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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"

共 1494 条记录，以下是1391-1400 订阅

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Perfectly Secure Shamir's Secret Sharing Scheme for Privacy Preserving image processing Over Cloud 2018

Perfectly Secure Shamir's Secret Sharing Scheme for Privacy ...

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proceedings of the 11th indian conference on computer vision, graphics and image processing

作者： Debanjan Sadhya Akshay Agarwal Balasubramanian Raman Indian Institute of Technology Roorkee Roorkee India Manipal Institute of Technology Manipal India

ISBN: (纸本)9781450366151

Cloud computing is a highly prospective paradigm in which computational resources from third parties are used for processing outsourced data. Nonetheless, the distributed architecture of this concept poses many security and privacy threats for the data owners. Shamir's secret sharing is an effective technique for distributing and processing secret images over the encrypted domain. However, it has got some critical limitations primarily due to the presence of correlated information between the image pixels. Our study addresses this problem by proposing a perfectly secure Shamir's secret sharing scheme for images. Our work builds upon the formal notion of perfect secrecy for encoding the Shamir's shares in a particular manner such that they (i.e. encoded shares) do not reveal any additional information about the original image. Importantly, we have provided both theoretical and empirical validation of our proposed approach. We have also performed several image filtering operations on the stored shares and found the resulting PSNR and NCC values to be similar in the plain and encrypted domains. Hence our work provides a privacy-preserving and secure framework for working with images over a cloud-based architecture.

关键词： Perfect Secrecy Encrypted Domain Cloud Computing Shamir's Secret Sharing

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Cluster Loss for Person Re-Identification 18

Cluster Loss for Person Re-Identification

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proceedings of the 11th indian conference on computer vision, graphics and image processing

作者： Doney Alex Zishan Sami Sumandeep Banerjee Subrat Panda Capillary Technologies Indian Institute of Technology Kharagpur

ISBN: (纸本)9781450366151

Person re-identification (ReID) is an important problem in computer vision, especially for video surveillance applications. The problem focuses on identifying people across different cameras or across different frames of same camera. The main challenge lies in identifying similarity of the same person against large appearance and structure variations, while differentiating between individuals. Recently, deep learning networks with triplet loss has become a common framework for person ReID. However, triplet loss focuses on obtaining correct orders on the training set. We demonstrate that it performs inferior in a clustering task. In this paper, we design a cluster loss, which can lead to the model output with a larger inter-class variation and a smaller intra-class variation compared to the triplet loss. As a result, our model has a better generalisation ability and can achieve a higher accuracy on the test set especially for a clustering task. We also introduce a batch hard training mechanism for improving the results and faster convergence of training.

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A Novel Statistical High Density Salt-and-Pepper Noise Removal Algorithm for Brain Magnetic Resonance images 22

A Novel Statistical High Density Salt-and-Pepper Noise Remov...

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proceedings of the Thirteenth indian conference on computer vision, graphics and image processing

作者： Amiya Halder Rudrajit Choudhuri Pritam Bhattacharya Apurba Sarkar St Thomas College of Engineering and Technology India St Thomas' College of Engineering & Technology India IIEST Shibpur India

ISBN: (纸本)9781450398220

Brain Magnetic Resonance Imaging (MRI) is a non-invasive technique that produces high quality images of the brain and is most suitable for analysis and diagnosis. However, these images can be soiled with noise during image acquisition or transmission. The paper is targeted at removing high density salt and pepper noise from such medical images using a denoising technique based on centroidal mean formulation. The presented method is tested on various noisy brain MRI images and the obtained results are promising even for images with high density corruptions, which is suggestive of the resiliency of the algorithm.

关键词： Statistical Denoising Technique Brain MRI images. Medical image Enhancement Centroidal Mean

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Detecting Coronavirus (COVID -19) Disease Cues from Chest Radiography images 22

Detecting Coronavirus (COVID -19) Disease Cues from Chest Ra...

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proceedings of the Thirteenth indian conference on computer vision, graphics and image processing

作者： Athira Vinod Shilna Sreeni K G Anurenjan Purushothaman Jeena R S Computer Vision Lab Dept. of Electronics and Communication College of Engineering Trivandrum India Computer Vision Lab Dept. of Electronics and Communication College of Engineering Trivandrum Indochina Computer Vision Lab Dept. of Electronics and Communication CET India Computer Vision Lab Dept. of Electronics and Communication College of Engineering Trivandrum India

ISBN: (纸本)9781450398220

This paper proposes a deep learning-based approach to detect COVID-19 infections in lung tissues from chest Computed Tomography (CT) images. A two-stage classification model is designed to identify the infection from CT scans of COVID-19 and Community Acquired Pneumonia (CAP) patients. The proposed neural model named, Residual C-NiN uses a modified convolutional neural network (CNN) with residual connections and a Network-in-Network (NiN) architecture for COVID-19 and CAP detection. The model is trained with the Signal processing Grand Challenge (SPGC) 2021 COVID dataset. The proposed neural model achieves a slice-level classification accuracy of 93.54% on chest CT images and patient-level classification accuracy of 86.59% with class-wise sensitivity of 92.72%, 55.55%, and 95.83% for COVID-19, CAP, and Normal classes, respectively. Experimental results show the benefit of adding NiN and residual connections in the proposed neural architecture. Experiments conducted on the dataset show significant improvement over the existing state-of-the-art methods reported in the literature.

关键词： Residual connections Network in Network COVID-19 Computed Tomography

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EDCGAN: Encoder Decoder based Conditional GAN for SAR to Optical image translation ✱ 22

EDCGAN: Encoder Decoder based Conditional GAN for SAR to Opt...

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proceedings of the Thirteenth indian conference on computer vision, graphics and image processing

作者： Avinash Chouhan Nitesh Jindal Arijit Sur Dibyajyoti Chutia Shiv Prasad Aggarwal North Eastern Space Applications Centre IN and Indian Institute of Technology Guwahati India Indian Institute of Technology Guwahati IN North Eastern Space Applications Centre IN

ISBN: (纸本)9781450398220

High-resolution optical images are heavily utilized in various remote sensing applications. The optical images cannot reflect the actual ground information in cloudy conditions. SAR images are used to solve this for their ability to see through clouds. But SAR images are usually available with coarser resolutions. So, there is a need to produce an optical image from a SAR image to overcome bad weather and poor resolution in a single go. In this paper, a novel deep learning architecture named EDCGAN is proposed. The proposed architecture is an encoder-decoder-based conditional GAN that uses multi-scale attentive discrimination to get accurate SAR to RGB image translation. In addition, we have used residual connections, spatial & channel-wise attention for better feature representation. A set of extensive experimentations show that this architecture outperforms the existing state-of-the-art method in terms of PSNR, SSIM, and FSIM_c values for the WHU-SENCity dataset.

关键词： Conditional GAN

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Toward automatic reconstruction of 3D environment with an active binocular head

Toward automatic reconstruction of 3D environment with an ac...

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International conference on Pattern Recognition

作者： Chung-Yi Lin Sheng-Wen Shih Yi-Ping Hung Department of Computer Science and Information Engineering National Taiwan University Taipei Taiwan Department of Computer Science and Information Engineering National Chi Nan University Nantou Taiwan Institute of Information Science Academia Sinica Taipei Taiwan

ISBN: (纸本)0818685123

This paper presents a new automatic approach to reconstructing a model for the 3D environment by use of an active binocular head (the IIS head). To efficiently store and access the depth estimates, we propose the use of the inverse polar octree. The depth estimates are computed by using the asymptotic Bayesian estimation method. The path of the local motion required by the asymptotic Bayesian method is determined online automatically to reduce the ambiguity of stereo matching. Some rules for checking the consistency between the new observation and the previous observations have been developed. Our experimental results have shown that the proposed approach is very promising for automatic generation of 3D models which can be used for rendering a 3D scene in a virtual reality system.

关键词： image reconstruction Head computer science Bayesian methods Layout Information science Virtual reality Stereo vision Data mining Stereo image processing

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SegFast: A Faster SqueezeNet based Semantic image Segmentation Technique using Depth-wise Separable Convolutions 18

SegFast: A Faster SqueezeNet based Semantic Image Segmentati...

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proceedings of the 11th indian conference on computer vision, graphics and image processing

作者： Anisha Pal Shourya Jaiswal Swarnendu Ghosh Nibaran Das Mita Nasipuri Dept. of Information and Communication Technology Manipal Institute of Technology Manipal Karnataka Dept. of Electronics and Communication Engineering Manipal Institute of Technology Manipal Karnataka Dept. of Computer Science and Engineering Jadavpur University Kolkata West Bengal

ISBN: (纸本)9781450366151

Recent trends in image segmentation algorithms have shown various large scale networks with impressive performance for natural scene images. However most of the networks come with costly overheads such has large memory requirements or dependence on huge number of parallel processing units. In most cases costly graphics processing units or GPUs are used to boost computational capability. However for creating products in the real world we need to consider speed, performance as well as cost of deployment. We propose a novel "spark" module which is a combination of the "fire" module of SqueezeNet and depth-wise separable convolutions. Along with this modified SqueezeNet as an encoder we also propose the use of depth-wise separable transposed convolution for a decoder. The resultant encoder-decoder network has approximately 49 times lesser number of parameters than SegNet and almost 223 times lesser number of parameters than fully convolutional networks(FCN). Even in a CPU the network completes a forward pass for a single sample in approximately 0.39 seconds which is almost 5.1 times faster as compared to SegNet and almost 8.7 times faster compared to FCN.

关键词： Deep Learning

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A Globally-Connected and Trainable Hierarchical Fine-Attention Generative Adversarial Network based Adversarial Defense 22

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

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proceedings of the Thirteenth indian conference on computer vision, graphics and image processing

作者： Wahengbam Kanan Kumar Manoj Paidimarri Arijit Sur CSE Indian Institute of Technology Guwahati IN

ISBN: (纸本)9781450398220

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.

关键词： Adversarial Perturbation Deep Learning Object Classification Generative Adversarial Network

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Comparison of iris recognition algorithms

Comparison of iris recognition algorithms

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International conference on Intelligent Sensing and Information processing (ICISIP)

作者： M. Vatsa Richa Singh P. Gupta Department of CSE Indian Institute of Technology Kanpur India

In this paper, we have studied various well known algorithms for iris recognition. Four algorithms due to Sanchez-Avila et al. (2001), Li Ma et al. (2002), Tisse et al. (2002) and Daugman (2001) are implemented and compared on the CASIA iris image database. The results show that the Daugman's algorithm gave the highest accuracy of 99.9%.

关键词： Iris recognition Biometrics Cornea Humans image databases Authentication Fingerprint recognition Muscles Contracts computer vision

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VidSeg-GAN: Generative Adversarial Network for Video Object Segmentation Tasks 2018

VidSeg-GAN: Generative Adversarial Network for Video Object ...

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proceedings of the 11th indian conference on computer vision, graphics and image processing

作者： Saptakatha Adak Sukhendu Das Indian Institute of Technology Madras Chennai India

ISBN: (纸本)9781450366151

Video object segmentation aims to segment objects in a video sequence, given some user annotation which indicates the object of interest. Although Convolutional Neural Networks (CNNs) have been used in the recent past for the purpose of foreground segmentation in videos, adversarial training methods have not been used effectively to solve this problem, in spite of its extensive use for solving many other problems in computer vision. Earlier, flow features and motion trajectories have been extensively used to capture the temporal consistency between subsequent frames to segment moving objects in videos. However, we show that our proposed framework of processing the video frames independently using a deep generative adversarial network (GAN), is able to maintain the temporal coherency across frames without the use of any explicit trajectory based information, to provide superior results. Our main contribution lies in introducing a GAN based framework along with the incorporation of an Intersection-over-Union score based novel cost function for training the model, to solve the problem of foreground object segmentation in videos. The proposed method, when evaluated on popular real-world video segmentation datasets viz. DAVIS, SegTrack-v2 and YouTube-Objects, exhibits substantial performance gain over the recent state-of-the-art methods.

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