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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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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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Heritage Representation of Kashi Vishweshwar Temple at Kalabgoor, Telangana with Augmented Reality Application Using Photogrammetry 12th

Heritage Representation of Kashi Vishweshwar Temple at Kalab...

引用

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

作者： Pawar, Tejas Sharma, Aman Ji, Shiva Indian Institute of Technology Hyderabad Telangana Sangareddy Kandi502285 India

ISBN: (纸本)9789811941351

Augmented reality (AR) is emerging rapidly in the field of Heritage Interpretation due to its feasible availability and accessibility. Heritage tourism is more concerned about the visitor experience at Historical sites, and AR can enhance this experience. the enhancement of tourist experience and better interpretations comes from better documentation. the advent of technology calls for user-friendly and cost-effective tools for the documentation of heritage structures. Photogrammetry helps people recreate three-dimensional (3D) virtual models with fewer resources and lesser time and high-resolution textures. AR representation of buildings is highly efficient to showcase conjectural models where there is a lack of structural evidence. Instead of actual reconstruction, the use of AR preserves the authenticity and integrity of the structure. this paper focuses on creating a minimum file size 3D model with the digital data recorded from Kashi Vishweshwar Temple, Kalabgoor, Telangana, India. It is a High-Resolution Photogrammetry model which is integrated into Augmented Reality. the working on Interactive Features, Accuracy of Floor Tracking, and image Tracking are also explored in this analysis. © 2022, the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Augmented reality

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Model Compression Based Lightweight Online Signature Verification Framework 12th

Model Compression Based Lightweight Online Signature Verific...

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

作者： Vorugunti, Chandra Sekhar Balasubramanian, S. Viswanath, Pulabaigari Gautam, Avinash Indian Institute of Information Technology Sri City India Sri Sathya Sai Institute of Higher Learning Anantapur India Birla Institute of Technology & Science Pilani India

ISBN: (纸本)9789811941351

Advances in networking and digital technologies have led to the widespread usage of Online Signature Verification (OSV) frameworks in real-time settings to validate a user's identity. Because of the superior performance of Deep Learning frameworks, CNN-based models have been widely used for solving difficult computer vision tasks such as Object Detection, Object Segmentation, and so on. the biggest impediment to OSV adoption of CNN-based models is the growing size of CNN models. this prohibits OSV frameworks from being widely used in devices with minimal computational resources, such as mobile/ embedded devices. the newly popular topic of CNN model pruning aims to solve this problem by deleting filters and neurons that do not contribute to the model's learning. Optimal CNN-based OSV frameworks are obtained by removing the less important filters and neurons and fine-tuning the pruned networks. In line with this, we offer a novel light weight OSV architecture in which a detailed ablation research is performed to examine the contribution of each layer, and non-contributive layers are deleted based on the analysis. As a result, ideal low weight models with improved classification accuracies and the ability to be applied in real-time devices emerge. Our model's performance is thoroughly tested on three commonly used datasets: MCYT-100 (DB1), SVC, and SUSIG. In MCYT-100, SVC, and SUSIG datasets, the pruned model achieves a state-of-the-art EER of 7.98%, 3.65%, and 12.39% in the skilled-1 group, respectively. the efficiency of pruning-based OSV frameworks has been demonstrated in experiments. © 2022, the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Object detection

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A novel bidirectional framework for control and refinement of area based correlation techniques

A novel bidirectional framework for control and refinement o...

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International conference on Pattern Recognition

作者： P. Werth S. Scherer Computer Graphics and Vision Graz University of Technology Graz Austria Erich-Schmid-Institute for Materials Science Austrian Academy of Sciences Leoben Austria

ISBN: (纸本)0769507506

A key issue in performing stereo reconstruction is to find stereo correspondences. For this purpose a novel unified framework for arbitrary area based correlation methods is presented. the commonly neglected problem of ambiguities is addressed and a solution based on relaxation of match candidates is introduced. the method comes with the major benefits of a reverse matching step at almost no extra computational cost. the results of one match direction is used to select match candidates. then these candidates are correlated backward with a different method. the proposed framework does not require any initial estimations. Experiments are performed on ground truth under varying geometric and varying noise conditions. Finally, the method is compared to a number of well known techniques.

关键词： Stereo image processing Correlation image matching Noise robustness Materials science and technology image reconstruction Computational efficiency Stereo vision computer graphics Computational complexity

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Recognizing human activities in videos using improved dense trajectories over LSTM 6th

Recognizing human activities in videos using improved dense ...

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6th National conference on computer vision, Pattern Recognition, image processing and graphics, NCVPRIPG 2017

作者： Singh, Krit Karan Mukherjee, Snehasis Indian Institute of Information Technology SriCity Sri City India

ISBN: (纸本)9789811300196

We propose a deep learning based technique to classify actions based on Long Short Term Memory (LSTM) networks. the proposed scheme first learns spatial temporal features from the video, using an extension of the Convolutional Neural Networks (CNN) to 3D. A Recurrent Neural Network (RNN) is then trained to classify each sequence considering the temporal evolution of the learned features for each time step. Experimental results on the CMU MoCap, UCF 101, Hollywood 2 dataset show the efficacy of the proposed approach. We extend the proposed framework with an efficient motion feature, to enable handling significant camera motion. the proposed approach outperforms the existing deep models for each dataset. © Springer Nature Singapore Pte Ltd. 2018.

关键词： Long short-term memory

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SSIM-based joint bit-allocation using frame model parameters for 3D video coding 6th

SSIM-based joint bit-allocation using frame model parameters...

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6th National conference on computer vision, Pattern Recognition, image processing and graphics, NCVPRIPG 2017

作者： Harshalatha, Y. Biswas, Prabir Kumar Indian Institute of Technology Kharagpur Kharagpur India

ISBN: (纸本)9789811300196

Optimum bit-allocation between texture video and depth map in 3D video results in better virtual view quality. To incorporate this, rate distortion optimization (RDO) property is used. the RDO in 3D video implies minimization of synthesis distortion at available rate. Several bit-allocation methods proposed in literature have not considered perceptual quality improvement. In this paper, we propose bit-allocation criteria that results in better visual quality of synthesized view. To achieve this, visual quality metrics are to be incorporated and structural similarity (SSIM) index is one of the metric that measures perceived quality. As SSIM gives similarity measure, we used dSSIM as distortion metric in mode decision and motion estimation instead of traditional metrics like mean square error (MSE) or sum of squared error (SSE). Synthesis distortion is modeled using dSSIM and joint bit-allocation is formulated as optimization problem that is solved using Lagrange multiplier method. Model parameters are determined at frame level for more accurate calculation of quantization parameters. BD-Rate evaluation shows a reduction in bit rate with improved SSIM. © Springer Nature Singapore Pte Ltd. 2018.

关键词： Motion estimation

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Detecting missed and anomalous action segments using approximate string matching algorithm 6th

Detecting missed and anomalous action segments using approxi...

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6th National conference on computer vision, Pattern Recognition, image processing and graphics, NCVPRIPG 2017

作者： Jain, Hiteshi Harit, Gaurav Indian Institute of Technology Jodhpur Jodhpur India

ISBN: (纸本)9789811300196

We forget action steps and perform some unwanted action movements as amateur performers during our daily exercise routine, dance performances, etc. To improve our proficiency, it is important that we get a feedback on our performances in terms of where we went wrong. In this paper, we propose a framework for analyzing and issuing reports of action segments that were missed or anomalously performed. this involves comparing the performed sequence with the standard action sequence and notifying when misalignments occur. We propose an exemplar based Approximate String Matching (ASM) technique for detecting such anomalous and missing segments in action sequences. We compare the results with those obtained from the conventional Dynamic Time Warping (DTW) algorithm for sequence alignment. It is seen that the alignment of the action sequences under conventional DTW fails in the presence of missed action segments and anomalous segments due to its boundary condition constraints. the performance of the two techniques has been tested on a complex aperiodic human action dataset with Warm up exercise sequences that we developed from correct and incorrect executions by multiple people. the proposed ASM technique shows promising alignment and missed/anomalous notification results over this dataset. © Springer Nature Singapore Pte Ltd. 2018.

关键词： String searching algorithms

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Unsupervised single-view depth estimation for real time inference 4th

Unsupervised single-view depth estimation for real time infe...

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

作者： Siddiqui, Mohammed Arshad Jain, Arpit Gour, Neha Khanna, Pritee PDPM Indian Institute of Information Technology Design and Manufacturing Jabalpur India

ISBN: (纸本)9789811540141

Several approaches using unsupervised methods have been proposed recently to perform the task of depth prediction with higher accuracy. However, none of these approaches are flexible enough to be deployed in the real-time environment with limited computational capabilities. Inference latency is a major factor that limits the application of such methods to the real world scenarios where high end GPUs cannot be deployed. Six models based on three approaches are proposed in this work to reduce inference latency of depth prediction solutions without losing accuracy. the proposed solutions can be deployed in real-world applications with limited computational power and memory. the new models are also compared with the models recently proposed in literature to establish a state of the art depth prediction model that can be used in real-time. © Springer Nature Singapore Pte Ltd 2020.

关键词： Forecasting

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MoMSGAN: Mode Collapse based Degradation Agnostic Multi-Scale Super-Resolution of Medical images 14

MoMSGAN: Mode Collapse based Degradation Agnostic Multi-Scal...

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

作者： Dey, Samiran Chakraborti, Tapabrata Basuchowdhuri, Partha Mitra, Debasis Augustine, Robin Saha, Sanjoy Kumar Indian Association for the Cultivation of Science India The Alan Turing Institute University College London United Kingdom Indian Institute of Engineering Science and Technology India Uppsala University Sweden Jadavpur University India

ISBN: (纸本)9798400716256

Existing super-resolution (SR) models require separate training for different scales of SR because of the fixed upsampling levels in their architecture. We propose a novel one-time training approach for multi-scale SR models with a fixed upsampling unit, that can effectively super-resolve images to various scales. Another persisting problem relevant to medical image SR is that the models are biased to the level of degradation present in the low-resolution (LR) images and generate variations in anatomical structures and pathologies when applied to LR images corresponding to the same high-resolution (HR) image, varying only in degradation. But regardless of the degradation in the LR images, the anatomical structures and clinical findings in the SR images should remain consistent and unique to the patient's specific anatomy and health conditions. We thus propose a novel approach for GANs to blindly constrain the variations in super-resolved medical images. Further, we introduce an ensemble loss function to gain details and accurate textures, while enabling multi-scale SR and making the model unbiased towards degradation in LR images. Experimental results on lung CT scans demonstrate that our model, MoMSGAN, is an efficient one-time training approach for multi-scale SR and is also capable of reducing the degradation bias in reproducing accurate information in medical images, thus establishing state-of-the-art. © 2023 Copyright held by the owner/author(s). Publication rights licensed to ACM.

关键词： computerized tomography

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