A Cluster Grouping Technique for Texture Segmentation
We propose an algorithm for texture segmentation based on a divide-and-conquer strategy of statistical modeling.
texture segmentation mixture models↗
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Bayesian Fusion of Color and Texture Segmentations
A common method for texture representation is to use the marginal probability densities over the outputs of a set of multi-orientation, multi-scale filters as a description of the texture.
Color↗
Bayesian Fusion of Color and Texture Segmentations
In many applications one would like to use information from both color and texture features in order to segment an image. We propose a novel technique to combine "soft" segmentations computed for two or more features independently. Our algorithm merges models according to a mean entropy criterion, and allows to choose the appropriate number of classes for the final grouping. This technique also allows to improve the quality of supervised classification based on one feature (e.g. color) by merging information from unsupervised segmentation based on another feature (e.g., texture.)
Manduchi, Roberto↗
Mixture Models and the Segmentation of Multimodal Textures
A problem of using mixture-of-Gaussian models for unsupervised texture segmentation is that multimodal textures cannot be well represented by a single Gaussian cluster.
texture↗
Unsupervised texture image segmentation by improved neural network ART2
We here propose a segmentation algorithm of texture image for a computer vision system on a space robot. An improved adaptive resonance theory (ART2) for analog input patterns is adapted to classify the image based on a set of texture image features extracted by a fast spatial gray level dependence method (SGLDM). The nonlinear thresholding functions in input layer of the neural network have been constructed by two parts: firstly, to reduce the effects of image noises on the features, a set of sigmoid functions is chosen depending on the types of the feature; secondly, to enhance the contrast of the features, we adopt fuzzy mapping functions. The cluster number in output layer can be increased by an autogrowing mechanism constantly when a new pattern happens. Experimental results and original or segmented pictures are shown, including the comparison between this approach and K-means algorithm. The system written in C language is performed on a SUN-4/330 sparc-station with an image board IT-150 and a CCD camera.
Wang, Zhiling↗
Passive range sensor refinement using texture and segmentation
Electrooptical sensors provide a covert way of computing range during helicopter flight. The optical flow-based computation of range provides range information only in certain distinguishable parts of the image. The regions where range information is available can be increased by performing texture analysis and object segmentation in the image. This paper reviews some of the literature on texture segmentation methods with a view towards applying them to images containing both man-made and natural objects at varying ranges. Two algorithmic approaches are given and their application to a real image is demonstrated. Results indicate that it will be necessary to combine several different texture measures and methods in a hierarchical way in order to achieve an object segmentation which is useful in enhancing range information.
Sridhar, Banavar↗
A Markov Random Field Model for Texture-based Segmentation of Small Cracks in Thin InconelTubes
Markov random fields have been used for image segmentation since their introduction in the1980s. This work applies a method from Principal Component Thermography to enhance thecontrast in damage regions in 3D images derived from X-ray computed tomography (CT)inspections. The developed method is applied to sizing of small cracks in thin Inconel tubes designed as probability of detection (POD) samples for radiographic inspection.Misclassification errors arising from artifacts due to beam-hardening are reduced by fitting the boundary of the segmented damage region with the arc of an ellipse. Results are comparedagainst those obtained through manual inspection.
Image segmentation↗
A Markov Random Field Model for Texture-based Segmentation of Small Cracks in Thin Inconel Tubes
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Peter Spaeth↗
Stereo-Based Region-Growing using String Matching
We present a novel stereo algorithm based on a coarse texture segmentation preprocessing phase. Matching is performed using a string comparison. Matching sub-strings correspond to matching sequences of textures. Inter-scanline clustering of matching sub-strings yields regions of matching texture. The shape of these regions yield information concerning object's height, width and azimuthal position relative to the camera pair. Hence, rather than the standard dense depth map, the output of this algorithm is a segmentation of objects in the scene. Such a format is useful for the integration of stereo with other sensor modalities on a mobile robotic platform. It is also useful for localization; the height and width of a detected object may be used for landmark recognition, while depth and relative azimuthal location determine pose. The algorithm does not rely on the monotonicity of order of image primitives. Occlusions, exposures, and foreshortening effects are not problematic. The algorithm can deal with certain types of transparencies. It is computationally efficient, and very amenable to parallel implementation. Further, the epipolar constraints may be relaxed to some small but significant degree. A version of the algorithm has been implemented and tested on various types of images. It performs best on random dot stereograms, on images with easily filtered backgrounds (as in synthetic images), and on real scenes with uncontrived backgrounds.
Mandelbaum, Robert↗
Scene segmentation of natural images using texture measures and back-propagation
Knowledge of the three-dimensional world is essential for many guidance and navigation applications. A sequence of images from an electro-optical sensor can be processed using optical flow algorithms to provide a sparse set of ranges as a function of azimuth and elevation. A natural way to enhance the range map is by interpolation. However, this should be undertaken with care since interpolation assumes continuity of range. The range is continuous in certain parts of the image and can jump at object boundaries. In such situations, the ability to detect homogeneous object regions by scene segmentation can be used to determine regions in the range map that can be enhanced by interpolation. The use of scalar features derived from the spatial gray-level dependence matrix for texture segmentation is explored. Thresholding of histograms of scalar texture features is done for several images to select scalar features which result in a meaningful segmentation of the images. Next, the selected scalar features are used with a neural net to automate the segmentation procedure. Back-propagation is used to train the feed forward neural network. The generalization of the network approach to subsequent images in the sequence is examined. It is shown that the use of multiple scalar features as input to the neural network result in a superior segmentation when compared with a single scalar feature. It is also shown that the scalar features, which are not useful individually, result in a good segmentation when used together. The methodology is applied to both indoor and outdoor images.
Sridhar, Banavar↗
Texture classification using autoregressive filtering
A general theory of image texture models is proposed and its applicability to the problem of scene segmentation using texture classification is discussed. An algorithm, based on half-plane autoregressive filtering, which optimally utilizes second order statistics to discriminate between texture classes represented by arbitrary wide sense stationary random fields is described. Empirical results of applying this algorithm to natural and sysnthesized scenes are presented and future research is outlined.
Lawton, W. M.↗
Rough-Wall Turbulent Heat Transfer Experiments in Hypersonic Free Flight
Experiments are being conducted in the NASA Ames Hypervelocity Free Flight Aerodynamic Facility to quantify the effects on turbulent convective heat transfer of surface roughness representative of a new class of 3D woven thermal protection system mRough-wall turbulent heat transfer measurements were obtained on ballistic-range models in hypersonic flight in the NASA Ames Hypervelocity Free Flight Aerodynamic Facility. Each model had three different surface textures on segments of the conic frustum: smooth wall, sand roughness, and a pattern roughness, thus providing smooth-wall and sand-roughness reference data for each test. The pattern roughness was representative of a woven thermal protection system material developed by NASA's Heatshield for Extreme Entry Environment Technology project. The tests were conducted at launch speeds of 3.2 km/s in air at 0.15 atm. Roughness Reynolds numbers, k+, ranged for 12 to 70 for the sand roughness, and as high as 200 for the pattern roughness. Boundary-layer parameters required for calculating k+ were evaluated using computational fluid dynamics simulations. The effects of pattern roughness are generally characterized by an equivalent sand roughness determined with a correlation developed from experimental data obtained on specifically-designed roughness patterns that do not necessarily resemble real TPS materials. Two sand roughness correlations were examined: Dirling and van Rij, et al. Both gave good agreement with the measured heat-flux augmentation for the two larger pattern roughness heights tested, but not for the smallest height tested. It has yet to be determined whether this difference is due to limitations in the experimental approach, or due to limits in the correlations used. Future experiments are planned that will include roughness patterns more like those used in developing the equivalent sand roughness correlations.aterials being developed by NASA's Heatshield for Extreme Entry Environment Technology (HEEET) project. Data were simultaneously obtained on sand-grain roughened surfaces and smooth surfaces, which can be compared with previously obtained data. Results are presented in this extended abstract for one roughness pattern. The full paper will include results from three roughness patterns representing virgin HEEET, nominal turbulent ablated HEEET, and twice the roughness of nominal turbulent ablated HEEET. Results will be used to compare with commonly used equivalent sand grain roughness correlations.
pattern roughness↗
Power spectral ensity of markov texture fields
Texture is an important image characteristic. A variety of spatial domain techniques were proposed for extracting and utilizing textural features for segmenting and classifying images. for the most part, these spatial domain techniques are ad hos in nature. A markov random field model for image texture is discussed. A frequency domain description of image texture is derived in terms of the power spectral density. This model is used for designing optimum frequency domain filters for enhancing, restoring and segmenting images based on their textural properties.
Shanmugan, K. S.↗
Image processing in remote sensing data analysis - The state of the art
Image analysis techniques applicable to remote sensing data and covering image models, feature detection, segmentation and classification, texture analysis, and matching are studied. Model types for characterizing images examined include random-field, mosaic, and facet models. Edge and corner detection as well as global extraction of linear features are discussed. Pixel clustering and classification are covered in addition to the regional approach to segmentation. Autocorrelation, second-order gray level probability density, and the use of primitive element statistics are discussed in relation to texture analysis. Finally, reducing the cost of (sub)imaging matching methods (e.g., pixelwise comparison of gray levels and normalized cross-correlation between two images) as well as improving match sharpness is considered.
Rosenfeld, A.↗
Proceedings of the NASA Workshop on Image Analysis
Three major topics of image analysis are addressed: segmentation, shape and texture analysis, and structural analysis.
Guseman, L. F., Jr.↗
Advances in image compression and automatic target recognition; Proceedings of the Meeting, Orlando, FL, Mar. 30, 31, 1989
Various papers on image compression and automatic target recognition are presented. Individual topics addressed include: target cluster detection in cluttered SAR imagery, model-based target recognition using laser radar imagery, Smart Sensor front-end processor for feature extraction of images, object attitude estimation and tracking from a single video sensor, symmetry detection in human vision, analysis of high resolution aerial images for object detection, obscured object recognition for an ATR application, neural networks for adaptive shape tracking, statistical mechanics and pattern recognition, detection of cylinders in aerial range images, moving object tracking using local windows, new transform method for image data compression, quad-tree product vector quantization of images, predictive trellis encoding of imagery, reduced generalized chain code for contour description, compact architecture for a real-time vision system, use of human visibility functions in segmentation coding, color texture analysis and synthesis using Gibbs random fields.
Tescher, Andrew G.↗
Generation of texture images using adaptive windows
Simulated forest and observed TM and SPOT images illustrate the improvements an adaptive-window texture image can have over traditional texture images for applications like image segmentation where edge effects in the texture image can be detrimental. By adaptive selection of the 'best' window position (as opposed to always having the window centered over the pixel) minimizes edge effects while retaining local texture information. Tests using 3 x 3 windows and local variance on SPOT, TM and simulated TM data show similar results.
Woodcock, Curtis E.↗
Mechanical and chemical effects of ion-texturing biomedical polymers
To determine whether sputter etching may provide substantial polymer surface texturing with insignificant changes in chemical and mechanical properties, an 8 cm beam diameter, electron bombardment, argon ion source was used to sputter etch (ion-texture process) nine biomedical polymers. The materials included silicone rubber, 32% carbon impregnated polyolefin, polyoxymethylene, polytetrafluoroethylene, ultrahigh molecular weight (UHMW) polyethylene, UHMW polyethylene with carbon fibers (10%), and several polyurethanes (bioelectric, segmented, and cross linked). Ion textured microtensile specimens of each material except UHMW polyethylene and UHMW polyethylene with 10% carbon fibers were used to determine the effect of ion texturing on tensile properties. Scanning electron microscopy was used to determine surface morphology changes, and electron spectroscopy for chemical analysis was used to analyze the near surface chemical changes that result from ion texturing. Ion energies of 500 eV with beam current densities ranging from 0.08 to 0.19 mA/sq cm were used to ion texture the various materials. Standard microtensile specimens of seven polymers were exposed to a saline environment for 24 hours prior to and during the tensile testing. The surface chemical changes resulting from sputter etching are minimal in spite of the often significant changes in the surface morphology.
Weigand, A. J.↗