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Maximum likelihood detector algorithms are developed for the matrix of transmitted symbols in a multiuser system in which the received signal is the sum of K cochannel continuous phase modulated (CPM) signals and additive white Gaussian noise. We illustrate that the maximum likelihood matrix detector, which provides optimum detector performance, consists of K sets of front-end matched filters followed by a Viterbi algorithm. We also derive two reduced complexity receivers, demonstrating through simulation that they perform within 1-2 dB of the optimal while substantially reducing complexity. The paper demonstrates how performance can be traded off against complexity, giving particular attention to cochannel Gaussian minimum shift keyed (GMSK) signals

We describe algorithms for automating the process of picking seismic events in prestack migrated common depth image gathers. The approach uses supervised learning and statistical classification algorithms along with advanced signal/image processing algorithms. No model assumption is made, such as hyperbolic moveout. We train a probabilistic neural network for voxel classification using event times, subsurface points, and offsets (ground truth information) picked manually by expert interpreters. The key to success is using effective features that capture the important behavior of the measured signals. We test a variety of features calculated in a local neighborhood about the voxel under analysis. Selection algorithms ensure that we use only the features that maximize class separability. This event‐picking algorithm has the potential to reduce significantly the cycle time and cost of 3‐D prestack depth migration while making the velocity model inversion more robust.

Mean curvature diffusion is shown to be a position vector diffusion, tending to scalar diffusion as a flat image region is approached, and providing noise removal by steepest descent surface minimization. At edges, it switches to a nondiffusion state due to two factors: the Laplacian of position vanishes and the magnitude of the surface normal attains a local maximum.

Proposes a geometrical surface representation for an image, and introduces a nonlinear adaptive filter that diffuses the surface in time at a speed proportional to the mean curvature. Linear variations in intensity (edges) are inclined planes of vanishing mean curvature, and are thus invariant. Noise is characterized by high mean curvature and will be diffused. The authors show that this diffusion resolves the conflict of removing noise while preserving edges. A novel nonlinear scale space filtering relating surface area to the diffusion speed is introduced resulting in very efficient algorithms. Experiments demonstrating excellent performance and efficiency are presented. >

We have developed a computer-aided system (Bony Parts) to analyze periodic bands in fish otoliths (or other structures) for age estimation. The image analysis program first scans the image of a thin otolith section, perpendicular to the bands specified by the user. Adjacent scans are averaged and filtered with Fourier transformation or spatial domain convolution. Bands of higher density are detected and are marked and summed on the screen. We evaluated this new technique using subsamples of thin-sectioned otoliths from the bank rockfish Sebastes rufus. The time and effort for cleaning, preparation, sectioning, and mounting are the same for both traditional and computer-aided techniques. The computer-aided technique reduced the time and tedium of counting bands, yet still allowed the user to interactively make subjective decisions about aging criteria. Both approaches produced similar readings, but computer-aided estimates were more precise than traditional readings and required less analysis time...

A network model is proposed for invariant object recognition. The overall complexity of the weight connection is lower than that of several established networks. The performance of the proposed model is comparable to that obtained with Zernike moments.

We develop and evaluate receiver signal practising algorithms for the detection of signals transmitted via the forward link of a cell in a cellular system modeled after the IS-95 standard for direct-sequence spread-spectrum code-division multiple-access (CDMA) communications. Multiuser detectors on board airborne and terrestrial motile interceptors or monitors attempt the simultaneous detection, in a single receiver, of all communication signals transmitted by the base station of interest. Due to the detrimental effects of transmitter, receiver and channel nonlinearities, very fast multipath fading, shadowing, path loss, Doppler spread, additive white Gaussian noise, and intracell and intercell multiple-access interference, the user signals are de-orthogonalized. This leads to performance degradation in conventional receivers that is too severe, especially when the powers of some of the interfering users are dominant. In order to improve upon the performance of conventional matched filter receivers, this article focuses on the development and evaluation of fast and reliable successive interference canceling (SIC) algorithms. The techniques we have developed can be used to enable successful interception of CDMA signals; to relax the strict requirements on power control; and to improve the capacity of CDMA systems.

We introduce a new theory relating the magnitude of the image surface normal to an inhomogeneous diffusion that solely diffuses (averages) the mean curvature of the image surface. We discuss the remarkable properties of this diffusion stressing the regularity it imposes on regions and boundaries while preserving the locality of edges and lines. Experiments demonstrating the excellent performance of the algorithms in the areas of noise removal and enhancement are presented. >

We describe the properties of algorithms based on methods of inhomogeneous diffusion and their application to image and video coding. Filtering by inhomogeneous diffusion has the desirable property of reducing noise and other artifacts while preserving the image structure, maintaining or improving image quality. As a preprocessor, this filtering removes non-perceptible noise, leading to improved compression. As a postprocessor, it suppresses coding artifacts, such as the block impairments of DCT-based coders. Maintaining the basic structure of JPEG, MPEG, and other coders, we describe the improvements possible with inhomogeneous diffusion preprocessing and postprocessing.

Proposes a perceptual Wiener filter for image restoration, a linear space-variant filter which accounts for the human visual system response to image details and noise in the vicinity of an edge. This filter provides a reduction in the ringing artifact observed in the vicinity of edges, when compared to the response of the widely used linear space-invariant classical Wiener filter. A fast, approximate implementation of the filter is discussed. >

A new formulation for inhomogeneous image diffusion is presented in which the image is regarded as a surface in 3-space. The evolution of this surface under diffusion is analyzed by classical methods of differential geometry. A nonlinear filtering theory is introduced in which only the divergence of the direction of the surface gradient is averaged. This averaging preserves edges and lines, as their direction is non-divergent, while noise is averaged since it does not have non-divergent consistency. Our approach achieves this objective by evolving the surface at a speed proportional to mean curvature leading to the minimization of the surface area and the imposition of regularity everywhere. Furthermore, we introduce a new filter that renders corners, as well as edges, invariant to the diffusion process. Experiments demonstrating the adequacy of this new theory are presented. >

Some preliminary results are presented on a simple progressive code for scanned 300 dpi documents. The effectiveness of the standard CCITT codes as a function of document resolution is determined. Extensions to the technique of document resolution are examined. For higher-resolution scanning, e.g. 300 dpi, a pyramid of the information source which allows for a tradeoff between the compression achieved and the quality of the representation is proposed. A progressive code that first generates a color block image that may be useful for identification of the document is proposed. Progression in quality corresponds to 100 dpi binary images, 100 dpi gray scale images, 300 dpi binary images, and finally 300 dpi gray-scale images. Each stage in the progressive code is based on the data available at the previous stage. The performance of the progressive code is examined. >

An approach to automatic target cueing (ATC) in hyperspectral images, referred to as K-means reclustering, is introduced. The objective is to extract spatial clusters of spectrally related pixels having specified and distinctive spatial characteristics. K-means reclustering has three steps: spectral cluster initialization, spectral clustering and spatial re-clustering, plus an optional dimensionality reduction step. It provides an alternative to classical ATC algorithms based on anomaly detection, in which pixels are classified as type anomaly or background clutter. K-means reclustering is used to cue targets of various sizes in AVIRIS imagery. Statistical performance and computational complexity are evaluated experimentally as a function of the designated number of spectral classes (K) and the initially specified spectral cluster centers.

We present an optimum multiuser receiver for detecting K cochannel asynchronous partial-response pulse amplitude modulated (PAM) signals. The receiver generalizes the optimum multiuser detector developed by Verdu (1983, 1986) for asynchronous full-response PAM signals and the optimum single-user receiver developed by Ungerboeck (1974) for the detection of ISI-corrupted PAM signals.

In this paper, we develop optimum and suboptimum receivers for jointly detecting two cochannel continuous phase modulated (CPM) signals. These receivers are based upon Laurent’s representation of binary CPM as the sum of a finite number of pulse amplitude modulated signals. We also provide a review of the Laurent representation and its application to the design of optimum and suboptimum single-channel receivers.

We describe algorithms for automating the process of picking seismic events in pre-stack migrated gathers. The approach uses supervised learning and statistical classification algorithms along with advanced signal-image processing algorithms. We train a probabilistic neural network (PNN) for pixel classification using event times and offsets (ground truth information) picked manually by expert interpreters. The key to success is in using effective features that capture the important behavior of the measured signals. We use a variety of features calculated in a local neighborhood about the pixel under analysis. Feature selection algorithms are used to ensure that we use only the features that maximize class separability. The novelty of the work lies in (a) the use of pre-stack migrated gathers rather than stacked data, (b) the use of two-dimensional statistical and wavelet features, and (c) the use of a PNN for classification. 8 refs., 3 figs

Coding techniques, such as JPEG and MPEG, result in visibly degraded images at high compression. The coding artifacts are strongly correlated with image features and result in objectionable structured errors. Among structured errors, the reduction of the end of block effect in JPEG encoding has received recent attention, with advantage being taken of the known location of block boundaries. However, end of block errors are not apparent in subband or wavelet coded images. Even for JPEG coding, end of block errors are not perceptible for moderate compression, while other artifacts are still quite apparent and disturbing. In previous work, we have shown that the quality of images can be in general improved by analysis based processing and interpolation. In this paper, we present a new approach that addresses the reduction of the end of block errors as well as other visible artifacts that persist at high image quality. We demonstrate that a substantial improvement of image quality is possible by analysis based post-processing.

The encoding of images at high quality is important in a number of applications. We have developed an approach to coding that produces no visible degradation and that we denote as perceptually transparent. Such a technique achieves a modest compression, but still significantly higher than error free codes. Maintaining image quality is not important in the early stages of a progressive scheme, when only a reduced resolution preview is needed. In this paper, we describe a new method for the progressive transmission of high quality still images, that efficiently uses the lower resolution images in the encoding process. Analysis based interpolation is used to estimate the higher resolution image, and reduces the incremental information transmitted at each step. This methodology for high quality image compression is also aimed at obtaining a compressed image of higher perceived quality than the original.© (1996) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.

In previous work, we reported on the benefits of noise reduction prior to coding of very high quality images. Perceptual transparency can be achieved with a significant improvement in compression as compared to error free codes. In this paper, we examine the benefits of preprocessing when the quality requirements are not very high, and perceptible distortion results. The use of data dependent anisotropic diffusion that maintains image structure, edges, and transitions in luminance or color is beneficial in controlling the spatial distribution of errors introduced by coding. Thus, the merit of preprocessing is for the control of coding errors. In this preliminary study, we only consider preprocessing prior to the use of the standard JPEG and MPEG coding techniques.© (1995) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.

We have recently proposed the use of geometry in image processing by representing an image as a surface in 3-space. The linear variations in intensity (edges) were shown to have a nondivergent surface normal. Exploiting this feature we introduced a nonlinear adaptive filter that only averages the divergence in the direction of the surface normal. This led to an inhomogeneous diffusion (ID) that averages the mean curvature of the surface, rendering edges invariant while removing noise. This mean curvature diffusion (MCD) when applied to an isolated edge imbedded in additive Gaussian noise results in complete noise removal and edge enhancement with the edge location left intact. In this paper we introduce a new filter that will render corners (two intersecting edges), as well as edges, invariant to the diffusion process. Because many edges in images are not isolated the corner model better represents the image than the edge model. For this reason, this new filtering technique, while encompassing MCD, also outperforms it when applied to images. Many applications will benefit from this geometrical interpretation of image processing, and those discussed in this paper include image noise removal, edge and/or corner detection and enhancement, and perceptually transparent coding.

The design of linear image filters based on properties of human visual perception has been shown to require the minimization of criterion functions in both the spatial and frequency domains. We extend this approach to continuous filters of infinite support. For lowpass filters, this leads to the concept of an ideal lowpass image filter that provides a response that is superior perceptually to that of the classical ideal lowpass filter. >

In the perceptually transparent coding of images, we use representation and quantization strategies that exploit properties of human perception to obtain an approximate digital image indistinguishable from the original. This image is then encoded in an error free manner. The resulting coders have better performance than error free coding for a comparable quality. Further, by considering changes to images that do not produce perceptible distortion, we identify image characteristics onerous for the encoder, but perceptually unimportant. Once such characteristic is the typical noise level, often imperceptible, encountered in still images. Thus, we consider adaptive noise removal to improve coder performance, without perceptible degradation of quality. In this paper, several elements contribute to coding efficiency while preserving image quality: adaptive noise removal, additive decomposition of the image with a high activity remainder, coarse quantization of the remainder, progressive representation of the remainder, using bilinear or directional interpolation methods, and efficient encoding of the sparse remainder. The overall coding performance improvement due to noise removal and the use of a progressive code is about 18%, as compared to our previous results for perceptually transparent coders. The compression ratio for a set of nine test images is 3.72 for no perceptible loss of quality.

The inadequacy of the classic linear approach to edge detection and scale space filtering lies in the spatial averaging of the Laplacian. The Laplacian is the divergence of the gradient and thus is the divergence of both magnitude and direction. The divergence in magnitude characterizes edges and this divergence must not be averaged if the image structure is to be preserved. We introduce a new nonlinear filtering theory that only averages the divergence of direction. This averaging keeps edges and lines intact as their direction is nondivergent. Noise does not have this nondivergent consistency and its divergent direction is averaged. Higher order structures such as corners are singular points or inflection points in the divergence of direction and also are averaged. Corners are intersection points of edges of nondivergent direction (or smooth curves of small divergence in direction) and their averaging is limited. This approach provides a better compromise between noise removal and preservation of image structure. Experiments that verify and demonstrate the adequacy of this new theory are presented.

A number of new approaches to image coding are being developed to meet the increasing need for a broader range of quality and usage environments for image compression. Several of these new approaches, such as subband coders, are intended to provide higher quality images at the same bit rate as compared to the JPEG standard, because they are not subject to end of block artifacts, or because they are inherently better attuned to the image representation that occurs in the peripheral visual system. Still, in the absence of a pertinent quality criterion, the quality and performance of subband coders, or wavelet coders, can be mediocre. We have developed over the past few years the elements of a methodology applicable to this problem. We reported last year at the SPIE in San Diego, a Comparison of Coding Techniques based on a new Picture Quality Scale (PQS). In that work, we were able to rate coders designed by any criteria, on the basis of performance and quality. The problem that we are now considering is to design the coding technique so as to provide a better quality or a lower bit rate. The image quality, as evaluated by PQS, depends on a combination of several objective distortion factors, which can be identified with perceived coding artifacts, but the design of coders using all the factors is much too complex for an analytical approach. We make use instead of two design methodologies. The first one is to optimize the design of an existing subband coder using PQS as a distortion metric. The second one makes use of a methodology for the design of linear filters based on properties of human perception that we have developed previously and that may provide a tractable design method.

The encoding of Super High Definition Images presents new problems with regard to the effect of noise on the quality of images and on coding performance. Although the information content of images decreases with increasing resolution, the noise introduced in the image acquisition or scanning process, remains at a high level, independently of resolution. Although this noise may not be perceptible in the original image, it will effect the quality of the encoded image, if the encoding process introduces correlation and structure in the coded noise. Further, the coder performance will be affected by the noise, even if the noise is not perceived. Therefore, there is a need to reduce the noise by pre-processing the SHD image, so as to maintain image quality and improve the encoding process. The reduction of noise cannot be performed by low pass filtering operations that will degrade image quality. We are applying to this problem image analysis for adaptive noise removal. We discuss first the information theoretic issues on the effect of noise on coders. We then consider adaptive noise removal techniques to the perceptually transparent and very high quality coding of still SHD images.© (1992) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.

An application of neural networks is the recognition of objects under translation, rotation, and scale change. Most existing networks for invariant object recognition require a huge number of connections and/or processing units. In this paper, we propose a new connectionist model for invariant object recognition for binary images with a reasonable network size. The network consists of five stages. The first stage shifts the object so that the centroid of the object coincides with the center of the image plane. The second stage is a variation of the polar- coordinate transformation used to obtain two N-dimensional representations of the input object. In this stage, the 0 axis is represented by the positions of the output units; therefore, any rotation of the original object becomes a cyclic shift of the output values of this stage. The third stage is a variation of the rapid transform, which provides invariant representations of cyclic-shift inputs. The next stage normalizes the outputs of the rapid transform to obtain scale invariance. The final stage is a nearest neighbor classifier. We tested the performance of the network for character recognition and good results were obtained with only one pattern per class in training.

In image processing operations involving changes in the sampling grid, including increases or decreases in resolution, care must be taken to preserve image structure. Structure includes regions of high contrast, such as edges, streaks, or corners, all indicated by large gradients. The authors consider the use of local spatial analysis for both sampling and interpolation. Anisotropic diffusion is considered as a directional smoothing technique that preserves structure. This is used in conjunction with a method of directional interpolation, which is also based on structure analysis. >

In the encoding of high quality images beyond current standards, a reexamination of issues in the representation, processing and encoding problems is needed. The fundamental reason for that change of emphasis is because the image representation, sampling density, color and motion parameters are no longer given by accepted practices or standards and, thus, require study. Some basic issues that should be reconsidered are as follows

We have undertaken a study of techniques for the perceptually transparent coding of very high quality and resolution images. These techniques should be free of any of the visual artifacts due to transform coders, DPCM coders or vector quantization, so that post processing to improve image quality or resolution can be performed. The approach starts with a decomposition of an image into a spline approximation, based on a subsampled array of pixels. The spline approximation is then subtracted from the original and the resulting remainder is quantized non-uniformly and with as few levels as possible while still insuring perceptual transparency. This differential quantization takes advantage of properties of human vision. The coarsely quantized remainder is then encoded in an error free fashion. Several techniques are being considered for this error free coding. Since the only errors are introduced in the quantization of the remainder, the errors are not perceptible and there is no structure to them.

A new approach is developed for detection of image objects and their orientations, based on distance transforms of intermediate level edge information(i.e., edge segments and vertices). Objects are modeled with edge segments and these edges are matched to edges extracted from an image by correlating spatially transformed versions of one with a distance transform of the other. Upper bounds on change in cross- correlation between edge maps and distance transforms are shown to be simple functions of change in translation and rotation. The process of computing the optimal object rotation at each possible translation can be accelerated by one to two orders of magnitude when these bounds are applied in conjunction with an object translation-rotation traversal strategy. Examples with detection and acceleration results demonstrate the robustness and high discriminatory power of the algorithm.

The goal of this research is to develop interpolation techniques which preserve or enhance the local structure critical to image quality. Preliminary results are presented which exploit either the properties of vision or the properties of the image in order to achieve the goals. Directional image interpolation is considered which is based on a local analysis of the spatial image structure. The extension of techniques for the design of linear filters based on properties of human perception reported previously to enhance the perceived quality of interpolated images is considered. >