We propose a new texture-based page segmentation algorithm which automatically extracts the text, halftone, and line-drawing regions from input greyscale document images. This approach utilizes a neural network to tra...
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We propose a new texture-based page segmentation algorithm which automatically extracts the text, halftone, and line-drawing regions from input greyscale document images. This approach utilizes a neural network to train a set of masks which is optimal for discriminating the three main texture classes in the page segmentation problem: halftone, background, and text and line-drawing regions. The test and line-drawing regions are further discriminated based on connectivity analysis. We have applied the algorithm to successfully segment English and Chinese document images. We also demonstrate that the masks can perform language separation (English/Chinese) when appropriately trained.
In this article a multimedia computer-assisted learning (MCAL) system is presented. The major objective of this work was to investigate the potential of using such systems as tools for transferring instructional cours...
In this article a multimedia computer-assisted learning (MCAL) system is presented. The major objective of this work was to investigate the potential of using such systems as tools for transferring instructional course information through various types of computer media as opposed to the classic CAL systems. The philosophy and techniques employed to design the system are investigated. Usage of the implemented system and its merits have been illustrated through its application to teach engineering students and technicians the theory and concepts of marine radar. System design, implementation, test, and revision phases are presented and discussed.
While the view of constructive and hierarchical vision prevails, the issues of cooperation and competition among individual modules become crucial. These issues are directly related to one of the most important aspect...
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While the view of constructive and hierarchical vision prevails, the issues of cooperation and competition among individual modules become crucial. These issues are directly related to one of the most important aspect...
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ISBN:
(纸本)078031865X
While the view of constructive and hierarchical vision prevails, the issues of cooperation and competition among individual modules become crucial. These issues are directly related to one of the most important aspects in computer vision research: integration. A major source of difficulty in developing a consistent and systematic integration formalism is the heterogeneity existing in modules, in information, and in knowledge. The author exploits, using the central theme of grouping, the homogeneous characteristics in vision problem solving and proposes a general framework, called hierarchical token grouping, that facilitates vision problem solving by providing a consistent and systematic environment for integrating modules, cues, and knowledge, all in a globally coherent mechanism.< >
This paper addresses the issue of tracking tubular objects, particularly blood vessels from MR images. A model-based approach is adopted. The generalized stochastic tube (GST) model is developed which is an extension ...
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A model-based approach is used for recognizing arterial blood vessels from MRA volumetric data. The modeling includes (1) a generalized stochastic tube model characterizing the structural properties of the vessels, an...
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This paper addresses the issue of tracking tubular objects, particularly blood vessels from MR images. A model-based approach is adopted. The generalized stochastic tube (GST) model is developed which is an extension ...
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This paper addresses the issue of tracking tubular objects, particularly blood vessels from MR images. A model-based approach is adopted. The generalized stochastic tube (GST) model is developed which is an extension of our previously proposed (1993) generalized tube (GT) model. Transitions among adjacent tubes are explicitly parameterized. Integrated with a bivariate Gaussian density function adopted to model the blood flow within cross sections, the GST model is applied to tracking blood vessels in MRA volumetric data. Experimental results on both synthetic data with different degrees of Gaussian noise and real MRA data demonstrated that simultaneously utilizing both models yields robust performance under noisy conditions.
＜正＞Feature extraction is very important for the classifier design and the overall performance of *** recognition ***,due to the lack of theoretical guidances,feature extraction and classifier design are usually tre...
＜正＞Feature extraction is very important for the classifier design and the overall performance of *** recognition ***,due to the lack of theoretical guidances,feature extraction and classifier design are usually treated separately in current speech recognition *** *** proposes an approach to combine linear feature extraction with continuous density hidden Markov modeling（HMM） which is currently the most successful speech pattern classifier.A maximumlikelihood based algorithm is derived to iteratively train HMM parameters as well as the parameters of the feature *** algorithm is an exteusion of the Baum-Welcli parameter re-estimation algorithm for conventional HMMs and thus has a nice property of guara, nteed convergence.
Many biological objects are elongated. This research addresses the issue of recognizing elongated objects from both 2D intensity images and 3D volumes. A mathematical model, called tube model, is developed for this cl...
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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, ...
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