Unlike existing global shape-from-shading algorithms which involve the brightness constraint in their formulation, we propose a new algorithm which replaces the brightness constraint by an intensity gradient constrain...
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Unlike existing global shape-from-shading algorithms which involve the brightness constraint in their formulation, we propose a new algorithm which replaces the brightness constraint by an intensity gradient constraint. This is a global approach which obtains the solution by the minimization of an error function over the entire image. Through the linearization of the reflectance map and the discretization of the surface gradient, the intensity gradient can be expressed as a linear function of the surface height. A quadratic error function, which involves the intensity gradient constraint and the traditional smoothness constraint, is minimized efficiently by solving a sparse linear system using the multigrid technique. Neither the information at singular points nor the information at occluding boundaries is needed for the initialization.< >
Indexing is an efficient method of recovering match hypotheses in model-based object recognition. Unlike other methods, which search for viewpoint-invariant shape descriptors to use as indices, we use a learning metho...
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Indexing is an efficient method of recovering match hypotheses in model-based object recognition. Unlike other methods, which search for viewpoint-invariant shape descriptors to use as indices, we use a learning method to model the smooth variation in appearance of local feature sets (LFS). Indexing from LFS effectively deals with the problems of occlusion and missing features. The indexing functions generated by the learning method are probability distributions describing the possible interpretations of each index value. During recognition, this information can be used to select the least ambiguous features for matching. A verification stage follows so that the final reliability and accuracy of the match is greater than that from indexing alone. This approach has the potential to work with a wide range of image features and model types.< >
This paper presents a framework for analyzing salient patterns (vortex structures) in the velocity fields of turbulent fluid flows. Vortices are modeled as rotational motions in the velocity fields and concentrations ...
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This paper presents a framework for analyzing salient patterns (vortex structures) in the velocity fields of turbulent fluid flows. Vortices are modeled as rotational motions in the velocity fields and concentrations in the corresponding vorticity fields. A pointwise linear model is then built to approximate the kinematics of the flow field. Locally, the vector fields are analyzed in terms of fluid motion and singular patterns. The regions of vortex structures are then extracted by identifying those dominated by rotational motion or those of focus-type singularity. In addition to this, 2D vortices, as a special case, are detected by searching regions of vorticity concentrations. The algorithm is applied to both 2D and 3D, experimental and computational turbulent flows.< >
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...
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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).< >
We develop two algorithms to register a range scan to a previous scan so as to compute relative robot positions in an unknown environment. The first algorithm is used on matching tangent lines defined on two scans and...
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We develop two algorithms to register a range scan to a previous scan so as to compute relative robot positions in an unknown environment. The first algorithm is used on matching tangent lines defined on two scans and minimizing a distance function. The second algorithm iteratively establishes correspondences between points in the two scans and then solves the point-to-point least-squares problem to compute the relative pose. Our methods avoid the use of localized features. They work in curved environments and can handle partial occlusions.< >
This paper presents a new algorithm for locating the boundaries of textured regions (both step changes and outliers) using a robust estimator. Previous robust image filters perform poorly on binary images, blur edges,...
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This paper presents a new algorithm for locating the boundaries of textured regions (both step changes and outliers) using a robust estimator. Previous robust image filters perform poorly on binary images, blur edges, round corners, and run slowly. The author avoids artifacts on binary images by modelling them as continuous and interpolating values. Information is combined directly between non-adjacent locations to prevent blurring. Corners are sharpened by relabelling mis-classified pixels. The algorithm is made as fast as a Marr-Hildreth edge finder by restructuring the estimator as a series of 2D image operations, using new multi-ring order statistic operators, and running most of the estimator on a randomly sampled image.< >
The basic MINPRAN (MINimize the Probability of RANdomness) technique, introduced by C.V. Stewart (1994), is extended to handle range data taken from complex scenes. Such data often includes: (1) a large numbers of out...
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The basic MINPRAN (MINimize the Probability of RANdomness) technique, introduced by C.V. Stewart (1994), is extended to handle range data taken from complex scenes. Such data often includes: (1) a large numbers of outliers, (2) points from multiple surfaces interspersed over large image regions, and (3) extended regions containing only bad data. The initial version of MINPRAN handles cases (1) and (3). For (2), given an image region containing data from more than one surface, the basic technique tends to favor a single fit that "bridges" two surfaces. We analyze the extent of this problem and introduce two modifications to solve it. The new version of the algorithm, called MINPRAN2, produces extremely good results on difficult range data.< >
We propose an algorithm for the determination of three dimensional shape and perspective based on the response of the human visual system to changes in visual textures. Current computervision algorithms are computati...
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We propose an algorithm for the determination of three dimensional shape and perspective based on the response of the human visual system to changes in visual textures. Current computervision algorithms are computationally intensive and show inherent difficulties in integrating additional cues for the determination of shape, such as shading, contour, or motion. In order to develop a fast and simple mechanism less constrained for integrating other cues, we incorporated aspects of the physiological properties of cortical cells in VI into a network model. We provide psychophysical evidence that the local spatial frequency spectrum is represented by the spatially averaged peak frequency (APF). After normalization, this APF measures texture compression and leads to estimates of 3D shape and depth. Simulations of the model show good agreement with human responses to a range of textured images.< >
A recognition strategy consisting of a mixture of indexing on invariants and search, allows objects to be recognised up to a Euclidean ambiguity with an uncalibrated camera. The approach works by using projective inva...
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A recognition strategy consisting of a mixture of indexing on invariants and search, allows objects to be recognised up to a Euclidean ambiguity with an uncalibrated camera. The approach works by using projective invariants to determine all the possible projectively equivalent models for a particular imaged object; then a system of global consistency constraints is used to determine which of these projectively equivalent, but Euclidean distinct, models corresponds to the objects viewed. These constraints follow from properties of the imaging geometry. In particular, a recognition hypothesis is equivalent to an assertion about, among other things, viewing conditions and geometric relationships between objects, and these assertions must be consistent for hypotheses to be correct. The approach is demonstrated to work on images of real scenes consisting of polygonal objects and polyhedra.< >
This paper unifies "line-process" approaches for regularization with discontinuities and robust estimation techniques. We generalize the notion of a "line process" to that of an analog "outlie...
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This paper unifies "line-process" approaches for regularization with discontinuities and robust estimation techniques. We generalize the notion of a "line process" to that of an analog "outlier process" and show that a problem formulated in terms of outlier processes can be viewed in terms of robust statistics. We also characterize a class of robust statistical problems for which an equivalent outlier-process formulation exists and give a straightforward method for converting a robust estimation problem into an outlier-process formulation. This outlier-processes approach provides a general framework which subsumes the traditional line-process approaches as well as a wide class of robust estimation problems. Examples in image reconstruction and optical flow are used to illustrate the approach.< >
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