Showing papers by "Alan C. Bovik published in 1990"

Multichannel texture analysis using localized spatial filters

Abstract: A computational approach for analyzing visible textures is described. Textures are modeled as irradiance patterns containing a limited range of spatial frequencies, where mutually distinct textures differ significantly in their dominant characterizing frequencies. By encoding images into multiple narrow spatial frequency and orientation channels, the slowly varying channel envelopes (amplitude and phase) are used to segregate textural regions of different spatial frequency, orientation, or phase characteristics. Thus, an interpretation of image texture as a region code, or carrier of region information, is emphasized. The channel filters used, known as the two-dimensional Gabor functions, are useful for these purposes in several senses: they have tunable orientation and radial frequency bandwidths and tunable center frequencies, and they optimally achieve joint resolution in space and in spatial frequency. By comparing the channel amplitude responses, one can detect boundaries between textures. Locating large variations in the channel phase responses allows discontinuities in the texture phase to be detected. Examples are given of both types of texture processing using a variety of real and synthetic textures. >

Visual pattern image coding

Abstract: A novel framework for digital image compression called visual pattern image coding, or VPIC, is presented. In VPIC, set of visual-patterns is defined independent of the images to be coded. Each visual pattern is a subimage of limited spatial support that is visually meaningful to a normal human observer. The patterns are used as a basis for efficient image representation; since it is assumed that the images to be coded are natural optical images to be viewed by human observers, visual pattern design is developed using relevant psychophysical and physiological data. Although VPIC bears certain resemblances to block truncation (BTC) and vector quantification (VQ) image coding, there are important differences. First, there is no training phase required: the visual patterns derive from models of perceptual mechanisms; second, the assignment of patterns to image regions is not based on a standard (norm) error criterion; expensive search operations are eliminated. >

Shape description of biological objects via stereo light microscopy

Abstract: Computational stereo vision techniques are applied to the analysis of the three-dimensional (3-D) shape of biological specimens imaged through a stereo light microscope. 3-D shape descriptions are derived by integrating binocular and monocular measurements. Microscopic biological objects viewed through a light microscope generally have ill-defined boundaries in shape and reflectance and often exhibit transparency. In addition, the limited illumination available in the light microscope often makes it difficult to obtain images of sufficiently high contrast. In the approach presented, a new matching algorithm is introduced using intensity gradient information to solve the problem. Shape descriptions are computed by exploiting a number of additional processing steps. Two different classes of shapes are considered, which lead to two distinct approaches for 3-D analysis: solid objects and vascular networks. The first class of objects is described using a visible surface representation, while the second is expressed in terms of a set of space curves. The efficacy of each approach is demonstrated using microscopic-scale solid and microvascular specimens. >

Method and apparatus for visual pattern image coding

Abstract: An improved method for coding and decoding digital images by partitioning images into blocks and coding each image separately according to visually significant responses of the human eye. Coding is achieved by calculating and subtracting a mean intensity value from digital numbers within each block or partition and detecting visually perceivable edge locations within the resultant residual image block. If a visually perceivable edge is contained within the block, gradient magnitude and orientation at opposing sides of the edge within each edge block are calculated and appropriately coded. If no perceivable edge is contained within the block, the block is coded as a uniform intensity block. Decoding requires receiving coded mean intensity value, gradient magnitude and pattern code, and then decoding a combination of these three indicia to be arranged in an orientation substantially similar to the original digital image.

Properties of multichannel texture analysis filters

Abstract: Texture analysis algorithms that decompose images into oriented spatial frequency channels are studied. Optimality properties for texture segmentation filters are considered using idealized (narrowband) image texture models. The functional uncertainty of the channel filters is shown to define a tradeoff between spectral selectivity and accuracy in boundary localization that is optimized by the 2-D Gabor functions. The idealized texture model is then relaxed to analyze the effects of textural perturbations interpreted as localized amplitude and phase variations on the segmentation. The effects of these perturbations are found to be effectively ameliorated with postdetection smoothing. >

Statistical optimality of locally monotonic regression

Abstract: We derive the maximum likelihood (ML) estimators for estimating locally monotonic signals embedded in white additive noise, when the noise is assumed to have a density function that is a member of a family of generalized exponential densities with parameter p that includes the Laplacian (p = 1), Gaussian (p = 2) and, as a limiting case, the uniform (p = ∞) densities. The estimators are given by the so-called locally monotonic regression of the noisy signal, a tool of recent introduction in signal processing. The approach that is used in the paper results from a geometric interpretation of the likelihood function of the sample; it takes advantage of the fact that a term in the likelihood function is the p-distance between the vector formed by the data in the given signal (sample) and the vector formed by the elements in the desired signal (estimator). Isotonic regression is a technique used in statistical estimation theory when the data are assumed to obey certain order restrictions. Local monotonicity is a generalization of the concept of isotonicity which is useful for some problems in signal processing.

3-D model of vascular network in rat skin obtained by stereo vision techniques.

Abstract: SUMMARY The quantitative analysis of the depth of injury, penetration of therapeutic agents in tissues, and the regeneration of vascular patency after a graded degree of thermal injury requires a knowledge of the shape and spatial configuration of the vascular networks in the tissue. We have applied computational stereo vision techniques to describe the 3-D configuration of microvessels in full thickness rat skin vascular casts produced by perfusion of Yellow Microfil latex solution through the aorta. The principal concern is to describe the 3-D structure of vascular networks using a set of 3-D space curves. This representation is computed by integrating monocular and binocular processing; the 2-D curve representation of blood vessels computed through monocular analysis is integrated with disparity data to yield a space curve representation for each vessel. A connection diagram is also computed to indicate the connections existing among the computed space curve representations.

Least-squares order statistic filters for signal restoration in dependent noise

Abstract: C.G. Boncelet's algorithm (SIAM J. Sci. Stat. Comput., vol.8, p.868-76, Sept. 1987) is used to explore the OS filter design/analysis problem. In particular, the optimal filter for restoring nonrandom signals immersed in Markov noise, using the mean square error as an optimality criterion, is studied. The noise processes are modeled either as causal first-order autoregressive Gaussian or as first-order moving-average Gaussian. Various structural signal constraints are improved on the solution by stating them as local unbiasedness constraints. >

Non-Euclidean locally monotonic regression

Abstract: The concept of locally monotonic regression is extended by considering metrics on r/sup n/ that are different from the Euclidean metric. The existence of regressions for a large class of metrics is shown. Algorithms that show the computability of locally monotonic regressions are given. A general algorithm that computes regressions for the l/sub p/ metrics is given; considered in detail are the cases corresponding to the metrics l/sub 1/ and l/sub infinity /, where the sample median and the sample midrange play important roles. >

Hierarchical visual pattern image coding

Abstract: Visual pattern image coding (VPIC) is a flexible image coding device offering excellent visual fidelity at high compression ratios with unprecedented, extremely low coding complexity. Hierarchical visual pattern image coding (HVPIC) schemes which achieve significantly increased compression (approaching 35:1) with improved fidelity and the very low complexity associated with VPIC are described. HVPIC employs standard VPIC embedded in a multiresolution (pyramid) structure. Low-resolution images coded at the upper levels augment coding of the higher-resolution images. Interactions between resolution levels is simple and efficient, yielding little increase in complexity relative to VPIC. >

Optimally localized estimation of the fractal dimension

Abstract: We present a method for making accurate measurements of the instantaneous fractal dimension of (1) images modeled as fractal Brownian surfaces, and (2) images of physical surfaces modeled as fractal Brownian surfaces. Fractal Brownian surfaces have the property that their apparent roughness increases as the viewing distance decreases. Since this true of many natural surfaces, fractal Brownian surfaces are excellent candithtes for modeling rough surfaces. To obtain accurate local values of the fractal dimension, spatio-spectrally localized measurements are necessary. Our method employs Gabor filters, which optimize the conflicting goals of spatial and speciral localization as constrained by the functional uncertainty principle. The outputs from multiple Gabor filters are fitted to a fractal power-law curve whose parameters determine the fractal dimension. The algorithm produces a local value of the fractal dimension for every point in the image. We also introduce a variational technique for producing a fractal dimension function which varies smoothly across the image. This technique is implemented using an iterative relaxation algorithm. A test of the method on 50 synthetic images of known global fractal dimensions shows that the method is accurate with an error of approximately 4.5% for fractal Brownian images and approximately 8.5% for images of physical fractal Brownian surfaces.

Tracking of vessel diameter fluctuations using digital image analysis

Abstract: An automatic digital image processing technique for vasomotion analysis in peripheral microcirculation atmultiple sites simultaneously and in real time, is presented. The algorithm utilizes either fluorescent or bright field microimages of the vasculature as input. The video images are digitized and analyzed on-line by an IBM RT PC. Usingdigital filtering and edge detection, the technique allows simultaneous diameter measurement at more than one site. Thesampling frequency is higher than 5Hz when only one site is tracked. The performance of the algorithm is tested in thehamster cutaneous microcirculation. 1. INTRODUCTIONThe microvasculature is a network of vessels (arteries, arterioles, veins, venules and capillaries) of differentsizes and branching order. Arterioles and sometimes venules are observed to possess spontaneous quasi-periodicrhythmic contractile movement (vasomotion) which may be involved in the regulation of blood flow, microvascular pressure, and/or the perfusion state of the peripheral tissue. In order to understand the mechanism and the physiological

GRUPO: a 3-D structure recognition system

Abstract: significant features, to limit the object and viewpoint search spaces in recognition. The system receivesfeature data from a segmentation based on perceptual organization and ranks the object space according toestimates of conditional object probabilities. Depth information is not used in the approach.

Multiple channel surface orientation from texture

Abstract: This paper studies the computation of surface orientation by analyzing the responses of multiple spatio-spectrally localized channel filters. Images containing textures that encode information about local surface orientation are decomposed into narrowband sub-images possessing characteristic radial frequency and orientation properties. By analyzing the spatial variation in the filter responses, information about the spatial variation in the pattern I texture can be elucidated and subsequently used to estimate surface orientation. The channel filters used are Gaborfunctions, which have previously been applied successfully to related problems in texture analysis, segmentation, and characterization. The Gabor functions are plausible approximations to the responses of the highly oriented simple cells in mammalian striate cortex. They also possess important properties for the local isolation and characterization of textures. In our approach, texture gradients are modeled as giving rise to pattern frequency gradients that can be exiracted on a highly localized basis. A variational optimization procedure for estimating the pattern frequency variation is implemented via a discrete relaxation procedure that is suitable for a massively parallel computation. The result of the optimization procedure is a stable dense map describing the localized image frequency content. The computed image frequency characteristics are then used to define a texture density measure used in a planar-surface approximation procedure, yielding slant/tilt estimates describing the surface orientation. Experimental results support the theoretical derivations.

Visual pattern image sequence coding

Abstract: Visual pattern image coding or VPIC 15 is an important new digital image coding process that possesses significant advantages relative to all other existing technologies: VPIC is capable of coding (i) with visual fidelity comparable to the best available techniques (ii) at very high compressions exceeding the best available technologies: compressions in the range 30: 140: 1 are obtained routinely (iii) with absolutely unprecedented coding efficiency - coding/decoding via VPIC is completely linear with respect to image size and entails a complexity 1-2 orders of magnitude faster than any prior high compression strategy (iv) configurability. In the current work the VPIC coding framework developed initially for single images is extended to image sequences. The algorithm for image sequence coding presented here termed Visual Pattern Image Sequence Coding (or VPISC) exploits all of the advantages of " static" VPIC in the reduction of information from an additional (temporal) dimension to achieve unprecedented image sequence coding performance stated in terms of coding complexity compression and visual fidelity.© (1990) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.

Feature classification techniques in model-based object recognition

Abstract: We describe a system that performs model-based recognition of the projections of generalized cylinders, and present new results on the final classification of the feature data. Two classification methods are proposed and compared. The first is a Bayesian technique that ranks the object space according to estimated conditional probability distributions. The second technique is a new feed-forward “neural” implementation that utilizes the back-propagation learning algorithm. The neural approach yields a 31.8% reduction in classification error for a database of twenty models relative to the Bayesian approach, although it does not provide an ordered ranking of the object space. The accuracy results of the neural approach represent a significant performance advance in feature-based recognition by perceptual organization without the use of depth information. Examples are provided using the results of a simple segmentation system applied to real image data.

On Using Color In Edge-Based Stereo Algorithms

Abstract: One approach to developing faster, more accurate stereo algorithms is to seek a more complete and efficient use of information available in stereo images. The use of chromatic (color) information has been largely neglected in this regard. Motivations for using chromatic information are discussed, including strong evidence for the use of chromatic information in the human stereo correspondence process. To investigate the potential role of chromatic information in edge-based stereo algorithms, a novel chromatic matching constraint -- the chromatic gradient matching constraint -- is presented. A thorough analysis of the utility of this constraint in both the "match extraction" and "disparity selection" stages of the PMF Algorithm 1 is performed for a wide range of matching strength "support neighborhood" sizes. The performances of the algorithm with and without these constraints are directly compared in terms of disambiguation ability, matching accuracy and algorithm speed. The results demonstrate that the use of chromatic information can greatly reduce matching ambiguity, resulting in increased matching accuracy and algorithm speed.

Dl.1 Non-Euclidean Locally Monotonic Regression

Abstract: The recently introduced concept of locally monotonic regression (l) is extended by considering metrics on Rn that are different from the Euclidean metric. We show the existence of regressions for a large class of metrics. Algorithms that show the computablity of locally monotonic regressions are given. A general algorithm that computes regressions for the Ip metrics is given: considered in detail are the cases corresponding to the metrics 11 and I,, where the sample median and the sample midrange play important roles.

Dense color stereo

Abstract: Motivated by the observation that chromatic (color) information is a salient property of surfaces in many natural scenes,we investigate the use of chromatic information in dense stereo correspondence -- a topic which has never been investigated.In this regard, the chromatic photometric constraint, which is used to specify a mathematical optimality criterion for solvingthe dense stereo correspondence problem, is developed. The result is a theoretical construction for developing dense stereo correspondence algorithms which use chromatic information. The efficacy of using chromatic information via thisconstruction is tested by developing a new dense stereo algorithm -- the Dense Color Stereo Algorithm. The results of applying intensity and chromatic versions of the Dense Color Stereo Algorithm to several stereo image pairs demonstrate thatthe use of chromatic information can significantly improve the performance of dense stereo correspondence. These resultscomplement our prior studies of the utility of chromatic information in edge-based stereo correspondence, where it was alsoshown to play a significant role.