A postprocessing approach that uses both statistical and structural information to eliminate the false minutiae in skeleton fingerprint images is described. The attributes of fingerprint ridges are presented by statis...
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ISBN:
(纸本)0879425970
A postprocessing approach that uses both statistical and structural information to eliminate the false minutiae in skeleton fingerprint images is described. The attributes of fingerprint ridges are presented by statistics, and these attributes are used to describe the structure of the false minutiae. The proposed approach analyzes the entire minutia structures, not just in a small window, much like the way humans analyze a fingerprint pattern. To verify the effectiveness of the approach, ten processed images with different shapes were contrasted with rolled fingerprint cards. False minutiae made up 36% of the total minutiae before processing and only about 4% after processing. Moreover, the ridge separation and continuity are good throughout the fingerprint image.
A description is presented of BONSAI, a model-based 3-D object recognition system, which identifies and localizes 3-D objects in range images of one or more parts which have been designed on a CAD system. recognition ...
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ISBN:
(纸本)0818620579
A description is presented of BONSAI, a model-based 3-D object recognition system, which identifies and localizes 3-D objects in range images of one or more parts which have been designed on a CAD system. recognition is performed via constrained search of the interpretation tree, using unary and binary constraints (derived automatically from the CAD models) to prune the search space. Experiments with over 200 images of 20 different parts demonstrate that the constrained search approach to 3-D object recognition has comparable accuracy to other existing systems.
A novel approach is presented for pruning the amount of search needed to match image features to object models. The technique relies on active networks which capture various visibility and geometric constraints betwee...
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ISBN:
(纸本)0818620579
A novel approach is presented for pruning the amount of search needed to match image features to object models. The technique relies on active networks which capture various visibility and geometric constraints between features of a model to prune these features from search space during matching. The networks, which can be efficiently implemented in Boolean logic, integrate harmoniously with the previous work in feature recognition and object matching. A method is proposed for clustering model features (vsets) and four types of constraints which assist in building the networks. The authors show, both analytically and empirically, the dramatic reduction in search provided by activation nets.
An approach to object recognition using cross correlation (CC) is presented. The CC utilizes samples of distances defined from a major axis to points located on the boundary of the object image (sampled boundary dista...
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ISBN:
(纸本)0818690275
An approach to object recognition using cross correlation (CC) is presented. The CC utilizes samples of distances defined from a major axis to points located on the boundary of the object image (sampled boundary distances, or SBDs). With this approach (CCSBD), the recognition of an object under translation, rotation, and uniform scaling can be achieved. The recognition of an object under stretching may also be achieved under the condition that the major axis of the image is not changed after transformation. The generalization of this approach is discussed. In particular, one of the generalizations is the use of axis of symmetry for symmetric images to measure SBDs. This can achieve recognition invariant under translation, rotation, uniform scaling, and stretching along the axis of symmetry and/or the direction perpendicular to it. The experimental results of using CCSBD to recognize some sampling objects are presented. The capability of recognizing objects under a certain type of nonaffine transformation using CCSBD is also investigated.
A model-based recognition method is introduced which is formulated as an optimization problem. An energy function is derived which represents the constraints on the best solution in order to find the best match. A two...
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ISBN:
(纸本)0818620579
A model-based recognition method is introduced which is formulated as an optimization problem. An energy function is derived which represents the constraints on the best solution in order to find the best match. A two-dimensional binary Hopfield neural network is implemented to minimize the energy function. The state of each neuron in the Hopfield network represents the possibility of a match between a node in the model graph and a node in the scene graph.
A method for the classification and segmentation of texture in images is presented. The method derives texture features from the image which are independent of mean gray-scale level but dependent on texture orientatio...
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A method for the classification and segmentation of texture in images is presented. The method derives texture features from the image which are independent of mean gray-scale level but dependent on texture orientation. Each pixel in the original image can then be classified using supervised pattern-recognition techniques. A segmentation of the image can be produced by delineating the boundaries within the classified image in horizontal and vertical directions. The algorithm is efficient and can be implemented using general or specialist processors. Results from a sidescan sonar image of a sea bed and a video image containing samples of real texture are given.
The Markov line process that has been used in some image segmentation and restoration studies is investigated. Realizations from this model are presented for a wide range of parameter values, and the effects of certai...
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The Markov line process that has been used in some image segmentation and restoration studies is investigated. Realizations from this model are presented for a wide range of parameter values, and the effects of certain parameters are studied. The maximum pseudolikelihood (MPL) estimation procedure is implemented for the Markov line process. The MPL procedure is applied to several images generated from the model as well as to a hand-drawn image and the edge-detector output of a natural image. It is expected that improved segmentation and restoration results can be obtained, if the Markov line process model is fine-tuned to the class of images under consideration, by estimating the parameters of some typical images in that class.
The mechanics of occlusion of one surface by another are described by a set of integer linear constraints. These constraints insure that the output of a contour grouping process is physically valid and consistent with...
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ISBN:
(纸本)0818620579
The mechanics of occlusion of one surface by another are described by a set of integer linear constraints. These constraints insure that the output of a contour grouping process is physically valid and consistent with the image evidence. Among the many feasible solutions, the most compelling is the solution which best explains the presence and form of image structure. The problem of computing a complete and consistent surface boundary representation is reduced to solving an integer linear program.
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 th...
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ISBN:
(纸本)0818620579
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.
An approach to automatic prediction and detection of ovulation is described. It is based on the application of imageprocessing techniques to the cervical mucus fern test, a popular clinical diagnostic method. The seq...
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ISBN:
(纸本)0818690402
An approach to automatic prediction and detection of ovulation is described. It is based on the application of imageprocessing techniques to the cervical mucus fern test, a popular clinical diagnostic method. The sequence of histogram equalization, filtering, edge detection, binarization, labeling, thinning, Hough transform, and automatic patternrecognition in a feature space is applied to microscopic images of the ferning patterns. This method permits decisions to be made based on quantitative data instead of the subjective evaluations that are presently used.
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