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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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A hybrid deep architecture for face recognition in real-life scenario 10

A hybrid deep architecture for face recognition in real-life...

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

作者： Sanya, A. Bhattacharya, U. Parui, S.K. Department of CSE Indian Institute of Technology Kanpur India CVPR Unit Indian Statistical Institute Kolkata India

this article describes our recent study of a real-life face recognition problem using a hybrid architecture consisting of a very deep convolution neural network (CNN) and a support vector machine (SVM). the novel aspects of this study include (i) implementation of a really deep CNN architecture consisting of 11 layers to study the effect of increasing depth on recognition performance by a subsequent SVM, and (ii) verification of the recognition performance of this hybrid classifier trained by samples of a certain standard size on test face images of smaller sizes reminiscent to various real-life scenarios. Results of the present study show that the features computed at various shallow levels of a deep architecture have identical or at least comparable performances and are more robust than the deepest feature computed at the inner most sub-sampling layer. We have also studied a simple strategy of recognizing face images of very small sizes using this hybrid architecture trained by standard size face images and the recognition performance is reported. We obtained simulation results using the cropped images of the standard extended Yale Face Database which show an interesting characteristic of the proposed architecture with respect to face images captured in a very low intensity lighting condition. © Springer International Publishing AG 2017.

关键词： Face recognition

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Segmentation of lumen and external elastic laminae in intravascular ultrasound images using ultrasonic backscattering physics initialized multiscale random walks 10

Segmentation of lumen and external elastic laminae in intrav...

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

作者： China, Debarghya Mitra, Pabitra Sheet, Debdoot Advanced Technology Development Centre Indian Institute of Technology Kharagpur Kharagpur India Department of Computer Science and Engineering Indian Institute of Technology Kharagpur Kharagpur India Department of Electrical Engineering Indian Institute of Technology Kharagpur Kharagpur India

Coronary artery disease accounts for a large number of deaths across the world and clinicians generally prefer using x-ray computed tomography or magnetic resonance imaging for localizing vascular pathologies. Interventional imaging modalities like intravascular ultrasound (IVUS) are used to adjunct diagnosis of atherosclerotic plaques in vessels, and help assess morphological state of the vessel and plaque, which play a significant role for treatment planning. Since speckle intensity in IVUS images are inherently stochastic in nature and challenge clinicians with accurate visibility of the vessel wall boundaries, it requires automation. In this paper we present a method for segmenting the lumen and external elastic laminae of the artery wall in IVUS images using random walks over a multiscale pyramid of Gaussian decomposed frames. the seeds for the random walker are initialized by supervised learning of ultrasonic backscattering and attenuation statistical mechanics from labelled training samples. We have experimentally evaluated the performance using 77 IVUS images acquired at 40 MHz that are available in the IVUS segmentation challenge dataset (http://***. ***/IVUSchallenge2011/***.) to obtain a Jaccard score of 0.89 ± 0.14 for lumen and 0.85 ± 0.12 for external elastic laminae segmentation over a 10-fold cross-validation study. © Springer International Publishing AG 2017.

关键词： Statistical mechanics

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Tracking of retinal microsurgery tools using late fusion of responses from convolutional neural network over pyramidally decomposed frames 10

Tracking of retinal microsurgery tools using late fusion of ...

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

作者： Mishra, Kaustuv Sathish, Rachana Sheet, Debdoot Indian Institute of Technology Kharagpur Kharagpur India

computer vision and robotic assistance are increasingly being used to improve the quality of surgical interventions. Tool tracking becomes critical in interventions viz. endoscopy, laparoscopy and retinal microsurgery (RM) where unlike open surgery the surgeons do not have direct visual and physical access to the surgical site. RM is performed using miniaturized tools and requires careful observation through a surgical microscope by the surgeon. Tracking of surgical tools primarily provides robotic assistance during surgery and also serves as a means to assess the quality of surgery, which is extremely useful during surgical training. In this paper we propose a deep learning based visual tracking of surgical tool using late fusion of responses from convolutional neural network (CNN) which comprises of 3 steps: (i) training of CNN for localizing the tool tip on a frame (ii) coarsely estimating the tool tip region using the trained CNN and (iii) a finer search around the estimated region to accurately localize the tool tip. Scale invariant tracking of tool is ensured by incorporating multi-scale late fusion where the CNN responses are obtained at each level of the Gaussian scale decomposition pyramid. Performance of the proposed method is experimentally validated on the publicly available retinal microscopy instrument tracking (RMIT) dataset (https://***. com/site/sznitr/code-and-datasets). Our method tracks tools with a maximum accuracy of 99.13% which substantiates the efficacy of the proposed method in comparison to existing approaches. © Springer International Publishing AG 2017.

关键词： Convolution

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Exploring the Limits of VLMs: A Dataset for Evaluating Text-to-Video Generation 24

Exploring the Limits of VLMs: A Dataset for Evaluating Text-...

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15th indian conference on computer vision graphics and image processing

作者： Srivastava, Avnish Sista, Raviteja Chakrabarti, Partha P. Sheet, Debdoot Indian Inst Technol Kharagpur Kharagpur W Bengal India

ISBN: (纸本)9798400710759

vision language models (VLMs) integrate vision and text by learning from images and their textual descriptions for generating text from images and vice-versa. VLMs are used for various tasks such as image captioning, and visual question answering. As VLMs extend to video generation tasks, significant challenges arise. the generated videos often lack temporal consistency, and there exist issues with alignment between the generated video content and the input text. this work proposes a set of prompts for systematically evaluating VLMs on text-to-video generation focused on object coherence and temporal consistency. this dataset of prompt covers two categories: first, the complexity of prompts, and second, the number of objects and actions. the first category consists of four levels of prompt complexity: simple, mid-level, high-level, and unique and rare object prompts. the second category also consists of four levels: single object, single object in action, multiple objects, and multiple objects in action. thus, the dataset is a combination of 16 different prompt scenarios where each scenarios has 10 prompts resulting in a dataset of 160 prompts. this work explores and evaluates the outputs of three models from the family of diffusion-based VLMs for the task of text to video generation. the videos generated by the models were assessed by five participants on a 0-5 Likert scale. VLMs under study were able to generate temporally consistent videos and proper object in video in only 33.63% and 39.43% of the total evaluation scenario prompts. Most of these prompts belong to the categories of single object and simple prompts. the performance of models drops with increase in objects, actions and prompt complexity. the scenarios of actions, with rare prompts are where all models perform poorly. thus, reflecting on limitations of VLMs in generating videos. the proposed dataset can be extended to other video generation models to benchmark their performances on the basic aspects

关键词： Benchmark dataset Diffusion models Temporal consistency Video generation vision language models

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SPODS: A dataset of color-official documents and detection of logo, stamp, and signature 10

SPODS: A dataset of color-official documents and detection o...

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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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Message from the program co-chairs

ACM International Conference Proceeding Series

引用

ACM International conference Proceeding Series 2016年

作者： Batra, Dhruv Natarajan, Vijay Brown, Michael Georgia Institute of Technology United States Indian Institute of Science Bangalore India York University Canada

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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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A Globally-Connected and Trainable Hierarchical Fine-Attention Generative Adversarial Network based Adversarial Defense 13

A Globally-Connected and Trainable Hierarchical Fine-Attenti...

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13th indian conference on computer vision, graphics, and image processing, icvgip 2022

作者： Kumar, Wahengbam Kanan Paidimarri, Manoj Sur, Arijit Department of Computer Science and Engineering Indian Institute of Technology Guwahati Assam Guwahati India

ISBN: (纸本)9781450398237

Deep Neural Network (DNN) inferences have been proven highly susceptible to carefully engineered adversarial perturbations, presenting a pivotal hindrance to real-world computer vision tasks. Most of the existing defenses have poor generalization ability due to their dependence on relatively limited Adversarial Examples (AE). Furthermore, the existing adversarial training necessitates continually retraining a target network with the sort of attack required to be repelled. the defense strategies that are primarily based on processing the perturbed image eventually fall short when pitted against constantly developing threats. Protection of DNN against adversarial attacks remains a difficult challenge on challenging datasets such as Fashion MNIST and CIFAR10. this paper proposes a GAN-based two-stage adversarial training model named Globally Connected and Trainable Hierarchical Fine Attention (GCthFA). the first stage of the proposed GCthFA GAN is to create a reconstructed image that is a purified version of an adversarial example. the proposed approach has used a trainable and globally linked attention map to teach the Generator about the different types of representations an image might have in different convolutional layers located at different levels in a network. the discriminator's reliance on feature vectors produced by transfer learning eliminates the traditional dependency on standard image pixels. the second step involves adversarial training of a target classifier to provide resistance to such attacks. Extensive testing on the MNIST, Fashion MNIST, and CIFAR10 datasets with different classifiers and attacks show that the proposed model can handle adversarial attack settings for various target models. the proposed model uses only one type of adversarial training, with no requirement for retraining based on the type of attack. © 2022 ACM.

关键词： Generative adversarial networks

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ContextCLIP: Contextual Alignment of image-Text pairs on CLIP visual representations 13

ContextCLIP: Contextual Alignment of Image-Text pairs on CLI...

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13th indian conference on computer vision, graphics, and image processing, icvgip 2022

作者： Grover, Chanda Mastan, Indra Deep Gupta, Debayan Ashoka University Sonipat Haryana India Rajasthan Jaipur India

ISBN: (纸本)9781450398237

State-of-the-Art empirical work has shown that visual representations learned by deep neural networks are robust in nature and capable of performing classification tasks on diverse datasets. For example, CLIP demonstrated zero-shot transfer performance on multiple datasets for classification tasks in a joint embedding space of image and text pairs. However, it showed negative transfer performance on standard datasets, e.g., BirdsNAP, RESISC45, and MNIST. In this paper, we propose ContextCLIP, a contextual and contrastive learning framework for the contextual alignment of image-Text pairs by learning robust visual representations on Conceptual Captions dataset. Our framework was observed to improve the image-Text alignment by aligning text and image representations contextually in the joint embedding space. ContextCLIP showed good qualitative performance for text-To-image retrieval tasks and enhanced classification accuracy. We evaluated our model quantitatively with zero-shot transfer and fine-Tuning experiments on CIFAR-10, CIFAR-100, Birdsnap, RESISC45, and MNIST datasets for classification task. © 2022 ACM.

关键词： Embeddings

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