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International Conference on pattern recognition

作者： M. Reiter R. Donner G. Langs H. Bischof Pattern Recognition and Image Processing Group Vienna University of Technology Austria Graz University of Technology Institute for Computer Graphics and Vision Austria

In this paper, we apply a multiple regression method based on canonical correlation analysis (CCA) to face data modelling. CCA is a factor analysis method which exploits the correlation between two high dimensional signals. We first use CCA to perform 3D face reconstruction and in a separate application we predict near-infrared (NIR) face texture. In both cases, the input data are color (RGB) face images. Experiments show, that due to the correlation between input and output signal, only a small number of canonical factors are needed to describe the functional relation of RGB images to the respective output (NIR images and 3D depth maps) with reasonable accuracy

关键词： Shape Face detection Lighting Image reconstruction Image analysis Image databases pattern analysis Rendering (computer graphics) Image color analysis pattern recognition

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Car Plate Detection Using Cascaded Tree-Style Learner Based on Hybrid Object Features

Car Plate Detection Using Cascaded Tree-Style Learner Based ...

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IEEE Conference on Advanced Video and Signal Based Surveillance (AVSS)

作者： Qiang Wu Huaifeng Zhang Wenjing Jia Xiangjian He Jie Yang Tom Hintz Computer Vision Research Group University of Technology Broadway NSW Australia Institute of Image Process. & Pattern Recognition Shanghai Jiaotong University China

Car plate detection is a key component in automatic license plate recognition system. This paper adopts an enhanced cascaded tree style learner framework for car plate detection using the hybrid object features including the simple statistical features and Harr-like features. The statistical features are useful for simplifying the process on cascade classifier. The cascaded tree-style detector design will further reduce the false alarm and the false dismissal while retaining a high detection ratio. The experimental results obtained by the proposed algorithm exhibit the encouraging performance.

关键词： Object detection Licenses Detectors computer vision pattern recognition Lighting Interference Australia Image recognition Robustness

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Region-based Image Annotation using Asymmetrical Support Vector Machine-based Multiple-Instance Learning

Region-based Image Annotation using Asymmetrical Support Vec...

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Conference on computer vision and pattern recognition (CVPR)

作者： Changbo Yang Ming Dong Jing Hua Machine Vision and Pattern Recognition Laboratory Department of Computer Science Wayne State University Detroit MI USA Department of Computer Science Wayne State University Detroit MI USA

In region-based image annotation, keywords are usually associated with images instead of individual regions in the training data set. This poses a major challenge for any learning strategy. In this paper, we formulate image annotation as a supervised learning problem under Multiple-Instance Learning (MIL) framework. We present a novel Asymmetrical Support Vector Machine-based MIL algorithm (ASVM-MIL), which extends the conventional Support Vector Machine (SVM) to the MIL setting by introducing asymmetrical loss functions for false positives and false negatives. The proposed ASVM-MIL algorithm is evaluated on both image annotation data sets and the benchmark MUSK data sets.

关键词： Machine learning Support vector machines Image retrieval pattern recognition computer science Training data Supervised learning computer vision Feature extraction Image segmentation

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Active Feature Models

Active Feature Models

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International Conference on pattern recognition

作者： G. Langs P. Peloschek R. Donner M. Reiter H. Bischof Institute for Computer Graphics and Vision Graz University of Technology Austria Pattern Recognition and Image Processing Group University of Technology Vienna Austria Department of Radiology Medical University of Vienna Austria

ISBN: (纸本)0769525210

In this paper active feature models are proposed. They utilize local texture features and a statistical shape model for the reliable localization of landmarks in images. They are related to active appearance models, but instead of modelling the entire texture of an object they represent image texture by means of local descriptors. The approach has advantages with complex image data like anatomical structures that exhibit high texture variation with limited relevance for the recognition of the object location. Experimental results and the comparison to AAMs on different data sets indicate that active feature models can improve search speed and result accuracy, considerably

关键词： Shape Active appearance model Biomedical imaging Image texture Image edge detection Image texture analysis computer graphics pattern recognition Image processing Radiology

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Finding a Semantic Structure Interactively in Image Databases

Finding a Semantic Structure Interactively in Image Database...

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IEEE International Conference on Multimedia and Expo (ICME)

作者： Manjeet Rege Ming Dong Farshad Fotouhi Machine Vision & Pattern Recognition Laboratory Department of Computer Science Wayne State University Detroit MI USA Database & Multimedia Systems Group Department of Computer Science Wayne State University Detroit MI USA

We present a new approach to organize an image database by finding a semantic structure interactively based on multi-user relevance feedback. By treating user relevance feedbacks as weak classifiers and combining them together, we are able to capture the categories in the users' mind and build a semantic structure in the image database. Experiments performed on an image database consisting of general purpose images demonstrate that our system outperforms some of the other conventional methods

关键词： Image databases Feedback Image retrieval Machine learning algorithms Support vector machines Support vector machine classification Information retrieval Machine vision pattern recognition Multimedia databases

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Co-Clustering Image Features and Semantic Concepts

Co-Clustering Image Features and Semantic Concepts

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IEEE International Conference on Image Processing

作者： Manjeet Rege Ming Dong Farshad Fotouhi Department of Computer Science Machine Vision & Pattern Recognition Laboratory Wayne State University Detroit MI USA Database & Multimedia Systems Group Wayne State University Detroit MI USA

In this paper, we present a novel idea of co-clustering image features and semantic concepts. We accomplish this by modelling user feedback logs and low-level features using a bipartite graph. Our experiments demonstrate that (1) incorporating semantic information achieves better image clustering and (2) feature selection in co-clustering narrows the semantic gap, thus enabling efficient image retrieval.

关键词： Feedback Image retrieval Bipartite graph Image databases Clustering algorithms Machine vision pattern recognition Multimedia databases Spatial databases Multimedia systems

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Automatic Surveying of Cutaneous Hemangiomas

Automatic Surveying of Cutaneous Hemangiomas

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International Conference on pattern recognition

作者： S. Zambanini G. Langs R. Sablatnig P. Donath H. Maier Pattern Recognition and Image Processing Group University of Technology Vienna Austria Institute for Computer Graphics and Vision Graz University of Technology Austria Division of Special and Environmental Dermatologyy Medical University of Vienna Austria

This paper presents a method for the fully automatic surveying of cutaneous hemangiomas by means of a hemangioma segmentation and a ruler visible in the images. The algorithm computes the spatial resolution of an image. Hemangioma segmentation is accomplished by a single-layer perceptron classification by means of pixel color features. The algorithm was evaluated on a set of 120 images. It achieves satisfactory results on images with clearly visible, saturated hemangiomas

关键词： Image segmentation Neoplasms pattern recognition Spatial resolution Lesions Area measurement Pixel Image processing computer graphics Biomedical imaging

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Co-clustering Documents and Words Using Bipartite Isoperimetric Graph Partitioning

Co-clustering Documents and Words Using Bipartite Isoperimet...

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IEEE International Conference on Data Mining (ICDM)

作者： Manjeet Rege Ming Dong Farshad Fotouhi Machine Vision and Pattern Recognition Laboratory Database and Multimedia Systems GroupDepartment of Computer Science Wayne State University Detroit MI USA Database and Multimedia Systems Group Database and Multimedia Systems GroupDepartment of Computer Science Wayne State University Detroit MI USA

In this paper, we present a novel graph theoretic approach to the problem of document-word co-clustering. In our approach, documents and words are modeled as the two vertices of a bipartite graph. We then propose isoperimetric co-clustering algorithm (ICA) - a new method for partitioning the document-word bipartite graph. ICA requires a simple solution to a sparse system of linear equations instead of the eigenvalue or SVD problem in the popular spectral co-clustering approach. Our extensive experiments performed on publicly available datasets demonstrate the advantages of ICA over spectral approach in terms of the quality, efficiency and stability in partitioning the document-word bipartite graph.

关键词： Bipartite graph Partitioning algorithms Independent component analysis Equations Eigenvalues and eigenfunctions Stability Data mining Random variables Mutual information Machine vision

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Processing of struck-out handwriting

Processing of struck-out handwriting

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2nd Indian International Conference on Artificial Intelligence, IICAI 2005

作者： Bhattacharya, U. Chaudhuri, B.B. Computer Vision and Pattern Recognition Unit Indian Statistical Institute Kolkata India

ISBN: (纸本)0972741216

Struck-out words are often found in handwritten manuscripts. A realistic off-line handwriting recognition system should take care of this common aspect. A simple but efficient approach to this problem is to subject each handwritten word to a two-class classifier in the initial stage. This classifier should take decision to pass the word to a word recognition engine in the next step or discard it as a struck-out/wrong word. In the present work, we studied a stroke analysis based technique, which can classify an input word as a correct or wrong (struck-out) word. We simulated the proposed approach on a database of 466 words collected from handwritten manuscripts of different languages and the results are promising. Copyright © IICAI 2005.

关键词： Artificial intelligence

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A corpus for OCR research on mathematical expressions

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International Journal on Document Analysis and recognition 2005年第4期7卷 241-259页

作者： Garain, Utpal Chaudhuri, B.B. Computer Vision and Pattern Recognition Unit Indian Statistical Institute Calcutta-700 035 203 B.T. Road India

This paper is concerned with research on OCR (optical character recognition) of printed mathematical expressions. Construction of a representative corpus of technical and scientific documents containing expressions is discussed. A statistical investigation of the corpus is presented, and usefulness of this analysis is demonstrated in the related research problems, namely, (i) identification and segmentation of expression zones from the rest of the document, (ii) recognition of expression symbols, (iii) interpretation of expression structures, and (iv) performance evaluation of a mathematical expression recognition system. Moreover, a groundtruthing format has been proposed to facilitate automatic evaluation of expression recognition techniques. © Springer-Verlag Berlin/Heidelberg 2005.

关键词： Database Groundtruthing Mathematical expressions OCR Performance evaluation Statistical learning

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