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The 1994 1st IEEE International Conference on image Processing

作者： Huang, Qian Stockman, G.C. IBM Research Division Almaden Research Center 650 Harry Road San JoseCA95120-6099 United States Pattern Recognition and Image Processing Laboratory Department of Computer Science Michigan State University East LansingMI48824 United States

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, and (2) a bivariate Gaussian function, modeling the expected cross sectional blood flow. This integrated model renders the recognition problem as a parameter estimation problem which is subsequently solved in a hierarchical fashion. Due to the descriptive representation for objects, a new visualization scheme for blood vessels is proposed that allows the observation of the blood flow of each cross section along a recognized vessel. Some experimental results from both synthetic data and real MRA volumes are given. The visualization of interior blood flow patterns for some vessels are also shown. © 1994 IEEE.

关键词： Blood vessels

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Voronoi pyramids and Hopfield networks

Voronoi pyramids and Hopfield networks

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International Conference on pattern recognition

作者： H. Bischof E. Bertin P. Bertolino Department f. Pattern Recognition and Image Processing Vienna Austria TIMC-IMAG Laboratory University of Grenoble Grenoble France

Presents an algorithm for image segmentation with irregular pyramids. Instead of starting with the original pixel grid, the authors first apply an adaptive Voronoi tessellation to the image. For irregular pyramid construction the authors present a Hopfield neural network which controls the decimation process. The validity of the authors' approach is demonstrated by several examples in image segmentation.

关键词： image segmentation Convergence Hopfield neural networks pattern recognition image processing Pixel Process control Joining processes Laboratories

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Generalized stochastic tube model: tracking 3D blood vessels in MR images

Generalized stochastic tube model: tracking 3D blood vessels...

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International Conference on pattern recognition

作者： Qian Huang G.C. Stockman Almaden Research Center San Jose CA USA Pattern Recognition and Image Processing Laboratory Department of Computer Science Michigan State University East Lansing MI USA

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.

关键词： Stochastic processes Blood vessels Biomedical imaging Shape Blood flow Gaussian noise image recognition Solid modeling pattern recognition Density functional theory

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Recognizing elongated objects using invariant surface features and matched filters 2

Recognizing elongated objects using invariant surface featur...

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Mathematical Methods in Medical Imaging II 1993

作者： Huang, Qian Stockman, George C. Pattern Recognition and Image Processing Laboratory Department of Computer Science Michigan State University East LansingMI48824 United States

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 class of objects and is effectively utilized in two stages of recognition. The explicit relationships between geometrical surface features and the object model parameters are quantitatively exploited to automatically locate seeds for recognition. Invariant surface features are used to constrain or hypothesize the objects of interest. The verification of a hypothesis is performed by correlating a matched filter, dynamically generated based on the hypothesis, with the sensor data. The tubes identified in such a local recognition process serve as the seeds from which the global recognition process is initiated. Each seed is swept along the trajectory where the best-fit is found. A smooth sweep is controlled by a set of adaptive constraints computed dynamically from an on-line sweeping history. We apply the proposed method to real world data from different application domains. Experimental results are presented and discussed. © 1993 SPIE. All rights reserved.

关键词： Matched filters

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Motion constraint patterns

Motion constraint patterns

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1993 IEEE Workshop on Qualitative Vision, WQV 1993

作者： Fermüller, Cornelia Computer Vision Laboratory Center for Automation Research University of Maryland College ParkMD20742-3275 United States Department for Pattern Recognition and Image Processing Institute for Automation Technical University Vienna Treitlstraße 3 ViennaA-1040 Austria

ISBN: (纸本)0818636920

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. © 1993 IEEE.

关键词： Flow measurement

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Generalized tube model: recognizing 3D elongated objects from 2D intensity images

Generalized tube model: recognizing 3D elongated objects fro...

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Conference on Computer Vision and pattern recognition (CVPR)

作者： Q. Huang G.C. Stockman Pattern Recognition and Image Processing Laboratory Department of Computer Science Michigan State University East Lansing MI USA

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.< >

关键词： image recognition Filters Object recognition Object detection Robustness image processing Laboratories Computer science Filtering theory Power system modeling

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Motion constraint patterns

Motion constraint patterns

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IEEE Workshop on Qualitative Vision

作者： C. Fermuller Department for Pattern Recognition and Image Processing Institute for Automation Technical University of of Vienna Vienna Austria Computer Vision Laboratory Center for Automation Research University of Maryland College Park MD USA

关键词： Motion estimation Computer vision Automation image motion analysis Fluid flow measurement Motion measurement Rotation measurement Laboratories Educational institutions Geometrical optics

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Text segmentation using gabor filters for automatic document processing

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Machine Vision and Applications 1992年第3期5卷 169-184页

作者： Jain, Anil K. Bhattacharjee, Sushil Pattern Recognition and Image Processing Processing Laboratory Michigan State University E. Lansing 48824-1027 MI United States

There is a considerable interest in designing automatic systems that will scan a given paper document and store it on electronic media for easier storage, manipulation, and access. Most documents contain graphics and images in addition to text. Thus, the document image has to be segmented to identify the text regions, so that OCR techniques may be applied only to those regions. In this paper, we present a simple method for document image segmentation in which text regions in a given document image are automatically identified. The proposed segmentation method for document images is based on a multichannel filtering approach to texture segmentation. The text in the document is considered as a textured region. Nontext contents in the document, such as blank spaces, graphics, and pictures, are considered as regions with different textures. Thus, the problem of segmenting document images into text and nontext regions can be posed as a texture segmentation problem. Two-dimensional Gabor filters are used to extract texture features for each of these regions. These filters have been extensively used earlier for a variety of texture segmentation tasks. Here we apply the same filters to the document image segmentation problem. Our segmentation method does not assume any a priori knowledge about the content or font styles of the document, and is shown to work even for skewed images and handwritten text. Results of the proposed segmentation method are presented for several test images which demonstrate the robustness of this technique. © 1992 Springer-Verlag New York Inc.

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On texture in document images

On texture in document images

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1992 IEEE Computer Society Conference on Computer Vision and pattern recognition, CVPR 1992

作者： Jain, A.K. Bhattacharjee, S.K. Chen, Y. Pattern Recognition and Image Processing Laboratory Computer Science Department Michigan State University E. LansingMI48824-1027 United States

ISBN: (纸本)0818628553

A multichannel filtering-based texture segmentation method is applied to a variety of document image processing problems: text-graphics separation, address-block location, and bar code localization. In each of these segmentation problems, the text context or bar code in the image is considered to define a unique texture. Thus, all three document analysis problems can be posed as texture segmentation problems. Two-dimensional Gabor filters are used to compute texture features. Both supervised and unsupervised methods are used to identify regions of text or bar code in the document images. The performance of the segmentation and classification scheme for a variety of document images demonstrates the generality and effectiveness of the approach. © 1992 IEEE.

关键词： image texture

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Automatic image analysis of plant root structures

Automatic image analysis of plant root structures

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International Conference on pattern recognition

作者： Q. Huang A.K. Jain G.C. Stockman A.J.M. Smucker Department of Computer Science Pattern Recognition and Image Processing Laboratory Department of Crop and Soil Sciences Michigan State University East Lansing MI USA

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.< >

关键词： image analysis Soil image processing Plants (biology) image edge detection Data structures pattern recognition Laboratories Computer science Crops

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