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A Deep Learning Architecture Based Dimensionality Reduction and Online Signature Verification 4th

A Deep Learning Architecture Based Dimensionality Reduction ...

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4th Workshop on Document Analysis and Recognition, DAR 2018, held in Conjunction with the 11th indian conference on vision, graphics, and image processing, ICVGIP 2018

作者： Vorugunti, Chandra Sekhar Pulabaigari, Viswanath Indian Institute of Information Technology-SriCity Chittoor India

ISBN: (纸本)9789811393600

In this paper, we propose a novel hybrid deep learning based autoencoder-CNN-Softmax architecture aims at obtaining reduced dimension feature set from raw feature set. the reduced feature set forms an input to CNN layers to learn deep global features. these global features are used to train the SoftMax layer for online signature classification. Ability to reduce the noisy features and to discover the hidden corelated features makes the proposed architecture light weight and efficient to use in critical applications like online signature verification (OSV) and to deploy in resource constraint mobile devices. We demonstrate the superiority of our model for feature correlation learning and signature classification by conducting experiments on standard datasets MCYT, SUSIG. the experimentation confirms that the proposed model achieves better accuracy (lower error rates) with a lesser number of features compared to the current state-of-the-art models. the proposed models yield state-of-the-art performance of 0.4% EER on MCYT-100 dataset and 3.47% with SUSIG dataset. © Springer Nature Singapore Pte Ltd. 2019.

关键词： Classification (of information)

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Unique Identity Generation with Global Features from Multimodal Biometric Data 14

Unique Identity Generation with Global Features from Multimo...

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

作者： Dash, Priyabrata Samanta, Debasis Sarma, Monalisa Indian Institute of Technology Kharagpur West Bengal Kharagpur India

ISBN: (纸本)9798400716256

the patterns in biometric data, such as fingerprints, iris, etc., are random and distinct from one individual to another, making them ideal for generating unique identities suitable for many applications. this work proposes creating users' unique identities based on their fingerprint and iris biometric data. the global feature descriptor, which is invariant to scale, rotation, and translation, is considered in this work. the global feature descriptors are obtained from fingerprint and iris images and fused to create a single feature vector. Next, feature selection based on the Laplacian score is applied to remove irrelevant features. Further, a metric-learning-based mechanism is introduced to enhance the feature vectors' intra-class similarity and inter-class separability. Finally, a novel quantization mechanism is used to generate a unique identity. Experiments with multimodal datasets show that the generated identities are distinct, random, revocable, and unlinkable. this identity generation scheme can be utilized in storage security, digital signature schemes, remote authentication protocols, blockchain security, etc. © 2023 Copyright held by the owner/author(s). Publication rights licensed to ACM.

关键词： Authentication

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Alzheimer's severity classification using Transfer Learning and Residual Separable Convolution Network 13

Alzheimer's severity classification using Transfer Learning ...

引用

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

作者： Isunuri, Bala Venkateswarlu Kakarla, Dr Jagadeesh Indian Institute of Information Technology Design and Managment Kancheepuram Tamil Nadu Chennai India

ISBN: (纸本)9781450398237

Severity classification is the most pivotal task in Alzheimer's disease diagnosis. Detection of brain structural changes from brain MR images is crucial for Alzheimer's classification. In this paper, we have proposed a transfer learning and residual separable convolution network for the classification of Alzheimer's. the proposed network includes three separable convolution layers with two average pooling layers. An upsampling has been performed to regain its spatial resolution for the residual connection. the main intuition of separable convolution is to optimize parameters with depth-wise convolution. Similarly, the residual connection has been used to reduce the vanishing gradient problem. Finally, a three-layer fully connected dense network has been used for the four-class Alzheimer's classification. Kaggle dataset has been utilized for the experiments to report results. We have achieved an accuracy of 97.32% on the dataset with five-fold cross-validation. Our model has reported an improvement of 1% in jaccard similarity and outperforms the competing models in all vital metrics. © 2022 ACM.

关键词： Magnetic resonance imaging

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Crowd abnormality detection and localization - A matrix decomposition approach 14

Crowd abnormality detection and localization - A matrix deco...

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9th indian conference on computer vision, graphics and image processing, ICVGIP 2014

作者： Gupta, Vikas Biswas, Soma Department of Electrical Engineering Indian Institute of Science Bangalore India

ISBN: (纸本)1595930361

Abnormality detection in crowded scenes plays a very important role in automatic monitoring of surveillance feeds. Here we present a novel framework for abnormality detection in crowd videos. the key idea of the approach is that rarely or sparsely occurring events correspond to abnormal activities while the commonly occurring events correspond to the normal activities. Given an input video, multiple feature matrices are computed which are decomposed into their low-rank and sparse components, out of which the sparse components correspond to the abnormal activities. the approach does not require any explicit modeling of crowd behavior or training. Localization of the anomalies is obtained as a by-product of the proposed approach by doing an inverse mapping between the entries of the matrix and the pixels in the video frames. the method is very general and can be applied for both sparsely crowded as well as densely crowded scenes and it can be used to detect both global and local abnormalities. Experimental evaluation on two widely used datasets as well as some dense crowd videos downloaded from the web shows the effectiveness of the proposed approach. Comparison with several state-of-the-art crowd abnormality detection approaches show that the proposed method compares well as compared to the other approaches. Copyright is held by the authors.

关键词： Matrix algebra

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Fine-grain Cluster Estimation of Land Cover Classes using Landsat 8 Multi-spectral images 14

Fine-grain Cluster Estimation of Land Cover Classes using La...

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

作者： Pal, Shankho Subhra Chatterjee, Ankita Mukhopadhyay, Jayanta Sarkar, Sudeshna Das, Partha Pratim Indian Institute of Technology Kharagpur West Bengal Kharagpur India Ashoka University Haryana Sonipat India

ISBN: (纸本)9798400716256

Earth observation satellites provide us with ample amount of raw data for land cover analysis. However, annotating these data is a cumbersome process, subjected to human error which compel us to shift from supervised to unsupervised techniques. Although clustering methods are being widely used for the past few years in the field of remote sensing, identifying the number of fine-grain classes present in a region remain a challenging problem. therefore, we propose a rule-based and neural-network learning technique that can divide the pixels into three standard classes, water bodies, vegetation and vegetation-void. these classes are easier to identify and is not region-specific. Later we apply fine-grain clustering on each of these classes to segregate them into finer groups. Our clustering method identifies the appropriate number of fine-grain classes present in a specific region. © 2023 Copyright held by the owner/author(s). Publication rights licensed to ACM.

关键词： Landsat

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GAN based indian sign language synthesis 21

GAN based indian sign language synthesis

引用

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

作者： Krishna, Shyam Ukey, Janmesh Babu, J. Dinesh International Institute of Information Technology Bengaluru India Reliance Jio AICoE Hyderabad India

ISBN: (纸本)9781450391276

Controllable visual reproduction of sign language, termed Sign Language Synthesis (SLS), is a major and challenging task in sign language processing. Traditional methods have used computer animation to perform this task, but these have faced several limitations. Animation usually requires expensive equipment to perform motion capture and intensive manual oversight to ensure accuracy. Recently, Generative Adversarial Networks (GANs) have had very promising results in pose and motion transfer. this has been explored as a SLS method in Stoll, et al. [22] and related work. However, this work has required datasets with manual annotation, both in the form of requiring a corpus of manually selected "good"hand poses, as well a large corpus of videos of continuous signing which are annotated for the sequence of signs appearing in them. Most sign languages, however, face a dearth in data, especially annotated data, and this is the case for indian Sign Language (ISL). In this paper, we present a method for overcoming this issue in the first GAN based SLS model created specifically for ISL. We use a combination of separate generators for the hand and body to overcome the problem of requiring hand-picked "good"hand images from training videos. We further refine the output with another network to remove the artefacts appearing due to combining separate GAN outputs. We also experiment with creating continuous sign language output without requiring an annotated corpus, by stitching together individual signs obtained from a publicly available video lexicon of ISL. We show our model performs competitively in these tasks in both quantitative measures as well as in human perception tests. © 2021 ACM.

关键词： Generative adversarial networks

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MBC-CA: Multithreshold binary conversion based salt-and-pepper noise removal using cellular automata 4th

MBC-CA: Multithreshold binary conversion based salt-and-pepp...

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

作者： Kumar, Parveen Ansari, Mohd Haroon Sharma, Ambalika Indian Institute of Technology Roorkee India National Institute of Technology Uttarakhand Srinagar Garhwal India Indian Institute of Science BengaluruKarnataka India

ISBN: (纸本)9789811540141

the salt-and-pepper noise, one of the forms of impulse noise, is one of the important problems that needs to be taken care of. Salt-and-pepper noise in the images are introduced during their acquisition, recording and transmitting. Cellular Automata (CA) is an emerging concept in the field of image processing due to its neighborhood dependence. Various methods have been proposed using CA for noise removal, simply due to the high complexity of CA, most of them are proven to be inefficient. However, CA can be used efficiently with some modifications that result in a reduction in its complexity, for the large number of image processing techniques. In this paper, we overcome the problem of CA by Multithreshold Binary Conversion (MBC) in which we convert the grayscale images to binary images based upon a chosen set of threshold values, reducing the state from 256 to 2 for every pixel. the resulting images are then fed to the CA. the result obtained is a set of binary images and these binary images need to be recombined to obtain a noise free grayscale image. We have used a method similar to a binary search that reduce the complexity of recombining the images from N2K to N2logK making our recombination algorithm an efficient algorithm, in terms of complexity, to recombine binary images to a single grayscale image. this reduction in the complexity of noise removal has no effect on the quality of a grayscale image. © Springer Nature Singapore Pte Ltd 2020.

关键词： Salt and pepper noise

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Fast semantic feature extraction using superpixels for soft segmentation 4th

Fast semantic feature extraction using superpixels for soft ...

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

作者： Verma, Shashikant Nagar, Rajendra Raman, Shanmuganathan Indian Institute of Technology Gandhinagar GandhinagarGujarat India

ISBN: (纸本)9789811540141

In this work, we address the problem of extracting high dimensional, soft semantic feature descriptors for every pixel in an image using a deep learning framework. Existing methods rely on a metric learning objective called multi-class N-pair loss, which requires pairwise comparison of positive examples (same class pixels) to all negative examples (different class pixels). Computing this loss for all possible pixel pairs in an image leads to a high computational bottleneck. We show that this huge computational overhead can be reduced by learning this metric based on superpixels. this also conserves the global semantic context of the image, which is lost in pixel-wise computation because of the sampling to reduce comparisons. We design an end-to-end trainable network with a loss function and give a detailed comparison of two feature extraction methods: pixel-based and superpixel-based. We also investigate hard semantic labeling of these soft semantic feature descriptors. © Springer Nature Singapore Pte Ltd 2020.

关键词： Superpixels

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A high precision rotating line detection method for the rotation angle measurement based on machine vision 4

A high precision rotating line detection method for the rota...

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4th International conference on computer graphics and Digital image processing, CGDIP 2020

作者： Cheng, Hao Cai, Chenguang Wang, Ying Liu, Zhihua Yang, Ming College of Information Science and Technology Beijing University of Chemical Technology No. 15 Bei San Huan Dong Lu Beijing China National Institute of Metrology No.18 Bei San Huan Dong Lu Beijing China Institute of Microelectronics of the Chinese Academy of Sciences NO.3 Beitucheng West Road Beijing China Yuquan Road Beijing China

the accuracy of the rotation angle measurement method based on machine vision with the line as the reference target is limited by line detection accuracy. the Line Segment Detector (LSD) method is a high precision and effective method, but there are detected false lines and discontinuous lines when the LSD method is actually used to detect the rotating lines in the images. In this paper, we adjust three parts of the LSD method: image scaling, gradient magnitude threshold, and aligned points density threshold to detect lines accurately. the high precision detection of the rotating line is obtained by our adjusted method. Besides, the rotation angle measurement accuracy with the lines detected by our adjusted LSD reaches the measurement error level of arcseconds, which proves its effective performance. © Published under licence by IOP Publishing Ltd.

关键词： computer vision

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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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