检索结果-内蒙古大学图书馆

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

来源：评论

学校读者我要写书评

暂无评论

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

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

引用

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

来源：评论

学校读者我要写书评

暂无评论

On texture in document images

On texture in document images

引用

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

来源：评论

学校读者我要写书评

暂无评论

Texture analysis in the presence of speckle noise 12

Texture analysis in the presence of speckle noise

引用

12th Annual International Geoscience and Remote Sensing Symposium, IGARSS 1992

作者： Schistad, Anne H. Jain, Anil K. Norwegian Computing Center P.O. Box 114 Blindern Oslo 3N-0314 Norway Pattern Recognition and Image Processing Lab. Computer Science Department Michigan State University East LansingMI48824 United States

ISBN: (纸本)0780301382

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 are evaluated: fractal models, lognormal random fields and grey level co-occurrence matrices. The effect of filtering and noise transformation is investigated. The lognormal random field model gives the best performance. © IEEE 1992.

关键词： Land use

来源：评论

学校读者我要写书评

暂无评论

Automatic image analysis of plant root structures

Automatic image analysis of plant root structures

引用

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

来源：评论

学校读者我要写书评

暂无评论

A THREE-LAYER ADAPTIVE NETWORK FOR pattern DENSITY ESTIMATION AND CLASSIFICATION

引用

International Journal of Neural Systems 1991年第3期2卷 211-220页

作者： Jian-xiong Wu Chorkin Chan Department of Computer Science University of Hong Kong Hong Kong Institute of Image Processing and Pattern Recognition Shanghai Jiaotong University P.R.C

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.

关键词：

来源：评论

学校读者我要写书评

暂无评论

CAD-based computer vision: from CAD models to relational graphs

CAD-based computer vision: from CAD models to relational gra...

引用

IEEE International Conference on Systems, Man and Cybernetics

作者： P.J. Flynn A.K. Jain Pattern Recognition and Image Processing Laboratory Department of Computer Science Michigan State University East Lansing MI USA

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.< >

关键词： computer vision Solid modeling Object recognition Engines Space exploration Industrial relations Relational databases Spatial databases Interference Data mining

来源：评论

学校读者我要写书评

暂无评论

Preface [Special issue intro.]

引用

IEEE Transactions on pattern Analysis and Machine Intelligence 1979年第2期PAMI-1卷 125-126页

作者： Y. T. Chien Theodosios Pavlidis Guest 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 pattern recognition (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.

关键词： Artificial intelligence pattern recognition image analysis Speech analysis Conferences Labeling Machine intelligence Algorithm design and analysis image edge detection computer Society

来源：评论

学校读者我要写书评

暂无评论

Graph-Based Representations in pattern recognition 1

引用

丛书名： Lecture Notes in computer science

1000年

作者： Walter G. Kropatsch Nicole M. Artner Yll Haxhimusa Xiaoyi Jiang

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 pattern recognition, 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.

关键词： pattern recognition image processing and computer Vision computer Imaging, Vision, pattern recognition and Graphics computer Graphics Discrete Mathematics in computer science Data Structures

来源：评论

学校读者我要写书评

暂无评论

Theoretical Foundations of computer Vision 1

引用

丛书名： Computing Supplementa

1000年

作者： W. Kropatsch R. Klette F. Solina R. Albrecht

ISBN: (数字)9783709165867

ISBN: (纸本)9783211827307

computer Vision is a rapidly growing field of research investigating computational and algorithmic issues associated with image acquisition, processing, and understanding. It serves tasks like manipulation, recognition, mobility, and communication in diverse application areas such as manufacturing, robotics, medicine, security and virtual reality. This volume contains a selection of papers devoted to theoretical foundations of computer vision covering a broad range of fields, e.g. motion analysis, discrete geometry, computational aspects of vision processes, models, morphology, invariance, image compression, 3D reconstruction of shape. Several issues have been identified to be of essential interest to the community: non-linear operators; the transition between continuous to discrete representations; a new calculus of non-orthogonal partially dependent systems.

关键词： image processing and computer Vision pattern recognition Artificial Intelligence Combinatorics Differential Geometry Statistics, general

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：