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17th European Signal processing Conference, EUSIPCO 2009

作者： Lettner, Martin Sablatnig, Robert Pattern Recognition and Image Processing Group Institute of Computer Aided Automation Vienna University of Technology 1040 Vienna Austria

In this paper we propose a method for segmenting characters in multispectral images of ancient documents. Due to the low quality of the document images the main idea of our study is to combine the multispectral behavior and contextual spatial information. Therefore we utilize a Markov Random Field model using the spectral information of the images and stroke properties to include spatial dependencies of the characters. The whole process is parameter free since we calculate the stroke properties and the Gaussian parameters for the imaging model automatically. The study shows the effectiveness of using multispectral data for a computer aided analysis of ancient text documents. We compare the results of the proposed segmentation method to traditional methods based on color images as well as gray level images. © EURASIP, 2009.

关键词： Computer aided analysis

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Salient object detection via color contrast and color distribution

Salient object detection via color contrast and color distri...

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11th Asian Conference on Computer Vision, ACCV 2012

作者： Fu, Keren Gong, Chen Yang, Jie Zhou, Yue Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University Key Laboratory of System Control and Information Processing Shanghai 200240 China

ISBN: (纸本)9783642373305

In this paper, we take the advantages of color contrast and color distribution to get high quality saliency maps. The overall procedure flow of our unified framework contains superpixel pre-segmentation, color contrast and color distribution computation, combination, final refinement and then object segmentation. During color contrast saliency computation, we combine two color systems and then introduce the using of distribution prior before saliency smoothing. It works to select correct color components. In addition, we propose a novel saliency smoothing procedure that is based on superpixel regions and is realized in color space. This processing step leads to total object being highlighted evenly, contributing to high quality color contrast saliency maps. Finally, a new refinement approach is utilized to eliminate artifacts and recover unconnected parts in the combined saliency maps. In visual comparison, our method produces higher quality saliency maps which stress out the total object meanwhile suppress background clutters. Both qualitative and quantitative experiments show our approach outperforms 8 state-of-the-art methods, achieving the highest precision rate 96% (3% improvement from the current highest), when evaluated via one of the most popular data sets [1]. Excellent content-aware image resizing also can be achieved with our saliency maps. © 2013 Springer-Verlag.

关键词： Superpixels

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Anatomy recognition in CT images of head and neck region via precision atlases

Anatomy recognition in CT images of head and neck region via...

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Medical Imaging 2021: image processing

作者： Li, Jieyu Udupa, Jayaram K. Tong, Yubing Odhner, Dewey Torigian, Drew A. Institute of Image Processing and Pattern Recognition Department of Automation Shanghai Jiao Tong University China Medical Image Processing Group Department of Radiology University of Pennsylvania United States

ISBN: (纸本)9781510640214

Multi-atlas segmentation methods will benefit from atlases covering the complete spectrum of population patterns, while the difficulties in generating such large enough datasets and the computation burden required in the segmentation procedure reduce its practicality in clinical application. In this work, we start from a viewpoint that different parts of the target object can be recognized by different atlases and propose a precision atlas selection strategy. By comparing regional similarity between target image and atlases, precision atlases are ranked and selected by the frequency of regional best match, which have no need to be globally similar to the target subject at either image-level or object-level, largely increasing the implicit patterns contained in the atlas set. In the proposed anatomy recognition method, atlas building is first achieved by all-totemplate registration, where the minimum spanning tree (MST) strategy is used to select a registration template from a subset of radiologically near-normal images. Then, a two-stage recognition process is conducted: in rough recognition, sub-image level similarity is calculated between the test image and each image of the whole atlas set, and only the atlas with the highest similarity contributes to the recognition map regionally;in refined recognition, the atlases with the highest frequencies of best match are selected as the precision atlases and are utilized to further increase the accuracy of boundary matching. The proposed method is demonstrated on 298 computed tomography (CT) images and 9 organs in the Head and Neck (HN) body region. Experimental results illustrate that our method is effective for organs with different segmentation challenge and samples with different image quality, where remarkable improvement in boundary interpretation is made by refined recognition and most objects achieve a localization error within 2 voxels. © 2021 SPIE.

关键词： image enhancement

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Characteristic line of planar homography matrix and its applications in camera calibration

Characteristic line of planar homography matrix and its appl...

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18th International Conference on pattern recognition, ICPR 2006

作者： Jianhua, Wang Yuncai, Liu Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University 800 Dong Chuan Road Shanghai 200240 China

ISBN: (纸本)0769525210

In this paper, we employ the concept of characteristic line to show some useful properties of planar homography matrix. These properties relate the characteristic line of a planar homography matrix with Euler angles of the planar pattern. Based on the characteristic line, a new method of linear camera calibration is proposed and a strategy to select poses of planar pattern during taking calibration images is suggested. This strategy can help ensure accuracy of calibration. Experiment results including both simulated data and real images validate the method and strategy. © 2006 IEEE.

关键词： Computer graphics

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Registration of multi-spectral manuscript images

Registration of multi-spectral manuscript images

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8th International Symposium on Virtual Reality, Archaeology, and Intelligent Cultural Heritage, VAST 2007

作者： Diem, M. Lettner, M. Sablatnig, R. Pattern Recognition and Image Processing Group Institute for Computer Aided Automation Vienna University of Technology Austria

ISBN: (纸本)9783905674019

Two medieval Slavonic manuscripts are recorded, investigated and analyzed by philologists in collaboration with computer scientists. The aim of the project is to develop algorithms that support the philologists by automatically deriving the description and restoration of the scripts. The parchment partially contains two scripts, where the first script was erased. In order to enhance the erased script, the manuscript pages are imaged in seven bands between 330 and 1000 nm. A registration, aligning the resultant images, is necessary so that further image processing algorithms can combine the information gained by the different spectral bands. Therefore, the images are coarsely aligned using rotationally invariant features and an affine transformation. Afterwards, the similarity of the different images is computed by means of the normalized cross correlation. Finally, the images are accurately mapped to each other by the local weighted mean transformation. The algorithms used for the registration and preliminary results are presented in this paper. © The Eurographics Association 2007.

关键词： image enhancement

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Weak metric learning for feature fusion towards perception-inspired object recognition

Weak metric learning for feature fusion towards perception-i...

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16th International Multimedia Modeling Conference on Advances in Multimedia Modeling, MMM 2010

作者： Li, Xiong Zhao, Xu Fu, Yun Liu, Yuncai Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University Shanghai 200240 China NY 14260 United States

ISBN: (纸本)3642113001

With extracted local features of a given image, computing its global feature under perceptual framework has shown promising performance in object recognition. However, under some tough applications with large intra-class variance, using only one kind of local feature is inadequate to build a robust classification system. To integrate the discriminability of complementary local features, in this paper, we extend the efficacy of perceptual framework to adapt to heterogeneous features. Given multiple raw global features, we propose a fusion strategy through metric learning, which is called weak metric learning in this work, for fusing high dimensional features. The fusion model is solved with the maximal kernel canonical correlation formulation with the multiple global features as outputs. Experimental results show that our method achieves significant improvements about 5% to 11% than the benchmark perceptual framework system, HMAX, on several difficult categories of object recognition with much less training samples and feature elements. © 2010 Springer-Verlag Berlin Heidelberg.

关键词： Object recognition

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Improvement of histogram-based image retrieval and classification

Improvement of histogram-based image retrieval and classific...

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作者： Siggelkow, Sven Burkhardt, Hans Albert-Ludwigs-Universität Freiburg Institute for Pattern Recognition and Image Processing 79085 Freiburg Germany

Histogram-based techniques are commonly used in image retrieval ranging from basic color histograms to sophisticated histograms of various local feature vectors. Our approach considers multidimensional histograms of locally invariant features. In order to get rid of the discontinuous behavior of traditional histograms we develop a fuzzy histogram which does no sharp assignment of a feature vector to one bin only but performs a weighted assignment to all neighboring bins. This improvement is not restricted to the kind of feature histograms considered here but could generally improve histogram-based retrieval or classification techniques. © 2002 IEEE.

关键词： pattern recognition

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Web Page Classification Algorithm Based on Semi-Supervised Support Vector Machine 2

Web Page Classification Algorithm Based on Semi-Supervised S...

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2nd IEEE Advanced Information Management, Communicates, Electronic and Automation Control Conference, IMCEC 2018

作者： Huang, Wenqing You, Hui Zhejiang Sci-Tech University Institute of Computer Vision Image Processing and Pattern Recognition Acceptable School of Information Hangzhou China

ISBN: (纸本)9781538618035

Most web page classification algorithms are learning algorithms under the single-instance single-label framework. Multi-Instance Multi-Label learning is a new machine learning framework. MIMLSVM+ algorithm, using degenerate strategy to transform multiple instances into single instances, and then decomposing multiple labels into a series of two types of classification problems, that is, building an SVM for each label. Because the MIMLSVM+ algorithm degrades the multi-label problem into a series of two types of classification problems, the processing of each label will lose the contact information between the labels independently. Therefore, multi-tasking technology is introduced to make use of the multi-tasking learning framework based on nuclear to validate the MIMLSVM+ expand, get E-MIMLSVM+ algorithm. The paper improves E-MIMLSVM+ algorithm by using the semi-supervised learning method. Experimental results show that the proposed method can achieve higher classification accuracy. © 2018 IEEE.

关键词： Learning algorithms

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Refining content based image retrieval via semi-supervised learning

Refining content based image retrieval via semi-supervised l...

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17th European Signal processing Conference, EUSIPCO 2009

作者： Zhu, Songhao Liu, Yuncai Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University 800 Dong Chuan Road 200240 Shanghai China

Content-based image retrieval plays a key role in the management of a large image database. However, the results of existing approaches are not as satisfactory for the gap between visual features and semantic concepts. Therefore, a novel scheme is here proposed. First, to tackle the problem of large computational cost involved in a large image database, a pre-filtering processing is utilized to filter out the most irrelevant images while keeping the most relevant ones. Second, the relevance between the query image and the remaining images is measured and the obtained relevance scores are stored for a later refinement processing. Finally, a semi-supervised learning algorithm is utilized to refine candidate ranking by taking into account both the pairwise information of unlabeled images and the relevance scores between the input query image and unlabeled images. Experiments conducted on a typical Corel dataset demonstrate the effectiveness of the proposed scheme. © EURASIP, 2009.

关键词： Learning algorithms

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Hand Gesture recognition Based on Improved FRNN

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Journal of Donghua University(English Edition) 2005年第5期22卷 47-52页

作者：滕晓龙王向阳刘重庆 Institute of Image processing and Pattern recognition Shanghai Jiaotong University

The trained Gaussian mixture model is used to make skincolour segmentation for the input image sequences. The hand gesture region is extracted, and the relative normalization images are obtained by interpolation operation. To solve the proem of hand gesture recognition, Fuzzy-Rough based nearest neighbour（RNN） algorithm is applied for classification. For avoiding the costly compute, an improved nearest neighbour classification algorithm based on fuzzy-rough set theory （FRNNC） is proposed. The algorithm employs the represented cluster points instead of the whole training samples, and takes the hand gesture data＇s fuzziness and the roughness into account, so the campute spending is decreased and the recognition rate is increased. The 30 gestures in Chinese sign language alphabet are used for approving the effectiveness of the proposed algorithm. The recognition rate is 94.96%, which is better than that of KNN （K nearest neighbor）and Fuzzy- KNN （Fuzzy K nearest neighbor）.

关键词： Fuzzy-Rough set edit nearest neighbour algorithm hand gesture recognition

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