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10th indian conference on computer vision, graphics and image processing, ICVGIP 2016

作者： Nandedkar, Amit Vijay Mukherjee, Jayanta Sural, Shamik Department of Computer Science and Engineering Indian Institute of Technology Kharagpur India

Office automation is an active area of research. It involves archival and retrieval of official documents. For developing a system for this purpose, it is necessary to have an extensive benchmark dataset consisting various types of official documents. However, it is hard to make available real world official documents as they are mostly confidential. In the absence of such benchmark datasets, it is difficult to evaluate newly developed algorithms. Hence, efforts have been made to build dataset consisting of different categories of documents that resemble real world official documents. In this work, we present a dataset called as scanned pseudo-official data-set (SPODS) which is created by us and made available online. Official documents are usually distinguished by presence of logo, stamp, signature, date, etc. the paper also presents a new approach for the detection of logo, stamp, and signature using spectral filtering and part based features. A comparative study on performances of the proposed method and existing algorithms on the SPODS dataset demonstrates the effectiveness of the proposed technique. © Springer International Publishing AG 2017.

关键词： Office automation

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Quantization Based Data Hiding Scheme for Efficient Quality Access Control of images Using DWT via Lifting

Quantization Based Data Hiding Scheme for Efficient Quality ...

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indian conference on computer vision, graphics & image processing (ICVGIP)

作者： Amit Phadikar Santi P. Maity Malay K. Kundu Department of Information Technology MCKV Institute of Engineering Howrah India Department of Information Technology Bengal Engineering and Science University Howrah India Machine Intelligence Unit & Center for Soft Computing Research Indian Statistical Institute Kolkata India

this paper proposes a transform domain data-hiding scheme for quality access control of images. the original image is decomposed into tiles by applying n-level lifting-based discrete wavelet transformation (DWT). A binary watermark image (external information) is spatially dispersed using the sequence of number generated by a secret key. the encoded watermark bits are then embedded into all DWT-coefficients of n th -level and only in the high-high (HH) coefficients of the subsequent levels using dither modulation (DM) but without complete self-noise suppression. It is well known that due to insertion of external information, there will be degradation in visual quality of the host image (cover). the degree of deterioration depends on the amount of external data insertion as well as step size used for DM. If this insertion process is reverted, better quality of images can be accessed. To achieve that goal, watermark bits are detected using minimum distance decoder and the remaining self-noise due to information embedding is suppressed to provide better quality of image. the simulation results have shown the validity of this claim.

关键词： Quantization Data encapsulation Access control Discrete wavelet transforms Watermarking Delta modulation Discrete transforms Tiles Degradation Decoding

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MR imaging via reduced generalized autocalibrating partially parallel acquisition compressed sensing 10

MR imaging via reduced generalized autocalibrating partially...

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10th indian conference on computer vision, graphics and image processing, ICVGIP 2016

作者： Islam, Sheikh Rafiul Maity, Seba Maity, Santi P. Ray, Ajoy Kumar Kolkata743368 India College of Engineering and Management Kolaghat721171 India Indian Institute of Engineering Science and Technology Shibpur Howrah711103 India

Magnetic Resonance Imaging (MRI) system in recent times demands a high rate of acceleration in data acquisition to reduce the scanning time. the data acquisition rate can be accelerated to a significant order through Parallel MRI (pMRI) approach. An additional improvement in low sensing time for data acquisition can be achieved using Compressed Sensing (CS) or Compressive Sampling that enables reconstruction of a sparse signal from sub-sample (incomplete) measurements. this paper proposes an efficient pMRI scheme by combining CS with Generalized Auto-calibrating Partially Parallel Acquisitions (GRAPPA) to produce an MR image at high data acquisition rate. A kernel of reduced size is used within GRAPPA for estimating the unobserved encoded samples. Instead of all the unobserved samples, a certain number of the same are estimated randomly. Now, an l1-minimization based CS reconstruction technique is used in which the observed and the estimated unobserved samples are taken as measurements to reconstruct the final MR images. Extensive simulation results show that a significant reduction in artifacts and thereby consequent visual improvement in the reconstructed MRIs are achieved even when a high rate of acceleration factor is used. Simulation results also demonstrate that the proposed method outperforms some state-of-art pMRI methods, both in terms of subjective and objective quality assessment for the reconstructed images. © Springer International Publishing AG 2017.

关键词： Compressed sensing

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DePondFi'23 Challenge on Real-Time Pond Environment: Methods and Results

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IEEE ACCESS 2024年 12卷 157975-157987页

作者： Sasithradevi, A. Suganya, R. Prakash, P. Mohamed Mansoor Roomi, S. Vijayalakshmi, M. Nathan, Sabari Kasthuri, P. Persiya, J. Brighty Ebenezer, L. Jain, Sparsh Verma, Sshubam Balasubramanian, S. Sai Subramaniam, M. Sai Sriram, T. Pranav Phanindra Sai, M. Raj, Chandan Yadav, Amandeep Payak, Ritik Paul Choudhury, Suman Singh, Rohit Vellore Inst Technol Ctr Adv Data Sci Chennai 600127 India Vellore Inst Technol Sch Comp Sci & Engn Chennai 600127 India Indian Inst Technol Madras Dept Elect Engn Chennai 600044 India Thiagarajar Coll Engn Dept Elect & Commun Engn Madurai 625015 India Vellore Inst Technol Sch Elect Engn Chennai 600127 India Couger Inc Tokyo Japan Maharaja Agrasen Inst Technol Dept Comp Sci & Engn Delhi 110086 India Sri Sathya Sai Inst Higher Learning Dept Math & Comp Sci Anantapur 515134 Andhra Pradesh India IIT Roorkee Dept Elect Engn Roorkee 247667 India SAHAJ AI Bangalore 560034 India

this article summarizes the Detection of Pond Fish Challenge (DePondfi'23 Challenge), held during the National conference on computer vision, Pattern Recognition, image processing and graphics (NCVPRIPG 2023). the main goal of the challenge was to find the most effective methods for detecting pond fish in underwater images, overcoming obstacles such as poor visibility, variations in turbidity, and environmental shifts. Sixty participants registered, with 15 teams submitting results for phase 1. the challenge concluded with four teams earning top honors based on mAP (mean Average Precision) score and time complexity. the mAP scores achieved by toppers are as follows: DETECTRON - 38.93%, DMACS SAI - 36.65%, Pondvision - 31.63%, and Sahajeevis - 29.06%. this article describes the toppers method and discusses the detection results. Our challenge event is in line with Sustainable Development Goal 14, which focuses on the conservation and sustainable utilization of ponds and marine resources for sustainable development.

关键词： Aquaculture underwater challenges detection models mAP computational time.

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Deep Learning-Based Bangla Isolated Character Recognition from Online and Offline Data 7th

Deep Learning-Based Bangla Isolated Character Recognition fr...

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7th National conference on computer vision, Pattern Recognition, image processing, and graphics, NCVPRIPG 2019

作者： Mukherjee, Himadri Sen, Shibaprasad Dhar, Ankita Obaidullah, Sk. Md. Roy, Kaushik Department of Computer Science West Bengal State University Kolkata India Department of Computer Science & Engineering Future Institute of Engineering and Management Kolkata India Department of Computer Science & Engineering Aliah University Kolkata India

ISBN: (纸本)9789811586965

In spite of the introduction of modern technologies, handwriting is still considered as one of the key means of communication and disseminating information in our daily life. though commercial handwriting recognizers are available for several Western languages but same is not true for Bangla. the complexity of the script is one of the reasons. Recognizing both online and offline handwriting has pros and cons of their own. In this paper, a deep-learning based approach has been adopted to recognize isolated Bangla characters in both online and offline modes. the experiments have been performed on a database of 15000 handwritten Bangla character samples and highest accuracies of 99.91% and 99.92% have been obtained for offline and online modes respectively. © 2020, Springer Nature Singapore Pte Ltd.

关键词： Character recognition

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Optimizing Knowledge Transfer in Sequential Models: Leveraging Residual Connections in Flow Transfer Learning for Lung Cancer Classification 14

Optimizing Knowledge Transfer in Sequential Models: Leveragi...

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14th indian conference on computer vision, graphics and image processing, ICVGIP 2023

作者： Sharma, Richa Kumar, Santosh Shrivastava, Abhishek Bhatt, Tejasav Shri Shankaracharya Institute of Engineering and Technology Chhattisgarh Bhilai India IIIT-Naya Raipur Raipur India United College of Engineering and Research Uttar Pradesh Prayagraj India

ISBN: (纸本)9798400716256

Lung cancer is a prevalent and life-threatening disease characterized by abnormal cell growth in lung tissue. Early detection and accurate classification of lung cancer are crucial for timely treatment and improved patient outcomes. Our work proposes, a novel framework for accurate multi-class categorization of lung cancer using deep learning. the proposed framework uses a customized Densenet-201 model to leverage the knowledge of transfer learning, which acts as a parent architecture, enhanced by a residual structure as the child architecture. the performance of our model was examined by conducting experiments on the LCS25000 data set. the results demonstrate the outstanding accuracy of our proposed framework, achieving a remarkable accuracy of 95% on the test data set. this signifies the model's ability to accurately classify different types of lung cancer based on histopathology images. Our model also achieved remarkably well results in the TCGA lung cancer dataset, hence proving its generalization. these findings have important repercussions for improving pulmonary pathology diagnostic abilities and hold promise for enhancing patient care in the field of lung cancer diagnosis. © 2023 Copyright held by the owner/author(s). Publication rights licensed to ACM.

关键词： image classification

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Message from the general chairs

ACM International Conference Proceeding Series

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ACM International conference Proceeding Series 2014年 14卷

作者： Ramakrishnan, A.G. Malik, Jitendra Indian Institute of Science Bangalore India University of California Berkeley United States

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Intelligent video editing: Incorporating modern talking face generation algorithms in a video editor 21

Intelligent video editing: Incorporating modern talking face...

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12th indian conference on computer vision, graphics and image processing, ICVGIP 2021

作者： Gupta, Anchit Khan, Faizan Farooq Mukhopadhyay, Rudrabha Namboodiri, Vinay P. Jawahar, C.V. IIIT Telangana Hyderabad India University of Bath United Kingdom

ISBN: (纸本)9781450391276

this paper proposes a video editor based on OpenShot with several state-of-the-art facial video editing algorithms as added functionalities. Our editor provides an easy-to-use interface to apply modern lip-syncing algorithms interactively. Apart from lip-syncing, the editor also uses audio and facial re-enactment to generate expressive talking faces. the manual control improves the overall experience of video editing without missing out on the benefits of modern synthetic video generation algorithms. this control enables us to lip-sync complex dubbed movie scenes, interviews, television shows, and other visual content. Furthermore, our editor provides features that automatically translate lectures from spoken content, lip-sync of the professor, and background content like slides. While doing so, we also tackle the critical aspect of synchronizing background content with the translated speech. We qualitatively evaluate the usefulness of the proposed editor by conducting human evaluations. Our evaluations show a clear improvement in the efficiency of using human editors and an improved video generation quality. We attach demo videos with the supplementary material clearly explaining the tool and also showcasing multiple results. © 2021 ACM.

关键词： Video signal processing

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Stochastic fusion of multi-view gradient fields

Stochastic fusion of multi-view gradient fields

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IEEE International conference on image processing

作者： Aswin C. Sankaranarayanan Rama Chellappa Center for Automation Research and Department of Electrical and Computer Engineering University of Maryland College Park MD USA

image gradients form powerful cues in a host of vision and graphics applications. In this paper, we consider multiple views of a textured planar scene and consider the problem of estimating the scene texture map using these multi-view inputs. Modeling each camera view as a projective transformation of the scene, we show that the problem is equivalent to that of studying the effect of noise (and the projective imaging) on the gradient fields induced by this texture map. We show that these noisy gradient fields can be modeled as complete observers of the scene radiance. Further, the corrupting noise can be shown to be additive and linear, although spatially varying. However, the specific form of the noise term can be exploited to design linear estimators that fuse the gradient fields obtained from each of the individual views. the fused gradient field forms a robust estimate of the scene gradients and can be used for scene reconstruction.

关键词： Stochastic processes Layout Cameras image motion analysis Optical imaging Additive noise Fuses image fusion Biomedical optical imaging Optical distortion

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A novel information theoretic cost measure for filtering based feature selection from hyperspectral images 4th

A novel information theoretic cost measure for filtering bas...

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4th International conference on computer vision and image processing, CVIP 2019

作者： Kookna, Vikas Singh, Ankit Kumar Raj, Agastya Banerjee, Biplab Indian Institute of Technology Roorkee India Indian Institute of Technology Bombay India

ISBN: (纸本)9789811540141

In this paper, we address the supervised dimensionality reduction problem for improved hyperspectral image (HSI) classification. In particular, we design a novel information theoretic ranking measure and further propose a greedy strategy that provides a more principled way of ranking the features. Note that the use of information theory is prevalent in the area of filtering based dimensionality reduction. However, the existing ranking measures in this regard are predominantly based on the notion of enforcing non-redundancy among the selected features while simultaneously ensuring relevance. In the process, it is not considered whether the selected feature dimensions are representatives of the discarded ones. However, those abandoned dimensions may contain subtle information regarding some of the land-cover classes. Inspired by this analogy, we propose a new ranking function in this paper which generates an ordering of the dimensions based on non-redundancy, relevancy while ensuring that the selected feature dimensions are representatives of all the original feature dimensions. We subsequently propose a graph-based greedy algorithm for feature selection in this regard. Experimental results on widely studied HSI datasets confirm the superiority of the proposed method compared to the existing information theoretic feature selection strategies. © Springer Nature Singapore Pte Ltd 2020.

关键词： Redundancy

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