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检索条件"机构=Computer Vision and Pattern"

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Building test collection from Old IR literature 14

Building test collection from Old IR literature

引用

6th International Workshop of the Forum for Information Retrieval Evaluation, FIRE 2014

作者： Chakraborty, Anirban Ghosh, Kripabandhu Parui, Swapan Kumar Computer Vision and Pattern Recognition Unit Indian Statistical Institute 203 B.T. Road Kolkata700108 India

ISBN: (纸本)9781450337557

Standard test collections form the very basis of Information Retrieval research and evaluation. Important datasets have been created to promote empirical research and experimentation. In this paper, we describe our endeavour in creating a test collection from old, archived writings of IR stalwarts. The documents are created in text format from the scanned and OCRed version. The test collection consists of a set of documents in TREC format along with a set of expert queries and their relevance assessments. This dataset, though small in size, would be of paramount interest for researchers and students of IR since it contains valuable discourses on the discipline from its very inception. Also, to the best of our knowledge, no standard IR dataset has been built so far comprising old research articles. Furthermore, this is a dataset without the original error-free digital text version. So, the resulting collection would expect researchers to run retrieval experiments on the erroneous collection without the scope of error modeling. This would invite new research ideas. © 2015 ACM.

关键词： Errors

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A new method for handwritten scene text detection in video

A new method for handwritten scene text detection in video

引用

International Conference on Frontiers in Handwriting Recognition

作者： Shivakumara, Palaiahnakote Dutta, Anjan Pal, Umapada Tan, Chew Lim School of Computing National University of Singapore Singapore Singapore Computer Vision Center Universitat Autònoma de Barcelona Barcelona Spain Computer Vision and Pattern Recognition Unit Indian Statistical Institute India

ISBN: (纸本)9780769542218

There are many video images where hand written text may appear. Therefore handwritten scene text detection in video is essential and useful for many applications for efficient indexing, retrieval etc. Also there are many video frames where text line may be multi-oriented in nature. To the best of our knowledge there is no work on handwritten text detection in video, which is multi-oriented in nature. In this paper, we present a new method based on maximum color difference and boundary growing method for detection of multi-oriented handwritten scene text in video. The method computes maximum color difference for the average of R, G and B channels of the original frame to enhance the text information. The output of maximum color difference is fed to a K-means algorithm with K=2 to separate text and non-text clusters. Text candidates are obtained by intersecting the text cluster with the Sobel output of the original frame. To tackle the fundamental problem of different orientations and skews of handwritten text, boundary growing method based on a nearest neighbor concept is employed. We evaluate the proposed method by testing on our own handwritten text database and publicly available video data (Hua's data). Experimental results obtained from the proposed method are promising. © 2010 IEEE.

关键词： Color

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Online Evaluation of Textile Yarn Evenness Using Fast Exposure Imaging and Image Processing Techniques 7

Online Evaluation of Textile Yarn Evenness Using Fast Exposu...

引用

7th International Conference on Automation, Control and Robots, ICACR 2023

作者： Haleem, Noman Bustreo, Matteo Del Bue, Alessio University of Groningen Campus Fryslan Leeuwarden Netherlands Istituto Italiano di Tecnologia Pattern Analysis & Computer Vision Genova Italy

ISBN: (纸本)9798350302882

Evenness is regarded as the most important attribute of textile yarn quality as irregular yarns reduce efficiency of spinning process and deteriorate quality of resultant fabrics. In traditional yarn spinning process, evenness is tested offline using laboratory scale testers through sampling from a whole lot of yarns. The lag associated with this approach limits agility in responding to a variety of process variations whereas limited sampling restricts achieving fine grained yarn quality insights. These factors together result in limited control on yarn spinning process and eventually compromise quality of the yarn *** aim to address this problem by developing a new yarn evenness testing system, which can be deployed on an individual spinning position to evaluate yarn quality in an online manner. The proposed system, which is based on a combination of fast exposure imaging and image processing techniques, was first validated using a control set of yarn specimens and later tested in normal yarn production through comparison with an existing offline yarn evenness tester. The results demonstrate promising potential of the proposed system for wider application in yarn spinning industry. © 2023 IEEE.

关键词： Textile industry

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Extraction of type style based meta-information from imaged documents 5

Extraction of type style based meta-information from imaged ...

引用

5th International Conference on Document Analysis and Recognition, ICDAR 1999

作者： Garain, U. Chaudhuri, B.B. Computer Vision and Pattern Recognition Unit Indian Statistical Institute 203 Barrackpore Trunk Road Calcutta700 035 India

ISBN: (纸本)0769503187

Extraction of some meta-information from printed documents without an OCR approach is considered. It can be statistically verified that important terms in articles are printed in italic, bold and all capital style. Detection of these type styles helps in automatic extraction of the lines containing titles, authors' names, subtitles, references as well as sentences having important terms occurring in the text. It also helps in improving the OCR performance for reading the italic text. Some experimental results on the performance of the approach on good quality as well as degraded document images are presented. © 1999 IEEE.

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Low Resource Degraded Quality Document Image Binarization – Domain Adaptation is the Way 22

Low Resource Degraded Quality Document Image Binarization –...

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Proceedings of the Thirteenth Indian Conference on computer vision, Graphics and Image Processing

作者： Ahana Kundu Ujjwal Bhattacharya Computer Vision and Pattern Recognition Indian Statistical Institute IN Computer Vision and Pattern Recognition ISI Kolkata IN

ISBN: (纸本)9781450398220

Usually, image binarization plays a crucial role in automatic analysis of degraded documents from their captured images. However, this binarization task is often difficult due to a number of reasons including the high similarity between noisy background and faded foreground pixels. The study presented here is particularly focused on binarization of images of low-resource degraded quality documents based on a set of recently collected image samples of several rare, ancient and severely degraded quality printed documents of Bangla, the 2nd and 5th most popular script of India and the world respectively. This new collection of degraded document image samples will henceforth be referred as ’ISIDDI2’ and it consists of 139 images of Bangla old document pages. Samples of ’ISIDDI’, another existing database of degraded Bangla document image samples, have also been used in the present study. A novel deep architecture based on attention UNET++ with dilated convolution operation is proposed for this binarization task. The model is optimized using human vision perceptible distance reciprocal distortion (DRD) loss. Since the binarization ground truth of samples of both ’ISIDDI2’ and ’ISIDDI’ are not available, the proposed network has been trained using samples of DIBCO and H-DIBCO datasets and an unsupervised domain adaptation (DA) module is employed for adaptation of the proposed architecture to the degradation patterns of ’ISIDDI2’ or ’ISIDDI’ samples. The proposed binarization strategy includes certain post-processing operation based on a modified k-neighbourhood based approach for recovery of broken characters. Results of our extensive experimentation show that the proposed binarization strategy has improved the binarization output of state-of-the-art methods on both ISIDDI2 and ISIDDI datasets. Also, its performance on well-known DIBCO samples is satisfactory.

关键词： UNET++ degraded document image domain adaptation Image binarization attention

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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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TREC 2020 NEWS Track Background Linking Task 29

TREC 2020 NEWS Track Background Linking Task

引用

29th Text REtrieval Conference, TREC 2020

作者： Gautam, Rahul Mitra, Mandar Roy, Dwaipayan Computer Vision and Pattern Recognition Unit Indian Statistical Institute 203 B.T.Road Kolkata700108 India

The Background Linking task is a problem that focuses on providing users with suggestions for articles to read next, when the user is reading a news article. The suggested articles should provide adequate context and background information for the article that the user is currently reading. In this paper, we describe several methods that we explored for this task, and report their results. © 2020 29th Text REtrieval Conference, TREC 2020 - Proceedings. All Rights Reserved.

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A new dataset of Persian handwritten documents and its segmentation

A new dataset of Persian handwritten documents and its segme...

引用

2011 7th Iranian Conference on Machine vision and Image Processing, MVIP 2011

作者： Alaei, Alireza Nagabhushan, P. Pal, Umapada Department of Studies in Computer Science University of Mysore Mysore 570006 India Computer Vision and Pattern Recognition Unit Indian Statistical Institute Kolkata-108 India

ISBN: (纸本)9781457715358

In document image analysis and especially in handwritten document image recognition, standard datasets play vital roles for evaluating performances of algorithms and comparing results obtained by different groups of researchers. In this paper, an unconstrained Persian handwritten text dataset (PHTD) is introduced. The PHTD contains 140 handwritten documents of three different categories written by 40 individuals. Total number of text-lines and words/subwords in the dataset are 1787 and 27073, respectively. In most of the PHTD documents either an overlapping or a touching text-lines is present. The average number of text-lines in documents of the PHTD is 13. Two types of ground truths based on pixels information and content information are generated for the dataset. Providing these two types of ground truths for the PHTD, it can be utilized in many areas of document image processing such as sentence recognition/understanding, text-line segmentation, word segmentation, word recognition, and character segmentation. To provide a framework for other researches, recent text-line segmentation results on this dataset are also reported. © 2011 IEEE.

关键词： Image segmentation

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A Simple Context Dependent Suggestion System 22

A Simple Context Dependent Suggestion System

引用

22nd Text REtrieval Conference, TREC 2013

作者： Roy, Dwaipayan Bandyopadhyay, Ayan Mitra, Mandar Computer Vision and Pattern Recognition Unit Indian Statistical Institute 203 B.T.Road Kolkata700108 India

The Contextual Suggestion Problem focuses on search techniques for complex information needs that are highly dependent on context and user interest. In this paper, we present our approach to providing user and context dependent suggestions. The official evaluation scores for our submitted run are reported and compared to the overall median scores (across all 34 runs). © 2013 22nd Text REtrieval Conference, TREC 2013 - Proceedings. All Rights Reserved.

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Efficient Audio-Visual Speaker Recognition via Deep Heterogeneous Feature Fusion 12th

Efficient Audio-Visual Speaker Recognition via Deep Heteroge...

引用

12th Chinese Conference on Biometric Recognition, CCBR 2017

作者： Liu, Yu-Hang Liu, Xin Fan, Wentao Zhong, Bineng Du, Ji-Xiang Department of Computer Science Huaqiao University Xiamen361021 China Xiamen Key Laboratory of Computer Vision and Pattern Recognition Huaqiao University Xiamen361021 China

ISBN: (纸本)9783319699226

Audio-visual speaker recognition (AVSR) has long been an active research area primarily due to its complementary information for reliable access control in biometric system, and it is a challenging problem mainly attributes to its multimodal nature. In this paper, we present an efficient audio-visual speaker recognition approach via deep heterogeneous feature fusion. First, we exploit a dual-branch deep convolutional neural networks (CNN) learning framework to extract and fuse the high-level semantic features of face and audio data. Further, by considering the temporal dependency of audio-visual data, we embed the fused features into a bidirectional Long Short-Term Memory (LSTM) networks to produce the recognition result, though which the speakers acquired under different challenging conditions can be well identified. The experimental results have demonstrated the efficiency of our proposed approach in both audio-visual feature fusion and speaker recognition. © 2017, Springer International Publishing AG.

关键词： Long short-term memory

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