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Traditional image processing techniques have proven inadequate for urban mapping using high spatial resolution remotesensing images. This study examined and evaluated wavelet transforms for urban texture analysis and image classification using high spatial resolution ATLAS imagery. For the purpose of comparison and to evaluate the effectiveness of the wavelet approaches, two different fractal approaches (isarithm and triangular prism), spatial autocorrelation (Moran’s I and Geary’s C), and spatial co-occurrence matrix of the selected urban classes were examined using 65 � 65, 33 � 33, and 17 � 17 samples with a pixel size of 2.5 m. Results from this study suggest that a multi-band and multi-level wavelet approach can be used to drastically increase the classification accuracy. The fractal techniques did not provide satisfactory classification accuracy. Spatial autocorrelation and spatial co-occurrence techniques were found to be relatively effective when compared to the fractal approaches. It can be concluded that the wavelet transform approach is the most accurate of all four approaches.

Texture or spatial arrangement of neighborhood objects and features plays an important role in the human visual system for pattern recognition and image classification. The traditional spectral–based image processing techniques have proven inadequate for urban land use and land cover mapping from images acquired by the current generation of fine–resolution satellites. This is because of the high frequency spatial arrangements or complex nature of urban features. There is a need for an effective algorithm to digitally classify urban land use and land cover categories using high–resolution image data. Recent studies using wavelet transforms for texture analysis have generally reported better accuracy. Based on a high–resolution ATLAS image, this study illustrates four different wavelet decomposition procedures – the standard, horizontal, vertical, and diagonal decompositions – for urban land use and land cover feature extraction with the use of 33×33 pixel samples. The standard decomposition approach was found to be the most efficient approach in urban texture analysis and classification. For comparison purposes and to better evaluate the accuracy of wavelet approaches in image classification, spatial autocorrelation techniques (Moran’s I and Geary’s C) and the spatial co–occurrence matrix method were also examined. The results suggest that the wavelet transform approach is superior to all other approaches.

This paper presents a three-dimensional (3D) adaptive finite element solution for gas flow in a porous media. The solution obtained is truly 3D and employs a dynamic h-adaptive refinement technique for efficiency. The adaptive procedure uses a new node-based storage mechanism [Wang GH, Tyler JM, Weltman JS, Callahan JD. Advances in Engineering Software including Computing Systems in Engineering 1999;30:31–41]. This node-based structure substantially reduces the memory necessary to store the finite element mesh. A prototype simulator, written in C++, has been implemented for Eugene Island block 305, a multi-well condensate reservoir off the coast of Louisiana to demonstrate the use of this adaptive procedure. Results, presented in this paper, show dramatic agreement with the actual production data. This prototype simulator uses Windows workstations to support a fully dynamic 3D mesh plus mesh generation.

We report the results of an all electron self-consistent calculation of the electronic structure of ${\mathrm{CrO}}_{2}$ in the local spin-density approximation, employing an extended version of the linear combination of Gaussian orbitals method. ${\mathrm{CrO}}_{2}$ is found to be a half-metallic ferromagnet in agreement with previous calculations and recent experiments. The predicted Fermi surface is described in detail.

This paper presents a fast, accurate and automatic method to register images of rigid bodies. It uses wavelets to obtain control points. Wavelets are not shift invariant but the structures determined by the wavelet high pass image, the Modulus Maxima Image, provide the information necessary for a fast-rough convergence. These structures represent shapes from which we segment the invariant shapes for the images being registered. For example, the MRI and CT images of the brain can be considered as rigid bodies that do not undergo a change in shape over reasonable periods of time. By using a convex hull, we make the procedure insensitive to the internal changes in the object. Hence, even with the growth of tumors, the procedure registers brain images very accurately. The method uses the correlation coefficient to measure the similarity between images and to determine how well the images are registered. The method has been extensively tested with various types of images and in all cases the registration accuracy is very high. The correlation coefficient used to validate the registrations has deficiencies that occasionally required a visual inspection to terminate the algorithm after a point of marginal improvement.

Similar images are images with common features, similar pixel distributions, and similar edge distributions. Fields such as medical imaging or satellite imaging often need to store large collections of similar images. In a set of similar images the image similarities represent patterns that consistently appear across all images; this results in ''set redundancy''. This paper presents the Centroid method that extracts and uses these similarity patterns to reduce set redundancy and achieve higher lossless compression in sets of similar images. Experimental results with a medical image database demonstrate that the Centroid method can deliver significantly improved image compression.

A new finite element, viable for use in the three-dimensional simulation of transient physical processes with sharply varying solutions, is presented. The element is intended to function in adaptive h-refinement schemes as a versatile transition between regions of different refinement levels, ensuring interelement continuity by constructing a piecewise linear solution at the element boundaries, and retaining all degrees of freedom in the solution phase. Construction of the element shape functions is described, and a numerical example is presented which illustrates the advantages of using such an element in an adaptive refinement problem. The new element can be used in moving-front problems, such as those found in reservoir engineering and groundwater flow applications.

Purpose To develop a method for efficient automatic correction of slow-varying nonuniformity in MR images. Materials and Methods The original MR image is represented by a piecewise constant function, and the bias (nonuniformity) field of an MR image is modeled as multiplicative and slow varying, which permits to approximate it with a low-order polynomial basis in a “log-domain.” The basis coefficients are determined by comparing partial derivatives of the modeled bias field with the original image. Results We tested the resulting algorithm named derivative surface fitting (dsf) on simulated images and phantom and real data. A single iteration was sufficient in most cases to produce a significant improvement to the MR image's visual quality. dsf does not require prior knowledge of intensity distribution and was successfully used on brain and chest images. Due to its design, dsf can be applied to images of any modality that can be approximated as piecewise constant with a multiplicative bias field. Conclusion The resulting algorithm appears to be an efficient method for fast correction of slow varying nonuniformity in MR images. J. Magn. Reson. Imaging 2006;. © 2006 Wiley-Liss, Inc.

A wavelet-lifting scheme that maps integers to integers, performs all calculations in-place, and is computationally efficient is used in this paper. It processes 2-D medical images row by row producing an equivalent 1-D signal. The interdependency of pixels in 2-D medical images is known to vary in different regions. Thus, some lifting schemes decorrelate the resultant signal more efficiently than others do. The effect of different scanning approaches on the performance of several lifting schemes is presented.

Lossless compression techniques are essential in archival and communication of large amounts of homogeneous data in radiological image databases. This paper exploits dependencies that exist between the pixel intensities in three dimensions to improve compression for a set of similar medical images. These 3-D dependencies are systematically presented as histograms, plots of wavelet decomposition coefficients, feature vectors of wavelet decomposition coefficients, entropy and correlation. This 3-D dependency is called set redundancy for medical image sets. Predictive coding is adapted to set redundancy and combined with integer wavelet transformations to improve compression. This set compression improvement is demonstrated with 3-D sets of magnetic resonance (MR) brain images.

This paper presents current results for out method for the estimation of oceanic surface velocity fields using wavelet decomposition of Sea Surface Temperature images (SST) images of the same region taken within a know time interval. Wavelet decompositions are performed on both images to obtain approximations that will be compared for local displacements at various resolution levels. The velocity vector field generated form coarser images is refined by the addition of velocity vector components at more detailed resolution levels. Results of the technique applied to artificial and satellite images are presented.

Diagnostically lossless compression techniques are essential in archival and communication of medical images. In this paper, an automated wavelet-based background noise removal method, i.e. diagnostically lossless compression method, is proposed. First, the wavelet transform modulus maxima procedure products the modulus maxima image which contains sharp changes in intensity that are used to locate the edges of the images. Then the Graham Scan algorithm is used to determine the convex hull of the wavelet modulus maxima image and extract the foreground of the image, which contains the entire diagnostic region of the image. Histogram analyses are applied to the non-diagnostic region, which is approximated by the image that is outside the convex hull. After setting all pixels in the non-diagnostic region to zero intensity, a higher compression ratio, without introducing loss of any data used for the diagnosis, is achieved with UNIX utilities compress and pack, and with lossless JPEG. Furthermore, an image of smaller rectangular region containing all the diagnostic region is constructed to further improve the compression ratio achieved.© (2000) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.

There is a great amount of similarity in a set of medical images. Set Redundancy Compression (SRC) has shown that compression of a similar set of images can provide better compression than the compression obtained from compressing the individual images of the set. SRC is based on the prediction of the other images in the set from a smaller subset (this subset can be as small as one image). This paper presents a new wavelet based prediction method for prediction of the intermediate images in a similar set of medical images. The technique uses the correlation between coefficients in the wavelet transforms of the image set to produce a better image prediction compared to direct image prediction.

A major problem associated with a `film-less hospital' is the amount of digital image data that is generated and stored. Image compression must be used to reduce the storage size. Most current image compression methods were developed for the compression of single images. A new compression that uses similar image set redundancy and minimal numbers of orthogonal features can be used to efficiently compress medical images. As presented in this paper, wavelet analysis, principal component analysis, and statistical recrimination can be successfully used to optimally denote image differences and achieve efficient similar set image compression for medical images.© (1999) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.

This paper presents a new method for the estimation of oceanic surface velocity fields using wavelet decomposition of Sea Surface Temperature images (SST). This method requires the use of two SST images of the same region taken within a known time interval. Wavelet decompositions are performed on both images to obtain approximations that will be compared for local displacements. These local displacements are smoothed to eliminate noise and to generate a rough initial estimate of the velocity field. Then this estimate is iteratively refined by a similar analysis over more detailed images produced by inverse wavelet transforms. A description of the method and its assumptions are presented with initial results.

This paper addresses efficient parallel compression and classification for sets of similar images that are normally generated from satellite imagery, medical imaging (CT and MR scans) or aerial surveillance. From our experiments it was observed that image similarities for each class of images can be more efficiently expressed in the domain of image compressing transforms. In particular, the paper shows that only one predictive compressing model can be constructed for the entire class of similar images of the same nature, and then used for nearly optimal compression of any image of the class. The extraction of the optimal class-compressing model still remains a computationally intensive process, which can be considerably improved on parallel computers. The paper demonstrates how a similar database compressing model can be extracted in parallel, and how this can be used for parallel similar database compression and classification of new images into appropriate similarity classes. The results of the parallel similar image analysis are demonstrated with MR and CT brain images obtained from the M.D. Anderson Cancer Center.© (1998) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.

The paper studies the use of common autoregressive models for a similar database compression and estimates compression ratio for predictive models based on correlation coefficient.© (1998) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.

The paper demonstrates the existence of a common autoregressive (CAR) compressing transform for a class of similar images, and examines the model sensitivity with respect to translations and rotations of the similar images.

Compression is based on the removal of redundancy inherent in most images. Most of the work in the area of image compression has been directed at removing the redundancy within the same image. However, if we examine similar images from MRI scans, or CT scans, or aerial images, from surveillance photos; there is often similarity between these images. Removing this inter-image redundancy is the focus of 'set compression' techniques. However, when similar images are registered their compression ratios are much higher, using set compression techniques. In order to register images we consider the Wavelet Modulus Maxima to determine the important control points which provide shift invariant structures. Using these structures and control points form different images in a similar set of images, we register the images using the homologous set of points. By suing the difference image we are able to exploit the inter-image redundancy. The difference image can be further compressed by any other available compression technique to further increase the compression ratio. Since we are dealing with sets of images, these applications are well suited for using parallel architectures. For example in similar sets of medical images, there is a great potential to exploit the inherent parallelism is the process, by working on several images simultaneously. We make use of several nodes on each image as well as process several images in parallel. This paper discusses the issues related to the parallel implementation of our image registration and compression algorithm. This paper also discussed the results and details the gains obtained in parallel implementation using several nodes.© (1997) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.

Registration of images is of great importance in the fields of aerial surveillance, automatic target recognition, machine vision and medical imaging. Wavelets are tools that are being used to analyze signals and images. Wavelets in some sense are alternatives to Fourier transforms. Wavelets provide excellent time and frequency localization properties when compared to Fourier transforms. Singularities and irregular structures carry very important information about edges. Wavelets are excellent tools for detecting these singularities and characterizing the regularity of the function using Lipschitz exponents as shown by mallat et. al. In this paper we use the wavelet modulus maxima which is the strict local maxima of the modulus of the wavelet of the modulus of the wavelet transform to locate the singularities at each scale. homologous points from two similar images are then used to register the images using a best fit criteria. The objective function being that the difference image is a minimum image. The technique exploits inter image redundancy in addition to the intra image redundancy in sets of similar images after they have been registered. This lead to higher compression ratios. It also permits the use of any existing compression technique to exploit intra image redundancy.© (1997) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.

Utilisation de la combinaison lineaire des orbitales gaussiennes. Etude de la structure de bande, des densites d'etats, de la surface de Fermi, des profiles de Compton, et de la conductivite optique. Comparaisons entre l'experience et les calculs

Fourier-based approaches to calculate the Fresnel diffraction of light provide one of the most efficient algorithms for holographic computations because this permits the use of the fast Fourier transform (FFT). This research overcomes the limitations on sampling imposed by Fourier-based algorithms by the development of a fast shifted Fresnel transform. This fast shifted Fresnel transform is used to develop a tiling approach to hologram construction and reconstruction, which computes the Fresnel propagation of light between parallel planes having different resolutions.

Due to ever increasing costs, the use of a systematic procedure for planning the transportation needs of urban school systems is mandatory. One such method is to apply optimization techniques utilizing a digital computer for the purpose of mathematically modeling the school district network, and after defining stop locations, provide an initial set of near-optimal routes. Following this, the routes may then be manually modified to remove any trip inconsistencies or illogical routings. Finally, to maintain a current status for the mathematical model and student population patterns, a continuing information updating program can be instituted. The effect of using a procedure, as described previously, on an actual school district was to significantly improve the operational characteristics of the routing, and thereby, reduce the overall cost of providing the required transportation service.

The faculty and administration of Louisiana State University recognized that word processing and text processors could facilitate and enhance our teaching, research, service, and administrative operations; however, we were concerned about the rapid proliferation of limited-function systems being purchased to meet critical present needs without a systematic plan for continued expansion. The unit cost for stand-alone word processors was usually rather high and the interactive or expansion capabilities were very limited; therefore, we sought to define a system or systems upon which we could base our developments in the areas of text processing utilizing current technology and which could expand with future developments.

How do I do....? Where do I get....? What do I do....? These are some of the kinds of general questions that have flooded the Computer Center ever since it was first formed. While the nature of the questions has switched from cards to terminals and from batch to on-line, they still keep pouring in. These same questions are still being asked over and over again by both administrative and academic customers. In the past, the answer, right or wrong, depended as much upon the person providing the answer and how much they knew as it did on anything else. Also, customers may have been routed to several different individuals before their question was answered.