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

共 1468 条记录，以下是221-230 订阅

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Evolution of Bagbazar Street Through Visibility Graph Analysis (1746–2020) 12th

Evolution of Bagbazar Street Through Visibility Graph Analys...

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

作者： Chakraborty, Shilpi Ji, Shiva Department of Design Indian Institute of Technology Hyderabad Telangana Sangareddy Kandi502284 India

ISBN: (纸本)9789811941351

Activities intended at increasing the accessibility of offers connected with urban growth and efforts and pragmatic practices targeted at boosting community contact through the usage of common areas contribute to cultural integration in the urban context. Bag Bazar Street is one of Kolkata’s three main streets, known as the grandmother road in ekantoporibar (joint family), making it an excellent case study site. By developing and assessing ideas regarding urban mobility characteristics and linkages across time, syntactic space analyses aid in the examination of diverse historical sources to build a coherent narrative of urban topology configuration. In a quantitative study of urban space and space syntax, enticing prospects as an exciting development of spaces use the city plan as the initial stage for investigating the historical relationship between the urban setting and urban life. The significant features of the axial line are with utilizing the argumentation of extrinsic spatial features in space syntax. DepthMap, open-source software for studying the visibility of urban core, was used in this study. The utilization of an axial map with a connection map is to frame basic facts in the study. Any strategy to revival or smart city development necessitates a full grasp of the spatial factors and stages that characterize our urban communities, as well as the link between the two. Consequently, this research uses axial analysis and visibility graph analysis to validate the degree of functionality of Kolkata’s Bag Bazar street over time as an input for digital preservation. The paper identifies the period having transformation for each attribute. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Urban growth

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STTGC-Net: Spatial-Temporal Transformer with Graph Convolution for Skeleton-Based Action Recognition 23

STTGC-Net: Spatial-Temporal Transformer with Graph Convoluti...

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

作者： Tanishka Yagneshwar Snehasis Mukherjee Computer Science and Engineering Shiv Nadar Institute of Eminence IN Computer Science and Engineering Shiv Nadar Institute of Eminence Greater Noida India IN

ISBN: (纸本)9798400716256

Skeleton data plays an important role in human action recognition due to the compact and distinct information of human poses provided by the skeleton data. Skeleton-based action recognition is gaining interest due to the availability of Kinect cameras at a reasonable price. With the growing popularity of Geometric deep learning, Graph Convolutional networks (GCN) are extensively used for processing skeleton data, due to their ability to model data topology. It has been found that Spatial-Temporal Graph Convolutional Networks (ST-GCN) are efficient in learning both spatial and temporal dependencies on non-Euclidean data, such as skeleton graphs. However, the state-of-the-art ST-GCN models lack flexibility in feature extraction and do not explicitly consider the high-order Spatio-Temporal significance of the spatial connection topology and intensity of the joints. They lack an attention module that can help us learn together when and where to concentrate on a certain action in the action sequence. The transformer-based methods can effectively capture long-distance dependencies. However, using just a traditional transformer approach overlooks the graph structure in the data, which can provide valuable information about the inter-relationships among the joint points during actions. To address this problem, we propose an architecture named Spatial-Temporal Transformer Network with Graph Convolution (STTGC-Net), that can flexibly capture local and global contexts. Spatial-Temporal Graph Convolutional Networks and Spatial-Temporal Transformer Attention Modules are sequentially fused to create the proposed framework. The ST-GCNs capture temporal dynamics, hierarchy, and local topological information at several levels, and the transformer attention module displays the correlations between joint pairs in global topology via dynamic attention, which resolves the mentioned constraints of the GCN and transformer. We validate our model by conducting tests on the NTU 60 and NTU 12

关键词： Attention ST-GCN action recognition skeleton transformer

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TexRGAN: A deep adversarial framework for text restoration from deformed handwritten documents 21

TexRGAN: A deep adversarial framework for text restoration f...

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

作者： Poddar, Arnab Chakraborty, Akash Mukhopadhyay, Jayanta Biswas, Prabir Kumar Indian Institute of Technology Kharagpur India

ISBN: (纸本)9781450391276

Free form handwritten document images commonly contains deformed text images such as struck-out and underlined words. The deformed text images drastically degrades the performance of intensely used document processing applications like optical character recognition (OCR). Here we propose an end-to-end text image restoration system based on generative adversarial network (GAN). The proposed model TexRGAN is perhaps the first attempt to restore deformed handwritten texts like struck-out and underlined text images using GAN model and it simultaneously handles strikeout and underline words both with a single deep network model. The proposed GAN model uses spatial as well as structural loss to generate restored text images from a given deformed text image input as condition. The proposed network is trained in weakly supervised approach to avoid unavailability of training data and the cost and error of manual annotations. We evaluate the performance of the proposed TexRGAN on various types of deformation and shapes of strike-through-strokes such as slanted strokes, nearly straight strokes, multiple strokes, underlines, crossed strokes etc. The TexRGAN is also evaluated directly in terms the OCR performance. The evaluation metrics show robustness and applicability in real-world scenario. © 2021 ACM.

关键词： image reconstruction

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Abnormality detection and classification of macular diseases from optical coherence tomography images: Using feature space comparison 21

Abnormality detection and classification of macular diseases...

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

作者： Ashok, L.R. Sreeni, K.G. Computer Vision Lab Dept.Electronics and Communication College of Engineering Kerala Trivandrum India

ISBN: (纸本)9781450391276

Optical Coherence Tomography (OCT) is a non-invasive imaging technology for diagnosing various macular pathologies. It assists ophthalmologists to detect abnormalities in the retina and thereby avoid many sight-threatening conditions. However, manual detection process depends on the experience of the clinical practitioner and it is a time-consuming process. Automatic detection of abnormalities using computer algorithms allows the clinical practitioner to detect the abnormalities in a faster and accurate manner. Here, a Deep learning-based approach is proposed which can detect abnormal retinal images in various disease classes which are not limited to classes present in the training data-set. The performance of the Abnormality detector is tested on UCSD, Kaggle & OCTID data-sets and obtained an accuracy of 99.80%, 99.90% & 94.59%, respectively. The performance of Classifier is also tested on UCSD & Kaggle data-sets and obtained an accuracy of 97.7%, 99.79%, respectively. © 2021 ACM.

关键词： Optical tomography

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Iterative gradient encoding network using feature co-occurrence loss for single image reflection removal 21

Iterative gradient encoding network using feature co-occurre...

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

作者： Bera, Sutanu Biswas, Prabir Kumar Indian Institute of Technology Kharagpur Kharagpur India

ISBN: (纸本)9781450391276

Removing undesired reflections from a photo taken in front of a glass is of great importance for enhancing visual computing systems' efficiency. Previous learning-based approaches have produced visually plausible results for some reflections type, however, failed to generalize against other reflection types. There is a dearth of literature for efficient methods concerning single image reflection removal, which can generalize well in large-scale reflection type. In this study, we proposed an iterative gradient encoding network for single image reflection removal. Next, to further supervise the network in learning the correlation between the transmission layer features, we proposed a feature co-occurrence loss. Extensive experiments on the public benchmark dataset of SIR2 demonstrated that our method can remove reflection favorably against the existing state of the art method on all imaging settings, including diverse backgrounds. Moreover, as the reflection strength increases, our method can still remove reflection even where other state of the art methods failed. © 2021 ACM.

关键词： Iterative methods

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GPU-based centroidal voronoi tessellation using local search on thinnest digital surface 21

GPU-based centroidal voronoi tessellation using local search...

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

作者： Soni, Ashutosh Bhunre, Piyush Kanti Bhowmick, Partha Indian Institute of Technology Kharagpur India Techno India University Kolkata India

ISBN: (纸本)9781450391276

Voronoi tessellation is a classical geometric problem with different variants and nature of constraints. Centroidal Voronoi tessellation (CVT) is one such leading variant and used in many applications. Although there exists a multitude of CVT techniques for 3D objects considered as discrete volumes, e.g., as sets of voxels, there is no significant work in the literature on CVT computation for voxelized (a.k.a. 'digital') surfaces. In this paper we focus on this problem and propose a novel GPU-based algorithm for CVT computation on a digital surface. Its novelty rests on several fundamental ideas. Firstly, the digital surface is thinnest by construction;that is, while being voxelized from a triangulated surface, its every triangle is 2-minimal in topological sense. As a result, each voxel of the digital surface has the smallest possible neighborhood, which eventually limits the search space and aids in efficient computation. Secondly, as the optimization constraint, a novel formulation of Voronoi energy is used, which is easy to compute and hence leads to quick convergence of the algorithm. The GPU-based algorithm with related procedures and their complexity analysis have been discussed in detail, and related experimental results have also been furnished to adjudge the merit and usefulness of the proposed algorithm. © 2021 ACM.

关键词： graphics processing unit

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Interpretive self-supervised pre-training: Boosting performance on visual medical data 21

Interpretive self-supervised pre-training: Boosting performa...

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

作者： Manna, Siladittya Bhattacharya, Saumik Pal, Umapada Indian Statistical Institute Kolkata West Bengal Kolkata India Indian Institute of Technology West Bengal Kharagpur India

ISBN: (纸本)9781450391276

Self-supervised learning algorithms have become one of the best tools for unsupervised representation learning. Although self- supervised algorithms have achieved state-of-the-art performance for classification tasks in the case of natural image data, their application on medical data has been limited. In this work, we have proposed a novel loss function and derive it's asymptotic lower bound. We have also shown that self-supervised pre-training with the proposed loss function helps in surpassing the supervised baseline on the downstream task. We have also shown that the self-supervised pre-training helps a model in learning better representation in general to achieve better performance compared to supervised baselines. We have mathematically derived that the contrastive loss function asymptotically treats each sample as a separate class and works by maximizing the distance between any two samples and this helps to get better performance. Finally, through exhaustive experiments, we demonstrate that self-supervised pre-training helps to surpass the performance of fully supervised models on downstream tasks. © 2021 ACM.

关键词： Learning algorithms

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Deep Learning Assisted Retinopathy of Prematurity Screening Technique 14

Deep Learning Assisted Retinopathy of Prematurity Screening ...

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14th International Joint conference on Biomedical Engineering Systems and Technologies (BIOSTEC) / 14th Int Conf on Bio-inspired Systems and Signal processing (BIOSIGNALS) / 14th Int Conf on Biomedical Electronics and Devices (BIODEVICES)

作者： Kumar, Vijay Patel, Het Paul, Kolin Surve, Abhidnya Azad, Shorya Chawla, Rohan Indian Inst Technol Khosla Sch Informat Technol Delhi India All India Inst Med Sci Dr Rajendra Prasad Ctr Ophthalm Sci Delhi India

ISBN: (纸本)9789897584909

Retinopathy of Prematurity (ROP) is the leading cause of blindness in preterm babies worldwide. By using proper scanning and treatment, the effect of the blindness of ROP can be reduced. However, due to lack of medical facilities, a large proportion of these preterm infants remain undiagnosed after birth. As a result, these babies are more likely to have ROP induced blindness. In this paper, we propose a robust and intelligent system based on deep learning and computer vision to automatically detect the optical disk (OD) and retinal blood vessels and also classify the high severity (Zone-1) case of ROP. To test and validate the proposed system, we present empirical results using the preterm infant fundus images from a local hospital. Our results showed that the YOLO-V5 model accurately detects the OD from preterm babies fundus images. Further, the computer vision-based system accurately segmented the retinal vessels from the preterm babies fundus images. Specifically for the Zone-1 case of ROP, our system is able to achieve an accuracy of 83.3%.

关键词： Fundus image Retinopathy of Prematurity (ROP) Plus Disease computer Aided Diagnosis (CAD) image processing Machine Learning (ML) Deep Learning (DL)

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Domain Adaptation Based Technique for image Emotion Recognition Using image Captions 5th

Domain Adaptation Based Technique for Image Emotion Recognit...

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5th International conference on computer vision and image processing, CVIP 2020

作者： Kumar, Puneet Raman, Balasubramanian Department of Computer Science and Engineering Indian Institute of Technology Roorkee Roorkee India

ISBN: (纸本)9789811610912

images are powerful tools for affective content analysis. image emotion recognition is useful for graphics, gaming, animation, entertainment, and cinematography. In this paper, a technique for recognizing the emotions in images containing facial, non-facial, and non-human components has been proposed. The emotion-labeled images are mapped to their corresponding textual captions. Then the captions are used to re-train a text emotion recognition model as the domain-adaptation approach. The adapted text emotion recognition model has been used to classify the captions into discrete emotion classes. As image captions have a one-to-one mapping with the images, the emotion labels predicted for the captions have been considered the emotion labels of the images. The suitability of using the image captions for emotion classification has been evaluated using caption-evaluation metrics. The proposed approach serves as an example to address the unavailability of sufficient emotion-labeled image datasets and pre-trained models. It has demonstrated an accuracy of 59.17% for image emotion recognition. © 2021, Springer Nature Singapore Pte Ltd.

关键词： Emotion Recognition

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Magnetic resonance image reconstruction by nullspace based finite rate of innovation framework 21

Magnetic resonance image reconstruction by nullspace based f...

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

作者： Reddy, P. Sudhakar Raghavendra, B.S. Narasimhadhan, A.V. National Institute of Technology Karnataka Mangalore India

ISBN: (纸本)9781450391276

The finite rate of innovation (FRI) framework has proved that it is possible to reconstruct the analog signals which have a finite number of parameters. FRI framework is used to reconstruct the images from undersampled magnetic resonance (MR) data. The reconstruction of the MR image from the MR data is a estimation problem, which can be solved by utilizing Prony's method. However, Prony's method involves solving the polynomial roots of the annihilating filter and this fact leads to an unstable reconstruction in the high noise scenario. In this paper, we introduce a novel reconstruction approach is also based on the annihilating filter. However, it involves the use of solutions of an underdetermined linear system. The simulation results of the proposed reconstruction approach show that the peak signal to noise ratio (PSNR) and the structural similarity index measure (SSIM) are higher magnitude than that of conventional FRI methods in the high noise scenario.1 © 2021 ACM.

关键词： Magnetic resonance imaging

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