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检索条件"任意字段=Proceedings of the Fourteenth Indian Conference on Computer Vision, Graphics and Image Processing"

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Removal of hand-drawn annotation lines from document images by digital-geometric analysis and inpainting

Removal of hand-drawn annotation lines from document images ...

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National conference on computer vision, Pattern Recognition, image processing and graphics (NCVPRIPG)

作者： Sanjoy Pratihar Partha Bhowmick Shamik Sural Jayanta Mukhopadhyay Department of Computer Science & Engineering Indian Institute of Technology Kharaunur India School of Information Technology Indian Institute of Technology Kharagpur India

Performance of an OCR system is badly affected due to presence of hand-drawn annotation lines in various forms, such as underlines, circular lines, and other text-surrounding curves. Such annotation lines are drawn by a reader usually in free hand in order to summarize some text or to mark the keywords within a document page. In this paper, we propose a generalized scheme for detection and removal of these hand-drawn annotations from a scanned document page. An underline drawn by hand is roughly horizontal or has a tolerable undulation, whereas for a hand-drawn curved line, the slope usually changes at a gradual pace. Based on this observation, we detect the cover of an annotation object-be it straight or curved-as a sequence of straight edge segments. The novelty of the proposed method lies in its ability to compute the exact cover of the annotation object, even when it touches or passes through any text character. After getting the annotation cover, an effective method of inpainting is used to quantify the regions where text reconstruction is needed. We have done our experimentation with various documents written in English, and some results are presented here to show the efficiency and robustness of the proposed method.

关键词： Morphological operations image edge detection

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Few-shot classification without forgetting of event-camera data 21

Few-shot classification without forgetting of event-camera d...

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

作者： Goyal, Anik Biswas, Soma Indian Institute of Science Karnataka Bangalore India

ISBN: (纸本)9781450391276

Event-based cameras can capture changes in brightness in the form of asynchronous events, unlike traditional cameras, which has sparked tremendous interest due to their wide range of applications. In this work, we address for the first time in literature, the task of few-shot classification of event data without forgetting the base classes on which it has been initially trained. This not only relaxes the constraint of data availability from all possible classes before the initial model is trained, but also the constraint of capturing large amounts of training data for each of the classes we want to classify. The proposed framework has three main stages: First, we train the base classifier by augmenting the original event data using a data mixing technique, so that the feature extractor can better generalize to unseen classes. We also utilize an adaptive semantic similarity between the classifier weights. This guarantees that the margin between similar classes is greater than that between dissimilar classes which in turn reduces confusion between similar classes. Second, weight imprinting is employed to learn the initial classifier weights for the new classes with few examples. Finally, we finetune the entire framework using a class-imbalance aware loss in an end-to-end manner. This is accomplished by converting the event data via a series of differentiable operations, which are then fed into our network. Extensive experiments on few-shot versions of two standard event-camera datasets justify the effectiveness of the proposed framework. We believe that this study will serve as a solid foundation for future work in this critical field. © 2021 ACM.

关键词： Cameras

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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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Semantic description of a video using representative frames

Semantic description of a video using representative frames

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National conference on computer vision, Pattern Recognition, image processing and graphics (NCVPRIPG)

作者： Ishan Jindal Shanmuganathan Raman Electrical Engineering Indian Institute of Technology Gandhinagar

ISBN: (纸本)9781467385657

Analysis of a very long video and semantically describe the contents is a challenging task in computer vision. The present approaches such as video shot detection and summarization address this problem partially while maintaining the temporal coherency. To reduce the user efforts for seeing the whole video we have introduced a new technique which combines similar content irrespective of their presence at different time instants. In this approach, we automatically identify only the representative frames corresponding to similar scenes which were captured at different instants of time. We also provide the labels of the objects that are present in the representative frames along with the compact representation for the video. We achieve the task of semantic labelling of frames in a unified framework using a deep learning framework involving pre-trained features through a convolutional neural network. We show that the proposed approach is able to address the semantic labelling effectively as justified by the results obtained for videos of different scenes captured through different modalities.

关键词： Semantics Histograms Cameras YouTube Surveillance Feature extraction Labeling

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Word recognition in natural scene and video images using Hidden Markov Model

Word recognition in natural scene and video images using Hid...

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National conference on computer vision, Pattern Recognition, image processing and graphics (NCVPRIPG)

作者： Sangheeta Roy Partha Pratim Roy Palaiahnakote Shivakumara Umapada Pal Tata Consultancy Services Kolkata India CVPR Unit Indian Statistical Institute Kolkata India University of Malaya Kuala Lumpur Malaysia

Text recognition from a natural scene and video is challenging compared to that in scanned document images. This is due to the problems of text on different sources of various styles, font variation, font size variations, background variations, etc. There are approaches for word segmentation from video and scene images to feed the word image into OCRs. Nevertheless, such methods often fail to yield satisfactory results in recognition. Therefore, in this paper, we propose to combine Hidden Markov Model (HMM) and Convolutional Neural Network (CNN) to achieve good recognition rate. Sequential gradient features with HMM help to find character alignment of a word. Later the character alignments are verified by Convolutional Neural network (CNN). The approach is tested on both video and scene data to show the effectiveness of the proposed approach. The results are found encouraging.

关键词： Hidden Markov models Text recognition Character recognition image recognition Feature extraction image segmentation Neural networks

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Multimodal image registration of lung images

Multimodal image registration of lung images

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National conference on computer vision, Pattern Recognition, image processing and graphics (NCVPRIPG)

作者： Bijju Kranthi Veduruparthi Jayanta Mukherjee Partha Pratim Das Sanjoy Chatterjee Soumendranath Ray Partha Sen Department of Computer Science and Engineering Indian Institute of Technology Kharagpur Nuclear Medicine Department Tata Medical Center Kolkata

Lung tumor estimation on imaging modalities is required to assess the extent of the tumor for diagnosis. Segmentation of tumor in Cone-Beam Computed Tomography (CBCT) images is non-trivial due to its imaging artifacts. Here we propose a novel technique for image registration of 18-Fluoro deoxyglucose Positron Emission Tomography (PET) and Computed Tomography(CT) images with CBCT images. The computation is performed in two stages. In the first stage, mutual information based rigid image registration is performed to obtain a rough global alignment of CBCT image with the corresponding PET and CT images. This result is fed to the second stage to perform deformable image registration between a pair of corresponding CBCT volumes of the same patient captures at different time instances using a viscous fluid model. The technique is adapted in both 2D (for slicewise computation) and 3D space (for computing with volume), and a comparative performance is presented with a simulated deformation model.

关键词： Computed tomography Positron emission tomography Tumors Mutual information Three-dimensional displays image registration

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UKF based multi-component phase estimation in digital holographic Moiré

UKF based multi-component phase estimation in digital hologr...

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National conference on computer vision, Pattern Recognition, image processing and graphics (NCVPRIPG)

作者： Rahul G Waghmare Deepak Mishra Sai Subrahmanyam G. R. K Dept. of Avionics IIST Thiruvananthapuram Kerala India Indian Institute of Space Science and Technology Thiruvananthapuram Kerala IN

Simultaneous measurement of multi-dimensional deformations can be done in digital holographic Moiré by illuminating the object with multiple beams from different angles and recording the interference pattern due to multiple beams. This technique involves reliable estimation of resultant multiple phase maps from a one record of the reconstructed interference moiré field. In this paper, we introduce a novel method, based on Unscented Kalman Filter (UKF), for the estimation of multiple phase maps from a complex multi-component signal encountered in digital holographic moiré. The State space model used in this method is determined by Taylor series expansion of phase component as process model and polar to Cartesian conversion as measurement model.

关键词： Noise Interference Mathematical model Vectors Equations Phase estimation Estimation

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Salient object detection in SfM point cloud

Salient object detection in SfM point cloud

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National conference on computer vision, Pattern Recognition, image processing and graphics (NCVPRIPG)

作者： Divyansh Agarwal Nishit Soni Anoop M. Namboodiri International Institute of Information Technology Hyderabad Gachibowli Telangana IN

In this paper we present a max-flow min-cut based salient object detection in 3D point cloud that results from Structure from Motion (SfM) pipeline. The SfM pipeline generates noisy point cloud due to the unwanted scenes captured along with the object in the image dataset of SfM. The background points being sparse and not meaningful, it becomes necessary to remove them. Hence, any further processes (like surface reconstruction) utilizing the cleaned up model will have no hinderance from the noise removed. We present a novel approach where the camera centers are used to segment out the salient object. The algorithm is completely autonomous and does not need any user input. We test our proposed method on indian historical models reconstructed through SfM. We evaluate the results in terms of selectivity and specificity.

关键词： Three-dimensional displays Cameras image segmentation Approximation algorithms image reconstruction Surface morphology Pipelines

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Simultaneous reconstruction of multiple hand shredded content-less pages using graph-based global reassembly 10

Simultaneous reconstruction of multiple hand shredded conten...

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

作者： Lalitha, K.S. Das, Sukhendu Menon, Arun Varghese, Koshy Department of Computer Science and Engineering IIT Madras Chennai India Department of Civil Engineering IIT Madras Chennai India

Hand shredded content-less pages reassembly is a challenging task. This has applications in forensics and fun games. The process is even more tedious when the number of pages from which the fragments are obtained is unknown. An iterative framework to solve the jigsaw puzzles of multiple hand shredded content-less pages has been proposed in this paper. This framework makes use of the shape-based information alone to solve the puzzle. All pairs of fragments are matched using the normalized shape-based features. Then, incorrect matches between the fragments are pruned using three scores that measure the goodness of the alignment. Finally, a graph-based technique is used to densely arrange the fragments for the global reassembly of the page(s). Experimental evaluation of our proposed framework on an annotated dataset of shredded documents shows the efficiency in the reconstruction of multiple content-less pages from arbitrarily torn fragments and performance metrics have been proposed to numerically evaluate the reassembly. © Springer International Publishing AG 2017.

关键词： Iterative methods

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Infinitesimal Machinery - A Nanorobotics System for Cancer Treatment

Infinitesimal Machinery - A Nanorobotics System for Cancer T...

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National conference on computer vision, Pattern Recognition, image processing and graphics (NCVPRIPG)

作者： D. Dutta Majumder Sankar Karan Electronics and Communication Science Unit Indian Statistical Institute Kolkata India Institute of Radiophysics and Electronics Calcutta University Kolkata India

We present an approach to control the machines at the nano-meter or molecular scale (10-9 meter) in the perspective of the theory of cybernetics, the science of control, communication and computation with integration to complex man -- machines systems. Though our theory is general, on study we concentrate on nano-robot design and control by describing various molecular level systems. We discuss the method of special autonomous robotic systems that can be used for bio-medical and bio-mechanical purposes. We also present a theory of nano scale self assembly for prototyping bio-nano systems with the goal of implementing their internal optimum movements in biological environment to perform manipulation of nano-medicine specially for cancer treatment. The system casualty, state notation and automata for nano-robotic systems in the light of general systems theory are suggested.

关键词： Nanobioscience Cancer Shape Drugs Self-assembly Nanoparticles Cybernetics

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