The first step to deal with the significant issue of air pollution in China and elsewhere in the world is to monitor it. While more physical monitoring stations are built, current coverage is limited to large cities with most other places under-monitored. In this paper we propose a complementary approach to monitor Air Quality Index (AQI): using machine learning models to estimate AQI from social media posts. We propose a series of progressively more sophisticated machine learning models, culminating in a Markov Random Field model that utilizes the text content in social media as well as the spatiotemporal correlation among cities and days. Our extensive experiments on Sina Weibo data from 108 cities during a one-month period demonstrate the accurate AQI prediction performance of our approach.

We study the cosegmentation problem where the objective is to segment the same object (i.e., region) from a pair of images. The segmentation for each image can be cast using a partitioning/segmentation function with an additional constraint that seeks to make the histograms of the segmented regions (based on intensity and texture features) similar. Using Markov Random Field (MRF) energy terms for the simultaneous segmentation of the images together with histogram consistency requirements using the squared L(2) (rather than L(1)) distance, after linearization and adjustments, yields an optimization model with some interesting combinatorial properties. We discuss these properties which are closely related to certain relaxation strategies recently introduced in computer vision. Finally, we show experimental results of the proposed approach.

The joint invariants of the projective group PSL(3,R) on R2, the five-point volume cross-ratios, are studied to address the problem of correspondence in a camera network. The distribution of cross-ratios over the unit square as well as in a small local-neighbourhood of a reference point are found to have a heavy tail. No cross ratio value is unique but the collection of five point cross ratios generated by taking all possible combination of five points completely prescribes the curve. Sections of the signature submanifold that admit large enough variation of cross ratios are found to be sufficient in providing correspondence across wide perspectives. Such invariant signatures may be collected independently at cameras with different viewpoints and shared, thereby achieving the registration of objects in the image. Experimental results with license plate database are provided.

In this paper we study some problems related to human age estimation using a large database. First, we study the influence of gender on age estimation based on face representations that combine biologically-inspired features with manifold learning techniques. Second, we study age estimation using smaller gender and age groups rather than on all ages. Significant error reductions are observed in both cases. Based on these results, we designed three frameworks for automatic age estimation that exhibit high performance. Unlike previous methods that require manual separation of males and females prior to age estimation, our work is the first to estimate age automatically on a large database. Furthermore, a data fusion approach is proposed using one of the frameworks, which gives an age estimation error more than 40% smaller than previous methods.

Head pose estimation has many useful applications in practice. How to estimate the head pose automatically and robustly is still a challenging problem. In pose estimation, different pose angles can be used as regression values or viewed as different class labels. Thus a question is raised in our study: which is proper for pose estimation - classification or regression? We investigate representative classification and regression methods on the same problem to see any difference. A method that combines regression and classification approaches is also examined. Preliminary experiments show some interesting results which might prompt further exploration of related issues in pose estimation.

Automatic human age estimation has considerable potential applications in human computer interaction and multimedia communication. However, the age estimation problem is challenging. We design a locally adjusted robust regressor (LARR) for learning and prediction of human ages. The novel approach reduces the age estimation errors significantly over all previous methods. Experiments on two aging databases show the success of the proposed method for human aging estimation.

Estimating human age automatically via facial image analysis has lots of potential real-world applications, such as human computer interaction and multimedia communication. However, it is still a challenging problem for the existing computer vision systems to automatically and effectively estimate human ages. The aging process is determined by not only the person's gene, but also many external factors, such as health, living style, living location, and weather conditions. Males and females may also age differently. The current age estimation performance is still not good enough for practical use and more effort has to be put into this research direction. In this paper, we introduce the age manifold learning scheme for extracting face aging features and design a locally adjusted robust regressor for learning and prediction of human ages. The novel approach improves the age estimation accuracy significantly over all previous methods. The merit of the proposed approaches for image-based age estimation is shown by extensive experiments on a large internal age database and the public available FG-NET database.

This paper describes a patch-based approach for rapid image correlation or template matching. By representing a template image with an ensemble of patches, the method is robust with respect to variations such as local appearance variation, partial occlusion, and scale changes. Rectangle filters are applied to each image patch for fast filtering based on the integral image representation. A new method is developed for feature dimension reduction by detecting the "salient" image structures given a single image. Experiments on a variety images show the success of the method in dealing with different variations in the test images. In terms of computation time, the approach is faster than traditional methods by up to two orders of magnitude and is at least three times faster than a fast implementation of normalized cross correlation.

In this paper we show that Ullman and Basri's linear combination (LC) representation, which was originally proposed for alignment-based object recognition, can be used for outlier detection in motion tracking with an affine camera. For this task LC can be realized either on image frames or feature trajectories, and therefore two methods are developed which we call linear combination of frames and linear combination of trajectories. For robust estimation of the linear combination coefficients, the support vector regression (SVR) algorithm is used and compared with the RANSAC method. SVR based on quadratic programming optimization can efficiently deal with more than 50 percent outliers and delivers more consistent results than RANSAC in our experiments. The linear combination representation can use SVR in a straightforward manner while previous factorization-based or subspace separation methods cannot. Experimental results are presented using real video sequences to demonstrate the effectiveness of our LC+SVR approaches, including a quantitative comparison of SVR and RANSAC.

Example-based learning for computer vision can be difficult when a large number of examples to represent each pattern or object class is not available. In such situations, learning from a small number of samples is of practical value. To study this issue, the task of face expression recognition with a small number of training images of each expression is considered. A new technique based on linear programming for both feature selection and classifier training is introduced. A pairwise framework for feature selection, instead of using all classes simultaneously, is presented. Experimental results compare the method with three others: a simplified Bayes classifier, support vector machine, and AdaBoost. Finally, each algorithm is analyzed and a new categorization of these algorithms is given, especially for learning from examples in the small sample case.

A linear programming technique is introduced that jointly performs feature selection and classifier training so that a subset of features is optimally selected together with the classifier. Because traditional classification methods in computer vision have used a two-step approach: feature selection followed by classifier training, feature selection has often been ad hoc using heuristics or requiring a time-consuming forward and backward search process. Moreover, it is difficult to determine which features to use and how many features to use when these two steps are separated. The linear programming technique used in this paper, which we call feature selection via linear programming (FSLP), can determine the number of features and which features to use in the resulting classification function based on recent results in optimization. We analyze why FSLP can avoid the curse of dimensionality problem based on margin analysis. As one demonstration of the performance of this FSLP technique for computer vision tasks, we apply it to the problem of face expression recognition. Recognition accuracy is compared with results using support vector machines, the AdaBoost algorithm, and a Bayes classifier.

This paper introduces a method for metric self-calibration that is based on a novel decomposition of the fundamental matrix between two views taken by a camera with fixed internal parameters. The method blends important advantages of the Kruppa constraints and the modulus constraint: it works directly from fundamental matrices and uses a reduced-parameter representation for stability. General properties of the new decomposition are also developed, including an intuitive interpretation of the three free parameters of internal calibration. The approach is demonstrated on both real and synthetic data.

The information conveyed by optical flow is analytically linked to observer motion by decomposing the optical flow field into its vector field components. It is shown that the observer may recover his ego-motion by interpreting the decomposed optical flow field, and he may further utilize his mobility to actively control the shape of the optical flow field, which directly reflects the surface shape of the object. The information of surface geometry discontinuity can be derived more directly from optical flow field by segmenting the whole field. A new method for the segmentation is proposed here, which combines both the magnitude and phase parts of the optical flow. The integration of these two different kinds of information proves to be effective in making various surface geometry boundaries explicit.

We introduce the problem of view interpolation for dynamic scenes. Our solution to this problem extends the concept of view morphing and retains the practical advantages of that method. We are specifically concerned with interpolating between two reference views captured at different times, so that there is a missing interval of time between when the views were taken. The synthetic interpolations produced by our algorithm portray one possible physically-valid version of what transpired in the scene during the missing time. It is assumed that each object in the original scene underwent a series of rigid translations. Dynamic view morphing can work with widely-spaced reference views, sparse point correspondences, and uncalibrated cameras. When the camera-to-camera transformation can be determined, the synthetic interpolation will portray scene objects moving along straight-line, constant-velocity trajectories in world space.

We present a framework combining differential geometry and scale-space to show that local geometric invariants of image contours such as tangent, curvature and derivative of curvature can be computed directly and stably from the raw image itself. To solve the problem of noise amplification by differential operations, scale-parameterized local kernels are used to replace differential operations by integral operations, which can be carried out accurately when we adopt a continuous image model. We also show that tangent estimation along contours can be made quite accurately using only eight tangent estimators (a /spl pi//4 quantization) when contour location is known, and high precision and efficiency in computation can be achieved for each of the invariants regardless of the differential order involved.

A novel scene reconstruction technique is presented, different from previous approaches in its ability to cope with large changes in visibility and its modeling of intrinsic scene color and texture information. The method avoids image correspondence problems by working in a discretized scene space whose voxels are traversed in a fixed visibility ordering. This strategy takes full account of occlusions and allows the input cameras to be far apart and widely distributed about the environment. The algorithm identifies a special set of invariant voxels which together form a spatial and photometric reconstruction of the scene, fully consistent with the input images. The approach is evaluated with images from both inward- and outward-facing cameras.

The question of which views map be inferred from a set of basis images is addressed. Under certain conditions, a discrete set of images implicitly describes scene appearance for a continuous range of viewpoints. In particular it is demonstrated that two basis views of a static scene determine the set of all views an the line between their optical centers. Additional basis views further extend the range of predictable views to a two- or three-dimensional region of viewspace. These results are shown to apply under perspective projection subject to a generic visibility constraint called monotonicity. In addition, a simple scanline algorithm is presented for actually generating these views from a set of basis images. The technique, called view morphing map be applied to both calibrated and uncalibrated images. At a minimum, two basis views and their fundamental matrix are needed. Experimental results are presented an real images. This work provides a theoretical foundation for image-based representations of 3D scenes by demonstrating that perspective view synthesis is a theoretically well-posed problem.

Article View morphing Share on Authors: Steven M. Seitz Department of Computer Sciences, University of Wisconsin--Madison, 1210 W. Dayton St., Madison WI Department of Computer Sciences, University of Wisconsin--Madison, 1210 W. Dayton St., Madison WIView Profile , Charles R. Dyer Department of Computer Sciences, University of Wisconsin--Madison, 1210 W. Dayton St., Madison WI Department of Computer Sciences, University of Wisconsin--Madison, 1210 W. Dayton St., Madison WIView Profile Authors Info & Claims SIGGRAPH '96: Proceedings of the 23rd annual conference on Computer graphics and interactive techniquesAugust 1996 Pages 21–30https://doi.org/10.1145/237170.237196Online:01 August 1996Publication History 471citation3,015DownloadsMetricsTotal Citations471Total Downloads3,015Last 12 Months115Last 6 weeks4 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access

A technique is presented for computing 3D scene structure from point and line features in monocular image sequences. Unlike previous methods, the technique guarantees the completeness of the recovered scene, ensuring that every scene feature that is detected in each image is reconstructed. The approach relies on the presence of four or more reference features whose correspondences are known in all the images. Under an orthographic or affine camera model, the parallax of the reference features provides constraints that simplify the recovery of the rest of the visible scene. An efficient recursive algorithm is described that uses a unified framework for point and line features. The algorithm integrates the tasks of feature correspondence and structure recovery, ensuring that all reconstructible features are tracked. In addition, the algorithm is immune to outliers and feature drift, two weaknesses of existing structure from motion techniques. Experimental results are presented for real images.>

We present an approach for identifying the occluding contour and determining its sidedness using an active (i.e., moving) observer. It is based on the non-stationarity property of the visible rim: When the observer's viewpoint is changed, the visible rim is a collection of curves that "slide," rigidly or non-rigidly over the surface. We show that the absenter can deterministically choose three views on the tangent plane of selected surface points to distinguish such curves from stationary surface curves (i.e., surface markings). Our approach demonstrates that the occluding contour can be identified directly, i.e., without first computing surface shape (distance and curvature).>

What real-time, qualitative viewpoint-control behaviors are important for performing global visual exploration tasks such as searching for specific surface markings, building a global model of an arbitrary object, or recognizing an object? In this paper we consider the task of purposefully controlling the motion of an active, monocular observer in order to recover a global description of a smooth, arbitrarily-shaped object using the occluding contour. By studying the epipolar parameterization, we develop two basic behaviors that allow reconstruction of a patch around any point in a reconstructible surface region. These behaviors rely only on information extracted directly from images (e.g., tangents to the occluding contour), and are simple enough to be executed in real time. We then show how global surface reconstruction can be provably achieved by (1) integrating these behaviors to iteratively "grow" the reconstructed regions, and (2) obeying four simple rules.>

Current approaches for detecting periodic motion assume a stationary camera and place limits on an object's motion. These approaches rely on the assumption that a periodic motion projects to a set of periodic image curves, an assumption that fails in general. Using affine-invariance, we derive necessary and sufficient conditions for an image sequence to be the projection of a periodic motion. No restrictions are placed on either the motion of the camera or the object. Our algorithm is shown to be provably-correct for noise-free data and is easily extended to be robust with respect to occlusions and noise. The extended algorithm is evaluated with real and synthetic image sequences.>

An approach is presented for exploring an unknown, arbitrary surface in three-dimensional (3D) space by a mobile robot. The main contributions are (1) an analysis of the capabilities a robot must possess and the trade-offs involved in the design of an exploration strategy, and (2) two provably-correct exploration strategies that exploit these trade-offs and use visual sensors (e.g., cameras and range sensors) to plan the robot's motion. No such analysis existed previously for the case of a robot moving freely in 3D space. The approach exploits the notion of the occlusion boundary, i.e., the points separating the visible from the occluded parts of an object. The occlusion boundary is a collection of curves that "slide" over the surface when the robot's position is continuously controlled, inducing the visibility of surface points over which they slide. The paths generated by our strategies force the occlusion boundary to slide over the entire surface. The strategies provide a basis for integrating motion planning and visual sensing under a common computational framework.>

In order to develop a foundation for visualization, we develop lattice models for data objects and displays that focus on the fact that data objects are approximations to mathematical objects and real displays are approximations to ideal displays. These lattice models give us a way to quantize the information content of data and displays and to define conditions on the visualization mappings from data to displays. Mappings satisfy these conditions if and only if they are lattice isomorphisms. We show how to apply this result to scientific data and display models, and discuss how it might be applied to recursively defined data types appropriate for complex information processing.>

The following problem is considered: how should an observer change viewpoint in order to generate a dense image sequence of an arbitrary smooth surface so that it can be incrementally reconstructed using the occluding contour and the epipolar parameterization? A collection of qualitative behaviors is presented that, when integrated appropriately, purposefully control viewpoint based on the appearance of the surface in order to provably solve this problem.>

Currently the aspect graph is computed from the theoretical standpoint of perfect resolution in object shape, the viewpoint and the projected image. This means that the aspect graph may include details that an observer could never see in practice. Introducing the notion of scale into the aspect graph framework provides a mechanism for selecting a level of detail that is "large enough" to merit explicit representation. This effectively allows control over the number of nodes retained in the aspect graph. This paper introduces the concept of the scale space aspect graph, defines three different interpretations of the scale dimension, and presents a detailed example for a simple class of objects, with scale defined in terms of the spatial extent of features in the image.>

The findings of a workshop, the goals of which were to identify applications, research problems, and designs of high performance computing and communications (HPCC) systems for supporting applications are discussed. In computer vision, the main scientific issues are machine learning, surface reconstruction, inverse optics and integration, model acquisition, and perception and action. In speech and natural language processing (SNLP), issues were identified statistical analysis in corpus-based speech and language understanding, search strategies for language analysis, auditory and vocal-tract modeling, integration of multiple levels of speech and language analyses, and connectionist systems. In AI, important issues that need immediate attention include the development of efficient machine learning and heuristic search methods that can adapt to different architectural configurations, and the design and construction of scalable and verifiable knowledge bases, active memories, and artificial neural networks.>

Currently the aspect graph is computed under the assumption of perfect resolution in the viewpoint, the projected image, and the object shape. Visual detail is represented that an observer might never see in practice. By introducing scale into this framework, a mechanism is provided for selecting levels of detail that are large enough to merit explicit representation, effectively allowing control over the size of the aspect graph. To this end the scale space aspect graph is introduced, and an interpretation of the scale dimension in terms of the spatial extent of image features is considered. A brief example is given for polygons in a plane.>

An approach for recovering surface shape from the occluding contour using an active (i.e., moving) observer is presented. It is based on a relationship between the geometries of a surface in a scene and its occluding contour: If the viewing direction of the observer is along a principal direction for a surface point whose projection is on the contour, surface shape (i.e., curvature) at the surface point can be recovered from the contour. An observer that purposefully changes viewpoint in order to achieve a well-defined geometric relationship with respect to a 3D shape prior to its recognition is used. It is shown that there is a simple and efficient viewing strategy that allows the observer to align the viewing direction with one of the two principal directions for a point on the surface. Experimental results demonstrate that the method can be easily implemented and can provide reliable shape information.>

A technique for defining graphical depictions for all the data types defined in an algorithm is presented. The ability to display arbitrary combinations of an algorithm's data objects in a common frame of reference, coupled with interactive control of algorithm execution, provides a powerful way to understand algorithm behavior. Type definitions are constrained so that all primitive values occurring in data objects are assigned scalar types. A graphical display, including user interaction with the display, is modeled by a special data type. Mappings from the scalar types into the display model type provide a simple user interface for controlling how all data types are depicted, without the need for type-specific graphics logic.>

Reports from five research centers involved with atmospheric and environmental visualization issues are presented in this case study. Visualization with heterogeneous computer architectures is highlighted in the US EPA Scientific Visualization Center discussion. The NASA Marshall Space Flight Center effort to develop the multidimensional analysis of sensor systems (MASS) environment is presented. Florida State University's building of a new scientific visualization package, Sci An, is reported. This is followed by a discussion of the design and implementation of VIS-AD, an experimental laboratory for developing scientific algorithms, at the University of Wisconsin-Madison. The visualization of global atmospheric data at IBM Thomas J. Watson Research Center is highlighted.>

A spatiotemporal (ST) image cube, created by stacking a temporally dense sequence of images together, is a temporally coherent data representation. Using ST surface flow, i.e., the extension of optical flow to ST surfaces, it is shown how ST flow curves can be recovered and then used to detect groups of flow curves such that each group represents a single object or surface in the scene undergoing motion. The algorithm forms clusters of similar flow curves and is based on constraints called the temporal uniqueness constraints. First, a point in an image can only move to at most one point in the next image. Second, a point in an image can come from at most one point in the previous image. When these constraints are violated, or it appears that they are violated, occlusion or disocclusion has occurred and therefore can also be detected. Successful grouping of coherent regions of the ST cube for two gray-level image sequences is shown.>

It is observed that arc length of a contour does not change if that contour is moved in the direction of motion on the surface. A function measuring arc length change is defined. The direction of motion of a contour undergoing motion parallel to the image plane is shown to be perpendicular to the gradient of this function. This gradient approximates the direction of motion when object motion in the scene is not parallel to the image plane or when perspective projection is used. This method has been implemented and is shown to compute the ST (spatiotemporal) surface flow in sequences of edge images and gray level images.>

A viewer-centered approach is presented to modeling the geometry of the visible occluding contour of solid 3-D shape. The rim appearance representation models the exact appearance of the occluding contour formed by the edges of a polyhedron. An algorithm is presented for constructing the rim appearance representation. Bounds on space and time are given, and implementation results show that the rim appearance representation is significantly smaller than both the aspect graph and the asp representation.>

Cyclic motion is formally defined as repeating curvature values along a path of motion. A procedure is presented for cyclic motion detection using spatiotemporal (ST) surfaces and ST curves. The projected movement of an object generates ST surfaces. ST curves are detected on the ST surfaces, providing an accurate, compact, qualitative description of the ST surfaces. Curvature scale-space of the ST curves is then used to detect intervals of repeating curvature values. The successful detection of cyclic motion in two data sets is presented.>

The problem in which the 3D motion of an object corresponding to a known polyhedral model is to be computed using only the motion of edge features in a continuous stream of 2D images is considered. Advantage is taken of the spatiotemporal density of the input signal and the limitations of long-range trajectory-prediction methods are avoided. Two parallel algorithms which use feature-based, short-range (spatiotemporally local) motion processes to achieve real-time tracking of modeled objects are presented. Both algorithms have been implemented and tested on a tightly coupled multiprocessor system consisting of an Aspex Pipe for low-level image-feature computations and a Sequent Symmetry for high-level model-based computations. An analysis is given of the actual performance limits of each method using the current hardware configuration.>

The authors' approach to stereo vision involves: (1) designing a general-purpose low-level matching algorithm: (2) testing it on a wide range of stereo pairs; and (3) adding mechanisms to the low-level algorithm that solve any remaining problems without interfering with its successful behavior. They begin by building the general support algorithm (GSA), a low-level matching algorithm that integrates the influence of a number of a locally defined constraints cooperatively and in parallel using only positive constraint influences (except for uniqueness). The constraints include uniqueness, coarse-to-fine and fine-to-coarse multiresolution, detailed match, figural continuity, and the disparity gradient. The GSA is implemented in a connectionist network. When tested on a wide-range of natural and synthetic images it produces a high percentage (97%) of correct matching decisions. The errors arise in situations that can not be identified using locally-defined constraints. These include partially occluded periodic regions, occlusions, and significant structural differences between the images.>

The combined use of binocular and new trinocular matching constraints in the Trinocular General Support Algorithm's (TGSA) parallel relaxation computation is shown to overcome many of the problems in binocular sterm matching. These problems include: (I) ambiguity in matching in periodic regions, especially when such a region is partially-occluded, (2) erroneous matches near occluded regions, and (3) missing and erronwus matches due to significant structural variations between the images. The TGSA employs cameras positioned at the vertices of an isosceles right triangle. Matching takes place between the horizontally-aligned pair of images and the vertically-aligned pair of images. Along with a variety of binocular constraints, new trinocular constraints, called trinocular uniqueness and the trinocular disparity gradient, are used to relate vertical and horizontal matches. When combined using a connectionist network relaxation algorithm, these constraints help to overcome the binocular matching problems listed above. For example, the trinocular disparity gradient provides enough information to directly resolve ambiguity in periodic regions in many cases. The TGSA has been tested on a number of image triples to demonstrate its advantages over previous binocular and trinocular stereo matching algorithms.

In this paper we present tight bounds on the maximum size of aspect graphs and give worstcase optimal algorithms for their construction, first in the convex case and then in the general case. In particular, we give upper and lower bounds on the maximum size (including vertex labels) of Θ(n3) and Θ(n5) and algorithms for constructing the aspect graph which run in time O(n3) and O(n5) for the convex and general cases respectively. The algorithm for the general case makes use of a new 3D object representation called the aspect representation or asp. We also show a different way to label the aspect graph in order to save a factor of n in the asymptotic size (at the expense of label retrieval time) in both the convex and general cases, and we suggest alternatives to the aspect graph which require less space and store more information.

A shape smoothing algorithm is presented which uses properties of the medial axis to define segments of the shape border and their prominence relative to the local radius of the shape. Prominence values are used to classify axes as either major or minor, and minor axes are then deleted. An augmented medial axis transform is also defined using an approximate Euclidean distance transform, and medial axis interpolation and linking.

An image segmentation algorithm is described that uses an overlapped pyramid to represent an image at multiple levels of resolution. The procedure `lifts' objects to levels of lower and lower resolution until they become `spot' or `streaklike' and are identifiable by local processing (using 3 by 3 operators). They are then `rooted.' The result is a forest embedded within the pyramid, with the single tree rooted at the pyramid apex representing the background regions and the remaining trees representing compact object regions. In addition to the definition of the pyramid linking algorithm, the convergence of the algorithm is proved and optimal rooting rules for binary images are analyzed.

An automatic method of inspecting the scaling accuracy of needle-type instrument gauges using a two-stage Hough transform technique is described. The system measures and verifies the relative accuracy of a gauge's response to a specified set of analog input signals. The method does not require that the gauge's position, orientation, or size be known a priori and the algorithm is very suitable for high-speed hardware implementation.

This paper introduces a generalization of cellular automata in which each celi is a tape-bounded Turing machine rather than a finite-state machine. Fast algorithms are given for performing various basic image processing tasks by such automata. It is suggested that this model of parallel computation is a very suitable one for studying the advantages of parallelism in this domain.

Two approaches to organ detection in ab dominal computerized tomography scans, one local and one global, have been developed. The first involves a boundary-delineating algorithm which operates homogeneously on entire images and coordinates the use of multiple local criteria for advanced edge detection. The second involves an iterative, adaptive boundary-delineating algorithm suitable for single organ detection and amenable to the incorporation of organ-specific knowledge. Regional results of the first (global) algorithm can initialize the second (local) algorithm. Algorithms were tested on mathematical phantoms prior to application on clinical patient data.

Elongated black objects in black-and-white pictures can be ``thinned'' to arcs and curves, without changing their connectedness, by (repeatedly) deleting black border points whose deletion does not locally disconnect the black points in their neighborhoods. This technique generalizes to gray-scale pictures if we use a weighted definition of connectedness: two points are ``connected'' if there is a path joining them on which no point is lighter than either of them. We can then ``thin'' dark objects by changing each point's gray level to the minimum of its neighbors' gray levels, provided this does not disconnect any pair of points in its neighborhood. Examples illustrating the performance of this technique are given.

Three standard approaches to automatic texture classification make use of features based on the Fourier power spectrum, on second-order gray level statistics, and on first-order statistics of gray level differences, respectively. Feature sets of these types, all designed analogously, were used to classify two sets of terrain samples. It was found that the Fourier features generally performed more poorly, while the other feature sets all performned comparably.