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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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Quaternion Factorized Simulated Exposure Fusion 22

Quaternion Factorized Simulated Exposure Fusion

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

作者： Saurabh Saini P. J. Narayanan CVIT IIIT-Hyderabad India

ISBN: (纸本)9781450398220

image Fusion maximizes the visual information at each pixel location by merging content from multiple images in order to produce an enhanced image. Exposure Fusion, specifically, fuses a bracketed exposure stack of poorly lit images to generate a properly illuminated image. Given a single input image, exposure fusion can still be employed on a ‘simulated’ exposure stack, leading to direct single image contrast and low-light enhancement. In this work, we present a novel ‘Quaternion Factorized Simulated Exposure Fusion’ (QFSEF) method by factorizing an input image into multiple illumination consistent layers. To this end, we use an iterative sparse matrix factorization scheme by representing the image as a two-dimensional pure quaternion matrix. Theoretically, our representation is based on the dichromatic reflection model and accounts for the two scene illumination characteristics by factorizing each progressively generated image into separate specular and diffuse components. We empirically prove the advantages of our factorization scheme over other exposure simulation methods by using it for the low-light image enhancement task. Furthermore, we provide three exposure fusion strategies which can be used with our simulated stack and provide a comprehensive performance analysis. Finally, in order to validate our claims, we show extensive qualitative and quantitative comparisons against relevant state-of-the-art solutions on multiple standard datasets along with relevant ablation analysis to support our proposition. Our code and data are publicly available for easy reproducibility and reference. 1

关键词： Exposure Fusion Quaternion image Representation Low Light image Enhancement Robust Principal Component Analysis

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Interpreting Intrinsic image Decomposition using Concept Activations 22

Interpreting Intrinsic Image Decomposition using Concept Act...

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

作者： Avani Gupta Saurabh Saini P. J. Narayanan CVIT KCIS International Institute of Information Technology IN

ISBN: (纸本)9781450398220

Evaluation of ill-posed problems like Intrinsic image Decomposition (IID) is challenging. IID involves decomposing an image into its constituent illumination-invariant Reflectance (R) and albedo-invariant Shading (S) components. Contemporary IID methods use Deep Learning models and require large datasets for training. The evaluation of IID is carried out on either synthetic Ground Truth images or sparsely annotated natural images. A scene can be split into reflectance and shading in multiple, valid ways. Comparison with one specific decomposition in the ground-truth images used by current IID evaluation metrics like LMSE, MSE, DSSIM, WHDR, SAW AP%, etc., is inadequate. Measuring R-S disentanglement is a better way to evaluate the quality of IID. Inspired by ML interpretability methods, we propose Concept Sensitivity Metrics (CSM) that directly measure disentanglement using sensitivity to relevant concepts. Activation vectors for albedo invariance and illumination invariance concepts are used for the IID problem. We evaluate and interpret three recent IID methods on our synthetic benchmark of controlled albedo and illumination invariance sets. We also compare our disentanglement score with existing IID evaluation metrics on both natural and synthetic scenes and report our observations. Our code and data are publicly available for reproducibility 1.

关键词： ML interpretability evaluation techniques ill-posed problems Intrinsic image Decomposition Disentanglement TCAV

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A Fine-Grained Vehicle Detection (FGVD) Dataset for Unconstrained Roads✱ 22

A Fine-Grained Vehicle Detection (FGVD) Dataset for Unconstr...

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

作者： Prafful Kumar Khoba Chirag Parikh C.V. Jawahar Ravi Kiran Sarvadevabhatla Rohit Saluja Indian Institute of Technology Delhi IN IIIT Hyderabad IN IIT Mandi IN

ISBN: (纸本)9781450398220

The previous fine-grained datasets mainly focus on classification and are often captured in a controlled setup, with the camera focusing on the objects. We introduce the first Fine-Grained Vehicle Detection (FGVD) dataset in the wild, captured from a moving camera mounted on a car. It contains 5502 scene images with 210 unique fine-grained labels of multiple vehicle types organized in a three-level hierarchy. While previous classification datasets also include makes for different kinds of cars, the FGVD dataset introduces new class labels for categorizing two-wheelers, autorickshaws, and trucks. The FGVD dataset is challenging as it has vehicles in complex traffic scenarios with intra-class and inter-class variations in types, scale, pose, occlusion, and lighting conditions. The current object detectors like yolov5 and faster RCNN perform poorly on our dataset due to a lack of hierarchical modeling. Along with providing baseline results for existing object detectors on FGVD Dataset, we also present the results of a combination of an existing detector and the recent Hierarchical Residual Network (HRN) classifier for the FGVD task. Finally, we show that FGVD vehicle images are the most challenging to classify among the fine-grained datasets. [GitHub]

关键词： dataset detection gradcam. Fine-grained unconstrained roads

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G-PReDICT: Generalizable Person Re-ID using Domain Invariant Contrastive Techniques✱ 22

G-PReDICT: Generalizable Person Re-ID using Domain Invariant...

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

作者： Chaitra Jambigi Umar Masud Anirban Chakraborty Department of Computational and Data Sciences Indian Institute of Science IN Jamia Millia Islamia IN and Department of Computational and Data Sciences Indian Institute of Science India

ISBN: (纸本)9781450398220

Learning identity-aware, domain-invariant representations is crucial in solving domain generalizable person ReID (DG-ReID). Existing methods commonly use augmentation techniques either in feature space by mixing instance and batch normalization layers or in pixel space by adversarially generating pseudo domains. However, neither of these techniques guarantee identity preservation. Apart from increasing training data diversity, the augmented positive pairs also encode rich semantic relations which have not been fully explored. To address the above issues, we propose a novel framework for Generalizable Person Re-identification using Domain Invariant Contrastive Techniques (G-PReDICT). Specifically, we use simple yet effective perturbation strategies to hallucinate positive samples across domains by realistically modelling domain variations while preserving the target identities. We harness rich sample-sample relations between the hallucinated positive-negative pairs to learn domain-invariant representations using supervised contrastive learning. We also use a domain independent auxiliary task, i.e. attribute prediction to learn robust representations and introduce attribute annotations for two large scale public benchmarks i.e. CUHK-03 and MSMT17. Extensive experiments on standard benchmarks demonstrate the effectiveness of the proposed method.

关键词： Deep learning

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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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