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 imag...
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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.< >
We present a method for navigating a robot from an initial position to a specified landmark in its visual field, using a sequence of monocular images. The location of the landmark with respect to the robot is determin...
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We present a method for navigating a robot from an initial position to a specified landmark in its visual field, using a sequence of monocular images. The location of the landmark with respect to the robot is determined using the change in size and position of the landmark in the image as the robot moves. The landmark location is estimated after the first three images are taken, and this estimate is refined after each motion. One of the novel aspects of this method is that it uses no explicit three-dimensional information.< >
Motion-based image segmentation becomes inherently ambiguous when apparent motions of different objects are locally or globally similar during a period. To disambiguate the segmentation, temporal coherence between the...
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Motion-based image segmentation becomes inherently ambiguous when apparent motions of different objects are locally or globally similar during a period. To disambiguate the segmentation, temporal coherence between the local image motion at each edge point and the apparent motion of every object is examined over a long sequence. The point is grouped into that segment of the object whose apparent motion is temporally most coherent with the local image motion at the point.< >
Color histogram matching has been shown to be a promising way of quickly indexing into a large image database. Yet, few experiments have been done to test the method on truly large databases, and even if they were per...
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Color histogram matching has been shown to be a promising way of quickly indexing into a large image database. Yet, few experiments have been done to test the method on truly large databases, and even if they were performed, they would give little guidance to a user wondering if the technique would be useful with his or her database. In this paper we define and analyze a measure relevant to extending color histogram indexing to large databases: capacity (how many distinguishable histograms can be stored).< >
We develop a new class of parameterized models based on the linear interpolation of two parameterized shapes using a blending function. Using a small number of additional parameters, blending extends the coverage of s...
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We develop a new class of parameterized models based on the linear interpolation of two parameterized shapes using a blending function. Using a small number of additional parameters, blending extends the coverage of shape primitives. In particular, it offers the ability to construct shapes whose genus can change. Blended models are incorporated into a physics-based shape estimation framework which uses dynamic deformable models with global and local deformations. We present experiments involving the extraction of complex shapes, including an example of dynamic genus change.< >
We present a practical patternrecognition system that is invariant with respect to translation, scale and rotation of objects. The system is also insensitive to large variations of the threshold used. As feature vect...
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ISBN:
(纸本)0818658258
We present a practical patternrecognition system that is invariant with respect to translation, scale and rotation of objects. The system is also insensitive to large variations of the threshold used. As feature vectors, Zernike moments are used and we compare them with Hu's seven moment invariants. For a practical machine vision system, three key issues are discussed: pattern normalization, fast computation of Zernike moments, and classification using k-NN rule. As testing results, the system recognizes a set of 62 alphanumeric machine-printed characters with different sizes, at arbitrary orientations, and with different thresholds where the size of the characters varies from 10/spl times/10 to 512/spl times/512 pixels.< >
Our long-term goal is to develop a trainable tool for locating patterns of interest in large image databases. Toward this goal we have developed a prototype system, based on classical filtering and statistical pattern...
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Our long-term goal is to develop a trainable tool for locating patterns of interest in large image databases. Toward this goal we have developed a prototype system, based on classical filtering and statistical patternrecognition techniques, for automatically locating volcanoes in the Magellan SAR database of Venus. Training for the specific volcano-detection task is obtained by synthesizing feature templates (via normalization and principal components analysis) from a small number of examples provided by experts. Candidate regions identified by a focus of attention (FOA) algorithm are classified based on correlations with the feature templates. Preliminary tests show performance comparable to trained human observers.< >
Complex problems, such as driving, can be solved more easily by decomposing them into smaller subproblems, solving each sub-problem, and then integrating the solutions. In the case of an autonomous vehicle, the integr...
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Complex problems, such as driving, can be solved more easily by decomposing them into smaller subproblems, solving each sub-problem, and then integrating the solutions. In the case of an autonomous vehicle, the integrated system should be able to "react" in real time to a changing environment and to "reason" about ways to achieve its goals. This paper describes the approach taken on the UMass Mobile Perception Laboratory (MPL) to integrate independent processes (each solving a particular aspect of the navigation problem) into a fully capable autonomous vehicle.< >
The paper gives a practical rapid algorithm for doing projective reconstruction of a scene consisting of a set of lines seen in three or more images with uncalibrated cameras. The algorithm is evaluated on real and id...
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The paper gives a practical rapid algorithm for doing projective reconstruction of a scene consisting of a set of lines seen in three or more images with uncalibrated cameras. The algorithm is evaluated on real and ideal data to determine its performance in the presence of varying degrees of noise. By carefully consideration of sources of error, it is possible to get accurate reconstruction with realistic levels of noise. The algorithm can be applied to images from different cameras or the same camera. For images with the same camera with unknown calibration, it is possible to do a complete Euclidean reconstruction of the image. This extends to the case of uncalibrated cameras previous results on scene reconstruction from lines,.< >
This paper describes a method of patternrecognition targeted for recognizing complex annotations found in paper documents. Our investigation is motivated by the high reliability required for accomplishing autonomous ...
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This paper describes a method of patternrecognition targeted for recognizing complex annotations found in paper documents. Our investigation is motivated by the high reliability required for accomplishing autonomous interpretation of maps and engineering drawings. Our approach includes a strategy based on multiscale representations obtained by hexagonal wavelet analysis. A feasibility study is described in which more than 10,000 patterns were recognized with an error rate of 2.06% by a neural network trained using multiscale representations from a class of 52 distinct patterns. We observed a 21-fold reduction in the amount of information needed to represent each pattern for recognition. These results suggest that high reliability is possible at a reduced cost of representation.< >
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