The issue of recognizing 3D elongated objects from 2D intensity images is addressed. A tube model, locally similar to generalized cones, is developed for the class of elongated objects. A recognition strategy that com...
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The issue of recognizing 3D elongated objects from 2D intensity images is addressed. A tube model, locally similar to generalized cones, is developed for the class of elongated objects. A recognition strategy that combines 2D contour properties and surface shading information is used to exploit the power of the 3D model. Reliable contours provide constraints for localizing the objects of interest. The theory of optimal filters is adopted in verifying the shading of hypothesized objects. Object recognition is achieved through optimizing the signal-to-noise response with respect to model parameters. A sweeping operation is proposed as a further stage of identifying objects so that the overall performance of the system does not heavily rely on the quality of local feature detection.< >
The problem of egomotion recovery has been treated by using as input local image motion, with the published algorithms utilizing the geometric constraint relating 2-D local image motion (optical flow, correspondence, ...
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The problem of egomotion recovery has been treated by using as input local image motion, with the published algorithms utilizing the geometric constraint relating 2-D local image motion (optical flow, correspondence, derivatives of the image flow) to 3-D motion and structure. Since it has proved very difficult to achieve accurate input (local image motion), a lot of effort has been devoted to the development of robust techniques. A new approach to the problem of egomotion estimation is taken, based on constraints of a global nature. It is proved that local normal flow measurements form global patterns in the image plane. The position of these patterns is related to the three dimensional motion parameters. By locating some of these patterns, which depend only on subsets of the motion parameters, through a simple search technique, the 3-D motion parameters can be found. The proposed algorithmic procedure is very robust, since it is not affected by small perturbations in the normal flow measurements. As a matter of fact, since only the sign of the normal flow measurement is employed, the direction of translation and the axis of rotation can be estimated with up to 100% error in the image measurements.< >
A multichannel filtering-based texture segmentation method is applied to a variety of document imageprocessing problems: text-graphics separation, address-block location, and bar code localization. In each of these s...
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We investigate the performance of selected texture models for the purpose of land use classification. The texture models are evaluated based on the resulting classification error rates. Three classes of texture models...
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The minirhizotron technique has provided agricultural scientists the opportunity of observing rhizosphere activities without destroying root structures. Nonetheless, the laborious analysis of the data still prohibits ...
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The minirhizotron technique has provided agricultural scientists the opportunity of observing rhizosphere activities without destroying root structures. Nonetheless, the laborious analysis of the data still prohibits its wide applications. Advanced image understanding techniques are needed to derive satisfactory descriptions of plant root networks in an efficient and robust way. The paper presents a plant root image analysis system designed as a blackboard architecture with a hierarchy of data abstractions. Important properties of plant roots are used throughout the processing and multiple sources of information are combined to resolve uncertainties in image interpretation. Experimental results from some stages of the research are given which support the overall processing scheme.< >
Based on the assumption that most probability densities in real life can be approximated by a mixture of Gaussian densities, we propose here a three-layer adaptive network with each neuron in the lower hidden layer re...
Based on the assumption that most probability densities in real life can be approximated by a mixture of Gaussian densities, we propose here a three-layer adaptive network with each neuron in the lower hidden layer representing a Gaussian basis function (covariance matrix equal to where I is a unit matrix) to estimate various probability densities and serve as a Bayes classifier. The width of the basis function may be the same for all neurons in this layer or it may vary from one neuron to another. This paper investigates the effectiveness of the network for both cases and presents a localized learning algorithm to adjust the network parameters. The network was trained with artificial data derived from known mixtures of memoryless Gaussian sources as well as exponential and Gamma densities. The performance of the network as a pattern density estimator was measured in terms of the relative difference between the target probability density function (p.d.f.) which generates the training and testing data and the network output representing the estimation. Samples from two mixtures corresponding to two classes were used to test the network capability as a classifier by comparing its error rate against that of a Bayes classifier. Both one- and two-dimensional cases were explored. The successfulness of the network depended on how well the target p.d.f.’s were represented by the training samples, the number of hidden neurons employed in the network and how thoroughly the network was trained. It was also found that allowing each basis function to have an independent width had a predominant effect on the network performance.
The authors outline their approach for automatic translation of geometric entities produced by a CAD system into a relational graph structure. They have developed a system which uses 3-D object descriptions created on...
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The authors outline their approach for automatic translation of geometric entities produced by a CAD system into a relational graph structure. They have developed a system which uses 3-D object descriptions created on a commercial CAD system and expressed in the industry-standard IGES form, and performs geometric inferencing to object a relational graph representation of the object which can be stored in a database of models of object recognition. Details of the IGES standard, the geometric inference engine, and some formal properties of 3-D models are discussed. In addition to the process of translation from one data format to another, the interference engine extracts higher-level information from the CAD model and stores it explicitly in the new data structure. The higher-level features will allow the search space explored during the object recognition stage to be pruned early.< >
作者：
Y. T. ChienTheodosios PavlidisGuest Editor
Professor and Head of the Department of Electrical Engineering and Computer Science. Guest Editor
member of the Association for Computing Machinery and Sigma Xi member of the editorial committee of the IEEE TRANSACTIONS OF PATTERN ANALYSIS AND MACHINE INTELLIGENCE Associate Editor of the Bulletin of Mathematical Biology Computer Graphics and Image Processing and Pattern Recognition.
This Special Issue is composed of the papers selected from the 1978 IEEE computer Society Workshop on patternrecognition (PR) and Artificial Intelligence (Al) held in Princeton, NJ, April 12-14, 1978. The Workshop wa...
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This Special Issue is composed of the papers selected from the 1978 IEEE computer Society Workshop on patternrecognition (PR) and Artificial Intelligence (Al) held in Princeton, NJ, April 12-14, 1978. The Workshop was sponsored by the Technical Committee on Machine Intelligence and pattern Analysis. Inevitably, the contributors to the Workshop determined, to a large degree, the tone and complexion of this Special Issue. For this reason, a brief account of the Workshop Proceedings, though now history, is given. About half of the papers presented at the Workshop were also submitted for the Special Issue, a total of 37. Those of high quality were far more than the number that could be accommodated within the available number of pages. We decided to choose three topics where the interaction between the methodologies of PR and Al was most prevelant: analysis of images, analysis of speech, and certain general algorithms. All the selected papers present either theoretical, or experimental results, or both. We felt that such results clearly demonstrate the progress achieved and can be seen as very impressive if measured against the difficult problem of emulating functions associated with human intelligence by machines. It is true that they often fall short from some of the earlier ambitious goals, but the time is probably ripe to reexamine such goals in view of the accumulated experience. The following is a brief scanning of the contents of this issue, especially as related to the integration and/or interaction of PR and Al methodologies.
This book constitutes the refereed proceedings of the 9th IAPR-TC-15 International Workshop on Graph-Based Representations in patternrecognition, GbRPR 2013, held in Vienna, Austria, in May 2013. The 24 papers presen...
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ISBN:
(数字)9783642382215
ISBN:
(纸本)9783642382208
This book constitutes the refereed proceedings of the 9th IAPR-TC-15 International Workshop on Graph-Based Representations in patternrecognition, GbRPR 2013, held in Vienna, Austria, in May 2013.
The 24 papers presented in this volume were carefully reviewed and selected from 27 submissions. They are organized in topical sections named: finding subregions in graphs; graph matching; classification; graph kernels; properties of graphs; topology; graph representations, segmentation and shape; and search in graphs.
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