N-FINDR: an algorithm for fast autonomous spectral end-member determination in hyperspectral data

Abstract: The analysis of hyperspectral data sets requires the determination of certain basis spectra called 'end-members.' Once these spectra are found, the image cube can be 'unmixed' into the fractional abundance of each material in each pixel. There exist several techniques for accomplishing the determination of the end-members, most of which involve the intervention of a trained geologist. Often these-end-members are assumed to be present in the image, in the form of pure, or unmixed, pixels. In this paper a method based upon the geometry of convex sets is proposed to find a unique set of purest pixels in an image. The technique is based on the fact that in N spectral dimensions, the N-volume contained by a simplex formed of the purest pixels is larger than any other volume formed from any other combination of pixels. The algorithm works by 'inflating' a simplex inside the data, beginning with a random set of pixels. For each pixel and each end-member, the end-member is replaced with the spectrum of the pixel and the volume is recalculated. If it increases, the spectrum of the new pixel replaces that end-member. This procedure is repeated until no more replacements are done. This algorithm successfully derives end-members in a synthetic data set, and appears robust with less than perfect data. Spectral end-members have been extracted for the AVIRIS Cuprite data set which closely match reference spectra, and resulting abundance maps match published mineral maps.

Multiresolution-based image fusion with additive wavelet decomposition

Abstract: The standard data fusion methods may not be satisfactory to merge a high-resolution panchromatic image and a low-resolution multispectral image because they can distort the spectral characteristics of the multispectral data. The authors developed a technique, based on multiresolution wavelet decomposition, for the merging and data fusion of such images. The method presented consists of adding the wavelet coefficients of the high-resolution image to the multispectral (low-resolution) data. They have studied several possibilities concluding that the method which produces the best results consists in adding the high order coefficients of the wavelet transform of the panchromatic image to the intensity component (defined as L=(R+G+B)/3) of the multispectral image. The method is, thus, an improvement on standard intensity-hue-saturation (IHS or LHS) mergers. They used the "a trous" algorithm which allows the use of a dyadic wavelet to merge nondyadic data in a simple and efficient scheme. They used the method to merge SPOT and LANDSAT/sup TM/ images. The technique presented is clearly better than the IHS and LHS mergers in preserving both spectral and spatial information.

Fuzzy Models and Algorithms for Pattern Recognition and Image Processing

Abstract: Pattern Recognition.- Cluster Analysis for Object Data.- Cluster Analysis for Relational Data.- Classifier Design.- Image Processing and Computer Vision.

Image enhancement based on equal area dualistic sub-image histogram equalization method

Abstract: Histogram equalization is a simple and effective image enhancing technique. But in some conditions, the luminance of an image may be changed significantly after the equalizing process, this is why it has never been utilized in a video system in the past. A novel histogram equalization technique, equal area dualistic sub-image histogram equalization, is put forward in this paper. First, the image is decomposed into two equal area sub-images based on its original probability density function. Then the two sub-images are equalized respectively. Finally, we obtain the results after the processed sub-images are composed into one image. The simulation results indicate that the algorithm can not only enhance the image information effectively but also preserve the original image luminance well enough to make it possible to be used in a video system directly.

Unsupervised classification using polarimetric decomposition and the complex Wishart classifier

Abstract: The authors propose a new method for unsupervised classification of terrain types and man-made objects using polarimetric synthetic aperture radar (SAR) data. This technique is a combination of the unsupervised classification based on polarimetric target decomposition, S.R. Cloude et al. (1997), and the maximum likelihood classifier based on the complex Wishart distribution for the polarimetric covariance matrix, J.S. Lee et al. (1994). The authors use Cloude and Pottier's method to initially classify the polarimetric SAR image. The initial classification map defines training sets for classification based on the Wishart distribution. The classified results are then used to define training sets for the next iteration. Significant improvement has been observed in iteration. The iteration ends when the number of pixels switching classes becomes smaller than a predetermined number or when other criteria are met. The authors observed that the class centers in the entropy-alpha plane are shifted by each iteration. The final class centers in the entropy-alpha plane are useful for class identification by the scattering mechanism associated with each zone. The advantages of this method are the automated classification, and the interpretation of each class based on scattering mechanism. The effectiveness of this algorithm is demonstrated using a JPL/AIRSAR polarimetric SAR image.

Blobworld: A System for Region-Based Image Indexing and Retrieval

Abstract: Blobworld is a system for image retrieval based on finding coherent image regions which roughly correspond to objects. Each image is automatically segmented into regions ("blobs") with associated color and texture descriptors. Queryingi s based on the attributes of one or two regions of interest, rather than a description of the entire image. In order to make large-scale retrieval feasible, we index the blob descriptions usinga tree. Because indexing in the high-dimensional feature space is computationally prohibitive, we use a lower-rank approximation to the high-dimensional distance. Experiments show encouraging results for both queryinga nd indexing.

Hidden digital watermarks in images

Abstract: An image authentication technique by embedding digital "watermarks" into images is proposed. Watermarking is a technique for labeling digital pictures by hiding secret information into the images. Sophisticated watermark embedding is a potential method to discourage unauthorized copying or attest the origin of the images. In our approach, we embed the watermarks with visually recognizable patterns into the images by selectively modifying the middle-frequency parts of the image. Several variations of the proposed method are addressed. The experimental results show that the proposed technique successfully survives image processing operations, image cropping, and the Joint Photographic Experts Group (JPEG) lossy compression.

Coherence-Enhancing Diffusion Filtering

Abstract: The completion of interrupted lines or the enhancement of flow-like structures is a challenging task in computer vision, human vision, and image processing. We address this problem by presenting a multiscale method in which a nonlinear diffusion filter is steered by the so-called interest operator (second-moment matrix, structure tensor). An m-dimensional formulation of this method is analysed with respect to its well-posedness and scale-space properties. An efficient scheme is presented which uses a stabilization by a semi-implicit additive operator splitting (AOS), and the scale-space behaviour of this method is illustrated by applying it to both 2-D and 3-D images.

Image processing with complex wavelets

Abstract: We first review how wavelets may be used for multi–resolution image processing, describing the filter–bank implementation of the discrete wavelet transform (DWT) and how it may be extended via separable filtering for processing images and other multi–dimensional signals. We then show that the condition for inversion of the DWT (perfect reconstruction) forces many commonly used wavelets to be similar in shape, and that this shape produces severe shift dependence (variation of DWT coefficient energy at any given scale with shift of the input signal). It is also shown that separable filtering with the DWT prevents the transform from providing directionally selective filters for diagonal image features. Complex wavelets can provide both shift invariance and good directional selectivity, with only modest increases in signal redundancy and computation load. However, development of a complex wavelet transform (CWT) with perfect reconstruction and good filter characteristics has proved difficult until recently. We now propose the dual–tree CWT as a solution to this problem, yielding a transform with attractive properties for a range of signal and image processing applications, including motion estimation, denoising, texture analysis and synthesis, and object segmentation.

Adaptive fuzzy segmentation of magnetic resonance images

Abstract: An algorithm is presented for the fuzzy segmentation of two-dimensional (2-D) and three-dimensional (3-D) multispectral magnetic resonance (MR) images that have been corrupted by intensity inhomogeneities, also known as shading artifacts. The algorithm is an extension of the 2-D adaptive fuzzy C-means algorithm (2-D AFCM) presented in previous work by the authors. This algorithm models the intensity inhomogeneities as a gain field that causes image intensities to smoothly and slowly vary through the image space. It iteratively adapts to the intensity inhomogeneities and is completely automated. In this paper, the authors fully generalize 2-D AFCM to three-dimensional (3-D) multispectral images. Because of the potential size of 3-D image data, they also describe a new faster multigrid-based algorithm for its implementation. They show, using simulated MR data, that 3-D AFCM yields lower error rates than both the standard fuzzy C-means (FCM) algorithm and two other competing methods, when segmenting corrupted images. Its efficacy is further demonstrated using real 3-D scalar and multispectral MR brain images.

Real-time elastic deformations of soft tissues for surgery simulation

Abstract: We describe a novel method for surgery simulation including a volumetric model built from medical images and an elastic modeling of the deformations. The physical model is based on elasticity theory which suitably links the shape of deformable bodies and the forces associated with the deformation. A real time computation of the deformation is possible thanks to a preprocessing of elementary deformations derived from a finite element method. This method has been implemented in a system including a force feedback device and a collision detection algorithm. The simulator works in real time with a high resolution liver model.

Face segmentation using skin-color map in videophone applications

Abstract: This paper addresses our proposed method to automatically segment out a person's face from a given image that consists of a head-and-shoulders view of the person and a complex background scene. The method involves a fast, reliable, and effective algorithm that exploits the spatial distribution characteristics of human skin color. A universal skin-color map is derived and used on the chrominance component of the input image to detect pixels with skin-color appearance. Then, based on the spatial distribution of the detected skin-color pixels and their corresponding luminance values, the algorithm employs a set of novel regularization processes to reinforce regions of skin-color pixels that are more likely to belong to the facial regions and eliminate those that are not. The performance of the face-segmentation algorithm is illustrated by some simulation results carried out on various head-and-shoulders test images. The use of face segmentation for video coding in applications such as videotelephony is then presented. We explain how the face-segmentation results can be used to improve the perceptual quality of a videophone sequence encoded by the H.261-compliant coder.

A categorization of multiscale-decomposition-based image fusion schemes with a performance study for a digital camera application

Abstract: The objective of image fusion is to combine information from multiple images of the same scene. The result of image fusion is a single image which is more suitable for human and machine perception or further image-processing tasks. In this paper, a generic image fusion framework based on multiscale decomposition is studied. This framework provides freedom to choose different multiscale decomposition methods and different fusion rules. The framework includes all of the existing multiscale-decomposition-based fusion approaches we found in the literature which did not assume a statistical model for the source images. Different image fusion approaches are investigated based on this framework. Some evaluation measures are suggested and applied to compare the performance of these fusion schemes for a digital camera application. The comparisons indicate that our framework includes some new approaches which outperform the existing approaches for the cases we consider.

Spread spectrum image steganography

Abstract: We present a new method of digital steganography, entitled spread spectrum image steganography (SSIS). Steganography, which means "covered writing" in Greek, is the science of communicating in a hidden manner. Following a discussion of steganographic communication theory and review of existing techniques, the new method, SSIS, is introduced. This system hides and recovers a message of substantial length within digital imagery while maintaining the original image size and dynamic range. The hidden message can be recovered using appropriate keys without any knowledge of the original image. Image restoration, error-control coding, and techniques similar to spread spectrum are described, and the performance of the system is illustrated. A message embedded by this method can be in the form of text, imagery, or any other digital signal. Applications for such a data-hiding scheme include in-band captioning, covert communication, image tamperproofing, authentication, embedded control, and revision tracking.

Digital volume correlation : Three-dimensional strain mapping using X-ray tomography

Abstract: A three-dimensional extension of two-dimensional digital image correlation has been developed. The technique uses digital image volumes generated through high-resolution X-ray tomography of samples with microarchitectural detail, such as the trabecular bone tissue found within the skeleton. Image texture within the material is used for displacement field measurement by subvolume tracking. Strain fields are calculated from the displacement fields by gradient estimation techniques. Estimates of measurement precision were developed through correlation of repeat unloaded data sets for a simple sum-of-squares displacement-only correlation formulation. Displacement vector component errors were normally distributed, with a standard deviation of 0.035 voxels (1.22 μm). Strain tensor component errors were also normally distributed, with a standard deviation of approximately 0.0003. The method was applied to two samples taken from the thigh bone near the knee. Strains were effectively measured in both the elastic and postyield regimes of material behavior, and the spatial patterns showed clear relationships to the sample microarchitectures.

Rate control in DCT video coding for low-delay communications

Abstract: An important motivation for the development of the emerging H.263+ and MPEG-4 coding standards is to enhance the quality of highly compressed video for two-way, real-time communications. In these applications, the delay produced by bits accumulated in the encoder buffer must be very small, typically below 100 ms, and the rate control strategy is responsible for encoding the video with high quality and maintaining a low buffer delay. In this work, we present a simple rate control technique that achieves these two objectives by smartly selecting the values of the quantization parameters in typical discrete cosine transform video coders. To do this, we derive models for bit rate and distortion in this type of coders, in terms of the quantization parameters. Using Lagrange optimization, we minimize distortion subject to the target bit constraint, and obtain formulas that indicate how to choose the quantization parameters. We implement our technique in H.263 and MPEG-4 coders, and compare its performance to TMN7 and VM7 rate control when the encoder buffer is small, for a variety of video sequences and bit rates. This new method has been adopted as a rate control tool in the test model TMN8 of H.263+ and (with some modifications) in the verification model VM8 of MPEG-4.

Real-time 3D image registration for functional MRI.

Abstract: Subject head movements are one of the main practical difficulties with brain functional MRI. A fast, accurate method for rotating and shifting a three-dimensional (3D) image using a shear factorization of the rotation matrix is described. Combined with gradient descent (repeated linearization) on a least squares objective function, 3D image realignment for small movements can be computed as rapidly as whole brain images can be acquired on current scanners. Magn Reson Med 42:1014-1018, 1999.

Perceptual watermarks for digital images and video

Abstract: The growth of new imaging technologies has created a need for techniques that can be used for copyright protection of digital images and video. One approach for copyright protection is to introduce an invisible signal, known as a digital watermark, into an image or video sequence. In this paper, we describe digital watermarking techniques, known as perceptually based watermarks, that are designed to exploit aspects of the the human visual system in order to provide a transparent (invisible), yet robust watermark. In the most general sense, any watermarking technique that attempts to incorporate an invisible mark into an image is perceptually based. However, in order to provide transparency and robustness to attack, two conflicting requirements from a signal processing perspective, more sophisticated use of perceptual information in the watermarking process is required. We describe watermarking techniques ranging from simple schemes which incorporate common-sense rules in using perceptual information in the watermarking process, to more elaborate schemes which adapt to local image characteristics based on more formal perceptual models. This review is not meant to be exhaustive; its aim is to provide the reader with an understanding of how the techniques have been evolving as the requirements and applications become better defined.

MRI simulation-based evaluation of image-processing and classification methods

Abstract: With the increased interest in computer-aided image analysis methods, there is a greater need for objective methods of algorithm evaluation. Validation of in vivo MRI studies is complicated by a lack of reference data and the difficulty of constructing anatomically realistic physical phantoms. The authors present here an extensible MRI simulator that efficiently generates realistic three-dimensional (3-D) brain images using a hybrid Bloch equation and tissue template simulation that accounts for image contrast, partial volume, and noise. This allows image analysis methods to be evaluated with controlled degradations of image data.

NeTra: a toolbox for navigating large image databases

Abstract: We present here an implementation of NeTra, a prototype image retrieval system that uses color, texture, shape and spatial location information in segmented image regions to search and retrieve similar regions from the database. A distinguishing aspect of this system is its incorporation of a robust automated image segmentation algorithm that allows object- or region-based search. Image segmentation significantly improves the quality of image retrieval when images contain multiple complex objects. Images are segmented into homogeneous regions at the time, of ingest into the database, and image attributes that represent each of these regions are computed. In addition to image segmentation, other important components of the system include an efficient color representation, and indexing of color, texture, and shape features for fast search and retrieval. This representation allows the user to compose interesting queries such as "retrieve all images that contain regions that have the color of object A, texture of object B, shape of object C, and lie in the upper of the image", where the individual objects could be regions belonging to different images. A Java-based web implementation of NeTra is available at http://vivaldi.ece.ucsb.edu/Netra.

Statistics of natural images and models

Abstract: Large calibrated datasets of 'random' natural images have recently become available. These make possible precise and intensive statistical studies of the local nature of images. We report results ranging from the simplest single pixel intensity to joint distribution of 3 Haar wavelet responses. Some of these statistics shed light on old issues such as the near scale-invariance of image statistics and some are entirely new. We fit mathematical models to some of the statistics and explain others in terms of local image features.

Image Processing: The Fundamentals

Abstract: From the Publisher: This book introduces the mathematical foundations of image processing in the context of specific applications in the four main themes: image enhancement, image compression, image restoration, and preparation of an image for automatic vision. This is the only teaching book on the market which deals with the topics in depth and great detail. All concepts are demonstrated with the help of small images that can be worked out by hand, and includes worked-out examples on all topics.

Fair benchmark for image watermarking systems

Abstract: Since the early 90s a number of papers on 'robust' digital watermarking systems have been presented but none of them uses the same robustness criteria. This is not practical at all for comparison and slows down progress in this area. To address this issue, we present an evaluation procedure of image watermarking systems. First we identify all necessary parameters for proper benchmarking and investigate how to quantitatively describe the image degradation introduced by the watermarking process. For this, we show the weaknesses of usual image quality measures in the context watermarking and propose a novel measure adapted to the human visual system. Then we show how to efficiently evaluate the watermark performance in such a way that fair comparisons between different methods are possible. The usefulness of three graphs: 'attack vs. visual-quality,' 'bit-error vs. visual quality,' and 'bit-error vs. attack' are investigated. In addition the receiver operating characteristic (ROC) graphs are reviewed and proposed to describe statistical detection behavior of watermarking methods. Finally we review a number of attacks that any system should survive to be really useful and propose a benchmark and a set of different suitable images.

Image compression via joint statistical characterization in the wavelet domain

Abstract: We develop a probability model for natural images, based on empirical observation of their statistics in the wavelet transform domain. Pairs of wavelet coefficients, corresponding to basis functions at adjacent spatial locations, orientations, and scales, are found to be non-Gaussian in both their marginal and joint statistical properties. Specifically, their marginals are heavy-tailed, and although they are typically decorrelated, their magnitudes are highly correlated. We propose a Markov model that explains these dependencies using a linear predictor for magnitude coupled with both multiplicative and additive uncertainties, and show that it accounts for the statistics of a wide variety of images including photographic images, graphical images, and medical images. In order to directly demonstrate the power of the model, we construct an image coder called EPWIC (embedded predictive wavelet image coder), in which subband coefficients are encoded one bitplane at a time using a nonadaptive arithmetic encoder that utilizes conditional probabilities calculated from the model. Bitplanes are ordered using a greedy algorithm that considers the MSE reduction per encoded bit. The decoder uses the statistical model to predict coefficient values based on the bits it has received. Despite the simplicity of the model, the rate-distortion performance of the coder is roughly comparable to the best image coders in the literature.