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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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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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Offline handwritten Devanagari word recognition: An HMM based approach

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2nd International Conference on pattern recognition and Machine Intelligence, PReMI 2007

作者： Parui, Swapan Kumar Shaw, Bikash Computer Vision and Pattern Recognition Unit Indian Statistical Institute 203 B.T. Road Kolkata 700108 India

ISBN: (纸本)3540770453

A hidden Markov model (HMM) for recognition of handwritten Devanagari words is proposed. The HMM has the property that its states are not defined a priori, but are determined automatically based on a database of handwritten word images. A handwritten word is assumed to be a string of several stroke primitives. These are in fact the states of the proposed HMM and are found using certain mixture distributions. One HMM is constructed for each word. To classify an unknown word image, its class conditional probability for each HMM is computed. The classification scheme has been tested on a small handwritten Devanagari word database developed recently. The classification accuracy is 87.71% and 82.89% for training and test sets respectively. © Springer-Verlag Berlin Heidelberg 2007.

关键词： Word processing

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TREC 2020 NEWS Track Background Linking Task 29

TREC 2020 NEWS Track Background Linking Task

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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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ISI at the Sigmorphon 2017 shared task on morphological reinflection

ISI at the Sigmorphon 2017 shared task on morphological rein...

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2017 CoNLL SIGMORPHON Shared Task: Universal Morphological Reinflection, CoNLL 2017

作者： Chakrabarty, Abhisek Garain, Utpal Computer Vision and Pattern Recognition Unit Indian Statistical Institute 203 B.T. Road Kolkata700108 India

ISBN: (纸本)9781945626692

We present a system for morphological reinflection based on the LSTM model. Given an input word and morphosyntactic descriptions, the problem is to classify the proper edit tree that, applied on the input word, produces the target form. The proposed method does not require human defined features and it is language independent also. Currently, we evaluate our system only for task 1 without using any external data. From the test set results, it is found that the proposed model beats the baseline on 15 out of the 52 languages in high resource scenario. But its performance is poor when the training set size is medium or low. © 2017 Association for Computational Linguistics.

关键词： Long short-term memory

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Periodic Action Temporal Localization Method Based on Two-Path Architecture for Product Counting in Sewing Video 15th

Periodic Action Temporal Localization Method Based on Two-Pa...

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15th International Conference on Intelligent Computing, ICIC 2019

作者： Huang, Jin-Long Zhang, Hong-Bo Du, Ji-Xiang Zheng, Jian-Feng Peng, Xiao-Xiao Department of Computer Science and Technology Huaqiao University Xiamen China Xiamen Key Laboratory of Computer Vision and Pattern Recognition Huaqiao University Xiamen China

ISBN: (纸本)9783030267650

Automatically product counting in the handmade process plays a vital role in the manufacturing industry, especially at the sewing industry. Nevertheless, there is currently a few methods to count the product number in the hand sewing process from surveillance video automatically. Due to the sewing procedure is a cyclical action, the product counting in hand sewing process is regarded as periodic action temporal localization and counting problem. In this paper, in order to solve this problem, we propose a novel two-path method, based on pose estimation and region-based convolutional neural network. The pose estimation method is used to obtain the trajectory information of human joint points, and the periodic action is located by detecting the periodic changes in joint trajectory. An effective two-threshold method is proposed to locate each action and count the number of periodic action from the trajectory information. To more accurately localization, we use a convolutional neural network to predict whether the workbench is empty or not. We fuse the results of joint trajectory and the status of the workbench to adjust the final periodic action localization and counting the number. To verify the proposed method, we built a new video database collected in the real sewing industry. The experimental results show that the proposed method is effective and constructive at periodic action localization and counting in the video for the sewing industry. © 2019, Springer Nature Switzerland AG.

关键词： Neural networks

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Evaluation of convex optimization techniques for the weighted graph-matching problem in computer vision

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23rd German Association for pattern recognition Symposium, DAGM 2001

作者： Schellewald, Christian Roth, Stefan Schnörr, Christoph Computer Vision Graphics and Pattern Recognition Group Dept Mathematics and Computer Science University of Mannheim MannheimD-68131 Germany

ISBN: (纸本)3540425969

We present a novel approach to the weighted graph-matching problem in computer vision, based on a convex relaxation of the underlying combinatorial optimization problem. The approach always computes a lower bound of the objective function, which is a favorable property in the context of exact search algorithms. Furthermore, no tuning parameters have to be selected by the user, due to the convexity of the relaxed problem formulation. For comparison, we implemented a recently published deterministic annealing approach and conducted numerous experiments for both established benchmark experiments from combinatorial mathematics, and for random ground-truth experiments using computer-generated graphs. Our results show similar performance for both approaches. In contrast to the convex approach, however, four parameters have to be determined by hand for the annealing algorithm to become competitive. © Springer-Verlag Berlin Heidelberg 2001.

关键词： computer vision

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Text independent writer identification for Oriya script

Text independent writer identification for Oriya script

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10th IAPR International Workshop on Document Analysis Systems, DAS 2012

作者： Chanda, Sukalpa Franke, Katrin Pal, Umapada Department of Computer Science and Media Technology Gjøvik University College Norway Computer Vision and Pattern Recognition Unit Indian Statistical Institute India

ISBN: (纸本)9780769546612

Automatic identification of an individual based on his/her handwriting characteristics is an important forensic tool. In a computational forensic scenario, presence of huge amount of text/information in a questioned document cannot be ensured. Lack of data threatens system reliability in such cases. We here propose a writer identification system for Oriya script which is capable of performing reasonably well even with small amount of text. Experiments with curvature feature are reported here, using Support Vector Machine (SVM) as classifier. We got promising results of 94.00% writer identification accuracy at first top choice and 99% when considering first three top choices. © 2012 IEEE.

关键词： Support vector machines

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HR_BMNet: A boundary perception and multiresolution fusion network for semantic segmentation of remote sensing

HR_BMNet: A boundary perception and multiresolution fusion n...

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2024 International Conference on Image Processing and Artificial Intelligence, ICIPAl 2024

作者： Chen, Yan Wang, Mengyuan Jiang, Wenxiang Kang, Menglei Wang, Xiaofeng Collaborative Innovation Centre for Computer Vision and Pattern Recognition School of Artificial Intelligence and Big Data Hefei University Hefei230601 China

ISBN: (纸本)9781510681514

The conventional approach for semantic segmentation of remote sensing imagery using encoder-decoder convolutional neural networks relies on the output of prior feature maps sequentially without considering the interactions between neighboring contextual feature maps with multiple resolutions. While the standard HRNet proposal has successfully improved multi-resolution semantic and spatial features to address the aforementioned issues, its lack of emphasis on boundary perception often results in inadequate target segmentation. Furthermore, a frequent occurrence of multiresolution contextual interaction in HRNet leads to the addition of a significant quantity of redundant information and amplifies the complexity of the model. Hence, to tackle the abovementioned issues, we propose a semantic segmentation network identified as HR-BMNet, which incorporates boundary sensitivity and multiple-resolution learning. The idea associated with standard HRNet is adopted as the foundational architecture. We extend novel boundary perception and multi-resolution fusion attention modules, integrating channel attention mechanisms. The strategy provides an ex-tensive optimization of edges and the efficient capture of crucial multi-scale features. During the feature combination stage, the boundary insights are employed to augment the semantic information, thereby mitigating the spatial details loss, enhancing the intra-class semantic consistency, and achieving superior segmentation. The efficacy of the proposed method is validated through comparison and ablation experiments conducted on the ISPRS Vaihingen and CSRSD datasets. Among the experiments conducted, the best ones attained a mean Intersection over Union (mIoU) of 72.11% on the Vaihingen dataset and 89.28% on the CSRSD dataset, respectively. © 2024 SPIE.

关键词： Semantic Segmentation

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On minimizing errors in 3D reconstruction for stereo camera systems

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pattern recognition and Image Analysis 2007年第2期17卷 337-348页

作者： Wenhardt, S. Denzler, J. Niemann, H. Department of Pattern Recognition University of Erlangen-Nurnberg Martensstr. 3 91058 Erlangen Germany Department of Computer Vision Friedrich Schiller University of Jena Ernst-Abbe-Platz 2 07743 Jena Germany

Active reconstruction of 3D surfaces deals with the control of camer a viewpoints to minimize error and uncertainty in the reconstructed shape of an object. In this paper we develop a mathematical relationship between the setup and focal lengths of a stereo camera system and the corresponding error in 3D reconstruction of a given surface. We explicitly model the noise in the image plane, which can be interpreted as pixel noise or as uncertainty in the localization of corresponding point features. The results can be used to plan sensor positioning, e.g., using information theoretic concepts for optimal sensor data selection. © Nauka/Interperiodica 2007.

关键词： Image reconstruction

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