A multidimensional edge model is established and a first-order estimation for multidimensional edge profiles is proposed. An optimal edge location detection algorithm is developed. The advantages of the algorithm are ...
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ISBN:
(纸本)0818621486
A multidimensional edge model is established and a first-order estimation for multidimensional edge profiles is proposed. An optimal edge location detection algorithm is developed. The advantages of the algorithm are that (1) it has little dependence on assumptions of edge models, noise models, or smoothing filters, (2) it has better abilities for detecting very weak edges and making less edge orientation errors than other edge detectors, (3) it can handle corners and complicated multidimensional image structures, and (4) it detects different edge types at the same time.
Two complementary methods for the detection of moving objects by a moving observer are described. The first (constraint ray filtering) uses a constraint that restricts the projected velocity at any image point to a 1-...
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ISBN:
(纸本)0818621486
Two complementary methods for the detection of moving objects by a moving observer are described. The first (constraint ray filtering) uses a constraint that restricts the projected velocity at any image point to a 1-D locus in velocity space to detect motion inconsistant with a rigid world assumption. The second (animate motion detection) utilizes a constraint on the time-rate-of-change of projected velocity due to smooth observer motion to detect moving objects such as animals and maneuvering vehicles whose projected motion changes rapidly. In both cases, the qualitative nature of the constraints allows the methods to be used with the inexact motion information typically available from real image sequences. Implementations of the methods that run in real time on a parallel pipelined imageprocessing system are described.
Rigid 3-D objects were modeled automatically from an image sequence taken by a camera that was rotated around the object. The image sequence was recorded using a calibrated camera which allows one to measure the camer...
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ISBN:
(纸本)0818621486
Rigid 3-D objects were modeled automatically from an image sequence taken by a camera that was rotated around the object. The image sequence was recorded using a calibrated camera which allows one to measure the camera positions and to estimate the true object size. The 3-D object shape was obtained in two steps. The object silhouettes were employed to find the enclosing volume of the object. The volume was converted into a flexible surface representation and the 3-D shape was refined based on the texture information of the object surface. Texture mapping was applied to generate a highly realistic 3-D model of the object.
The surface reconstruction problem is formulated as a two-stage reconstruction procedure. The first stage is a robust local fit to the data in a multiresolution scheme and the second is a regularized least squares fit...
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ISBN:
(纸本)0818621486
The surface reconstruction problem is formulated as a two-stage reconstruction procedure. The first stage is a robust local fit to the data in a multiresolution scheme and the second is a regularized least squares fit with the addition of an adaptive mechanism in the smoothness functional in order to make the solution well behaved. The authors present the details of the second stage in which they use the weighted bicubic spline as a surface representation in a regularization framework, with a Tikhonov stabilizer as the smoothness norm. It is shown how the adaptive weights in the stabilizer help the surface bend across discontinuities by varying the energy of the surface.
A simple MAP model-matching criteron that captures important aspects of recognition in controlled situations is described. A detailed metrical object model is assumed. A probabilistic model of image features is combin...
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ISBN:
(纸本)0818621486
A simple MAP model-matching criteron that captures important aspects of recognition in controlled situations is described. A detailed metrical object model is assumed. A probabilistic model of image features is combined with a simple prior on both the pose and the feature interpretations to yield a mixed objective function. The parameters that appear in the probabilistic models can be derived from images in the application domain. By extremizing the objective function, an optimal matching between model and image feature results. Within this framework, good models of feature uncertainty allow for robustness despite inaccuracy in feature detection. In addition, the relative likelihood of features arising from either the object or the background can be evaluated in a rational way. The objective function provides a simple and uniform means of evaluating match and pose hypotheses. Several linear projection and feature models are discussed. An experimental implementation of MAP model matching, among features derived from low-resolution edge images, is described.
The notion of consensus structure, a special form of random graph, is formally defined. The consensus structure represents an extended higher-order representation of the random n-tuple for statistical and structural p...
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ISBN:
(纸本)0818621486
The notion of consensus structure, a special form of random graph, is formally defined. The consensus structure represents an extended higher-order representation of the random n-tuple for statistical and structural patternrecognition. An algorithm inferring the consensus structure from a random n-tuple is designed based on the detection of statistical interdependency under certain structural constraints. In applying structural constraints, a circular diagram indicating variable interaction is used to extract global structural features in a macromolecular modeling problem.
Multichannel filtering techniques are presented for obtaining both region- and edge-based segmentations of textured images. The channels are represented by a bank of even-symmetric Gabor filters that near uniformly co...
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ISBN:
(纸本)0818621486
Multichannel filtering techniques are presented for obtaining both region- and edge-based segmentations of textured images. The channels are represented by a bank of even-symmetric Gabor filters that near uniformly covers the spatial-frequency domain. Feature images are obtained by subjecting each (selected) filtered image to a nonlinear transformation and computing a measure of energy around each pixel. Region-based segmentations are obtained by using a square-error clustering algorithm. Edge-based segmentations are obtained by applying an edge detector to each feature image and combining their magnitude responses. An integrated segmentation technique that combines the strengths of the previous two techniques while eliminating their weaknesses is proposed. The integrated approach is truly unsupervised, since it eliminates the need for knowing the exact number of texture categories in the image.
A curvature-based approach to estimating nonrigid deformation of moving surfaces is described. conformal motion can characterized by stretching of the surface. At each point this stretching is equal in all directions,...
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ISBN:
(纸本)0818621486
A curvature-based approach to estimating nonrigid deformation of moving surfaces is described. conformal motion can characterized by stretching of the surface. At each point this stretching is equal in all directions, but is different for different points. This stretching function can be defined as an additional (with global translation and rotation) motion parameter. An algorithm for local stretching recovery from Gaussian curvature based on polynomial approximations of the stretching function is presented. These methods require point correspondences between time frames, but not the complete knowledge of nonrigid transformation.
The authors present techniques with which camera viewpoints can be automatically determined in order to satisfy the generic feature detectability requirements of resolution, focus, and field-of-view. These optical con...
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ISBN:
(纸本)0818621486
The authors present techniques with which camera viewpoints can be automatically determined in order to satisfy the generic feature detectability requirements of resolution, focus, and field-of-view. These optical constraints are characterized analytically for a general thick lens model, and complete viewpoint loci that satisfy these constraints are obtained. The results discussed are useful for automating the vision system design process, as well as for programming the vision system itself. In addition, such planning techniques can also automate robot imaging systems that reconfigure themselves in an intelligent manner in order to optimize imaging quality.
Two patternrecognition algorithms are proposed to detect two types of knots in lumber, lumped and surface knots, using an optical scanner. It is shown that lumped knots can be recognized by a log profile approach and...
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ISBN:
(纸本)0818621486
Two patternrecognition algorithms are proposed to detect two types of knots in lumber, lumped and surface knots, using an optical scanner. It is shown that lumped knots can be recognized by a log profile approach and surface knots can be detected by an imageprocessing approach. A mathematical model for describing the geometry of the knots inside the log based on the surface parameters of the knots is presented. By a log breakdown strategy, the log can be cut into a maximum number of boards with prescribed dimensionalities. The mathematical knot model can be used to predict the locations, sizes, orientations, and shapes of the knots on these boards, whereby their lumber grades can be determined. Summing up the value yields of all the boards gives the value yield of the log. By computing all the possible value yields of the log from different log orientations, one can determine a maximum value yield of a log.
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