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International Symposium on Speech, image processing and Neural Networks

作者： Qian Huang Pattern Recognition and Image Processing Laboratory Department of Computer Science Michigan State University East Lansing MI USA

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.< >

关键词： Machine vision Problem-solving computer vision Organizing pattern recognition image processing Laboratories computer science Gratings Humans

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Generalized stochastic tube model: Tracking 3D blood vessels in MR images 12

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

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12th IAPR International Conference on pattern recognition - Conference B: pattern recognition and Neural Networks, ICPR 1994

作者： Huang, Qian Stockman, George 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

ISBN: (纸本)0818662700

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 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. © 1994 Institute of Electrical and Electronics Engineers Inc.. All rights reserved.

关键词： Blood vessels

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Model-based automatic recognition of blood vessels from MR images and its 3D visualization

Model-based automatic recognition of blood vessels from MR i...

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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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A New Deep Fuzzy Based MSER Model for Multiple Document images Classification 3rd

A New Deep Fuzzy Based MSER Model for Multiple Document Imag...

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3rd International Conference on pattern recognition and Artificial Intelligence, ICPRAI 2022

作者： Biswas, Kunal Shivakumara, Palaiahnakote Sivanthi, Sittravell Pal, Umapada Lu, Yue Liu, Cheng-Lin Ayub, Mohamad Nizam Bin Computer Vision and Pattern Recognition Unit Indian Statistical Institute Kolkata India Faculty of Computer Science and Information Technology University of Malaya Kuala Lumpur Malaysia Shanghai Key Laboratory of Multidimensional Information Processing East China Normal University Shanghai China National Laboratory of Pattern Recognition Institute of Automation of Chinese Academy of Sciences Beijing China School of Artificial Intelligence University of Chinese Academy Beijing China

ISBN: (纸本)9783031090363

Understanding document images uploaded on social media is challenging because of multiple types like handwritten, printed and scene text images. This study presents a new model called Deep Fuzzy based MSER for classification of multiple document images (like handwritten, printed and scene text). The proposed model detects candidate components that represent dominant information irrespective of the type of document images by combining fuzzy and MSER in a novel way. For every candidate component, the proposed model extracts distance-based features which result in proximity matrix (feature matrix). Further, the deep learning model is proposed for classification by feeding input images and feature matrix as input. To evaluate the proposed model, we create our own dataset and to show effectiveness, the proposed model is tested on standard datasets. The results show that the proposed approach outperforms the existing methods in terms of average classification rate. © 2022, Springer Nature Switzerland AG.

关键词： image classification

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Empirically validating software metrics for risk prediction based on intelligent methods

Journal of Digital Information Management

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Journal of Digital Information Management 2007年第3期5卷 99-106页

作者： Xu, Zhihong Zheng, Xin Guo, Ping Image Processing and Pattern Recognition Laboratory Beijing Normal University Beijing 100875 China School of Computer Science Beijing Institute of Technology Beijing 100081 China

The software systems which are related to national science and technology projects are very crucial. This kind of systems always involves high technical factors and has to spend a large amount of money, so the quality and reliability of the software deserve to be further studied. Hence, we propose to apply four intelligent classification techniques most used in data mining fields, including Bayesian belief networks (BBN), nearest neighbor (NN), rough set (RS) and decision tree (DT), to validate the usefulness of software metrics for risk prediction. Results show that comparing with metrics such as Lines of code (LOC) and Cyclomatic complexity (V(G)) which are traditionally used for risk prediction, Halstead program difficulty (D), Number of executable statements (EXEC) and Halstead program volume (V) are the more effective metrics as risk predictors. By analyzing obtained results we also found that BBN was more effective than the other three methods in risk prediction.

关键词： Bayesian networks

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Multi-source remote sensing classification based on Mallat fusion and residual error feature selection

Journal of Digital Information Management

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Journal of Digital Information Management 2007年第3期5卷 130-137页

作者： Cao, Dongdong Yin, Qian Guo, Ping Image Processing and Pattern Recognition Laboratory Beijing Normal University Beijing 100875 China School of Computer Science Beijing Institute of Technology Beijing 100081 China

Classification of multi-source remote sensing images has been studied for decades, and many methods have been proposed or improved. Most of these studies focus on how to improve the classifiers in order to obtain higher classification accuracy. However, as we know, even if the most promising method such as neural network, its performance not only depends on the classifier itself, but also has relation with the training pattern (i.e. features). On consideration of this aspect, we propose an approach to feature selection and classification of multi-source remote sensing images based on Mallat fusion and residual error in this paper. Firstly, the fusion of multi-source images can provide a fused image which is more preferable for classification. And then a featureselection scheme approach based on fused image is proposed, which is to select effective subsets of features as inputs of a classifier by taking into account the residual error associated with each land-cover class. In addition, a classification technique base on selected features by using a feed-forward neural network is investigated. The results of computer experiments carried out on a multisource data set confirm the validity of the proposed approach.

关键词： Classification (of information)

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Fuzzy c-means in an MDL-framework

Fuzzy c-means in an MDL-framework

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International Conference on pattern recognition

作者： A. Selb H. Bischof A. Leonardis Pattern Recognition and Image Processing Group University of Technology Vienna Vienna Austria Faculty of Computer and Information Science University of Ljubljana Ljubljana Slovenia

ISBN: (纸本)0769507506

In this paper we present a minimum description length (MDL) framework for fuzzy clustering algorithms. This framework enables us to find an optimal number of cluster centers. We applied our approach to the fuzzy c-means algorithm for which we designed a computationally efficient procedure. We report the results of our approach on a 2D clustering problem and on RGB color image segmentation.

关键词： Clustering algorithms Radial basis function networks Encoding Color image segmentation image recognition image processing Algorithm design and analysis Unsupervised learning pattern recognition

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Multiple eigenspaces by MDL

Multiple eigenspaces by MDL

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International Conference on pattern recognition

作者： A. Leonardis H. Bischof Faculty of Computer and Information Science University of Ljubljana Ljubljana Slovenia Pattern Recognition and Image Processing Group University of Technology Vienna Vienna Austria

ISBN: (纸本)0769507506

We propose an approach to constructing multiple eigenspaces from a set of training images based on the minimum description length (MDL) principle. The main idea is to systematically build a redundant set of eigenspaces, which are treated as hypotheses that are then subject to a selection procedure. The selection procedure, based on the MDL principle, selects the final resulting set of eigenspaces as an optimal representation of the training set. We have tested the proposed method on a number of standard image sets, and the significance of the approach with respect to the recognition rate has been clearly demonstrated.

关键词： Testing image recognition Principal component analysis Information science image analysis image reconstruction pattern recognition image processing Robustness Robust control

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Illumination insensitive eigenspaces

Illumination insensitive eigenspaces

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International Conference on computer Vision (ICCV)

作者： H. Bischof H. Wildenauer A. Leonardis Pattern Recognition and Image Processing Group University of Technology Vienna Vienna Austria Faculty of Computer and Information Science University of Ljubljana Ljubljana Slovenia

ISBN: (纸本)0769511430

Variations in illumination can have a dramatic effect on the appearance of an object in an image. In this paper we propose how to deal with illumination variations in eigenspace methods. We demonstrate that the eigenimages obtained by a training set under a single illumination condition (ambient light) can be used for recognition of objects taken under different illumination conditions. The major idea is to incorporate a set of gradient based filter banks into the eigenspace recognition framework. This can be achieved since the eigenimage coefficients are invariant for linearly filtered images (input and eigenimages). To achieve further illumination insensitivity we devised a robust procedure for coefficient recovery. The proposed approach has been extensively evaluated on a set of 2160 images and the results were compared to other approaches.

关键词： Lighting Nonlinear filters Principal component analysis image coding Object recognition Filtering Filter bank image recognition Information science image sampling

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Software quality prediction using Affinity Propagation algorithm

Software quality prediction using Affinity Propagation algor...

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International Joint Conference on Neural Networks (IJCNN)

作者： Bingbing Yang Qian Yin Shengyong Xu Ping Guo Image Processing and Pattern Recognition Laboratory Beijing Normal University Beijing China Laboratory of Computer Science Institute of Software Chinese Academy and Sciences Beijing China

Software metrics are collected at various phases of the software development process. These metrics contain the information of the software and can be used to predict software quality in the early stage of software life cycle. Intelligent computing techniques such as data mining can be applied in the study of software quality by analyzing software metrics. Clustering analysis, which can be considered as one of the data mining techniques, is adopted to build the software quality prediction models in the early period of software testing. In this paper, a new clustering method called Affinity Propagation is investigated for the analysis of two software metric datasets extracted from real-world software projects. Meanwhile, K-Means clustering method is also applied for comparison. The numerical experiment results show that the Affinity Propagation algorithm can be applied well in software quality prediction in the very early stage, and it is more effective on reducing Type II error.

关键词： Software Software quality Clustering methods Clustering algorithms Software metrics Testing Software algorithms

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