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

共 1497 条记录，以下是221-230 订阅

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HDR-cGAN: Single LDR to HDR image translation using conditional GAN 21

HDR-cGAN: Single LDR to HDR image translation using conditio...

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

作者： Raipurkar, Prarabdh Pal, Rohil Raman, Shanmuganathan CVIG Lab IIT Gandhinagar Gujarat India

ISBN: (纸本)9781450391276

The prime goal of digital imaging techniques is to reproduce the realistic appearance of a scene. Low Dynamic Range (LDR) cameras are incapable of representing the wide dynamic range of the real-world scene. The captured images turn out to be either too dark (underexposed) or too bright (overexposed). Specifically, saturation in overexposed regions makes the task of reconstructing a High Dynamic Range (HDR) image from single LDR image challenging. In this paper, we propose a deep learning based approach to recover details in the saturated areas while reconstructing the HDR image. We formulate this problem as an image-to-image (I2I) translation task. To this end, we present a novel conditional GAN (cGAN) based framework trained in an end-to-end fashion over the HDR-REAL and HDR-SYNTH datasets. Our framework uses an overexposed mask obtained from a pre-trained segmentation model to facilitate the hallucination task of adding details in the saturated regions. We demonstrate the effectiveness of the proposed method by performing an extensive quantitative and qualitative comparison with several state-of-the-art single-image HDR reconstruction techniques. © 2021 ACM.

关键词： Generative adversarial networks

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A novel unsupervised thresholding technique for landsat image change detection 21

A novel unsupervised thresholding technique for landsat imag...

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

作者： Gupta, Neha Ari, Samit Mishra, Anoop Kumar VIT-AP University Andhra Pradesh Amaravati India National Institute of Technology Odisha Rourkela India

ISBN: (纸本)9781450391276

Thresholding is the most widely used change detection technique for identifying the changes in remote sensing images. However, most of the thresholding methods would generate isolated spots in the final change map, which are reduced by applying postprocessing step. This paper proposes a novel thresholding technique to address the aforesaid problem without applying the postprocessisng operation. The proposed technique uses two thresholds simultaneously to generate the change map, which is the key point of this method. Above-stated thresholds are calculated by initial indicators that are generated by local neighborhood mutual information. In addition, this approach compares the local statistics of the interested pixel with the derived thresholds instead of the pixel itself, which enhances the robustness of this technique. Particularly, the method resides on three things: generation of initial indicators, use of two thresholds simultaneously, and comparison of local statistics with thresholds. Finally, the method is tested on multitemporal multispectral images of different sensors, and experimental results validate the effectiveness of the proposed method. © 2021 ACM.

关键词： K-means clustering

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Contrastive Multi-View Textual-Visual Encoding: Towards One Hundred Thousand-Scale One-Shot Logo Identification✱ 22

Contrastive Multi-View Textual-Visual Encoding: Towards One ...

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

作者： Nakul Sharma Abhirama Subramanyam V B Penamakuri Anand Mishra Indian Institute of Technology Jodhpur IN Indian Institute of Technology Jodhpur IN

ISBN: (纸本)9781450398220

In this paper, we study the problem of identifying logos of business brands in natural scenes in an open-set one-shot setting. This problem setup is significantly more challenging than traditionally-studied ‘closed-set’ and ‘large-scale training samples per category’ logo recognition settings. We propose a novel multi-view textual-visual encoding framework that encodes text appearing in the logos as well as the graphical design of the logos to learn robust contrastive representations. These representations are jointly learned for multiple views of logos over a batch and thereby they generalize well to unseen logos. We evaluate our proposed framework for cropped logo verification, cropped logo identification, and end-to-end logo identification in natural scene tasks; and compare it against state-of-the-art methods. Further, the literature lacks a ‘very-large-scale’ collection of reference logo images that can facilitate the study of one-hundred thousand-scale logo identification. To fill this gap in the literature, we introduce Wikidata Reference Logo Dataset (WiRLD), containing logos for 100K business brands harvested from Wikidata. Our proposed framework that achieves an area under the ROC curve of 91.3% on the QMUL-OpenLogo dataset for the verification task, outperforms state-of-the-art methods by 9.1% and 2.6% on the one-shot logo identification task on the Toplogos-10 and the FlickrLogos32 datasets, respectively. Further, we show that our method is more stable compared to other baselines even when the number of candidate logos is on a 100K scale.

关键词： logo identification. open-set recognition one-shot learning supervised contrastive learning

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Disparity based depth estimation using light field camera 21

Disparity based depth estimation using light field camera

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

作者： Nehra, Suresh Das, Tamal Chakraborty, Simantini Biswas, P.K. Mukhopadhyay, J. Indian Institute of Technology Kharagpur India National Institute of Technology Agartala India

ISBN: (纸本)9781450391276

Light field cameras have a unique feature of capturing the direction of light rays along with its intensity. This additional information is used to estimate the depth of a 3-D scene. Recently a considerable amount of research has been done in depth estimation using light field data. However, these depth estimation methods heavily rely on iterative optimization techniques and do not fully exploit the inherent structured light field data. In this paper, we present a novel three-step disparity based algorithm to estimate accurate depth maps of a light field image. First, an initial depth map of scene points is estimated using principal views by estimating the disparity of segments in the central image. This initial depth helps in resolving ambiguity in depth propagation of refined depth map in Epi-Polar line images (EPIs). Second, refined depth is estimated at lines using the disparity vector in EPIs. Finally, refined depth at lines in EPIs is propagated to other locations using the initial depth map. We also provided a synthetic data-set having inherent characteristic of a light field. We have tested our approach on a variety of real-world scenes captured with Lytro Illum camera and also on synthetic images. The proposed method outperforms several state-of-the-art algorithms. © 2021 ACM.

关键词： Cameras

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An encoder-decoder based deep architecture for visible to near infrared image transformation 21

An encoder-decoder based deep architecture for visible to ne...

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

作者： Aswatha, Shashaank M. Malladi, Sai Phani Kumar Mukherjee, Jayanta Philips Benguluru India IIT Kharagpur India

ISBN: (纸本)9781450391276

Near-infrared (NIR) band is also one of the exciting wavelength ranges, besides optical range, to the human visual perception system. The NIR band shares various attributes with visible spectrum range. NIR images have the potential for better representation of vegetation and certain details of a scene that are sometimes opaque to RGB wavelengths. In this paper, we propose a dual encoder-decoder based architecture with different depths for transforming RGB images to NIR images. This network handles scale variations in data and helps to reduce artifacts in the transformed image domain. Two different resolutions of the input image are subjected to feature extraction by a series of five consecutive convolution and ReLu layers. Then, their corresponding aggregated features are assembled and up-sampled to regress over the ground truth NIR image. We consider two datasets with different resolutions and properties, EPFL and OMSIV, which provide corresponding RGB-NIR pairs of images of natural scenes. We train and test our network on both the datasets and evaluate its qualitative and quantitative performance. The results of our network in transforming RGB images to NIR images are found to be interesting which are demonstrated through various examples. © 2021 ACM.

关键词： Infrared devices

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Cross-view kernel similarity metric learning using pairwise constraints for person re-identification 21

Cross-view kernel similarity metric learning using pairwise ...

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

作者： Ali, T.M. Feroz Chaudhuri, Subhasis Indian Institute of Technology Bombay Maharashtra Mumbai India

ISBN: (纸本)9781450391276

Person re-identification is the task of matching pedestrian images across non-overlapping cameras. In this paper, we propose an efficient kernel based similarity metric learning for learning non-linear features using small scale training data for practical person re-ID systems. The method employs non-linear mappings combined with cross-view discriminative subspace learning and cross-view distance metric learning based on pairwise similarity constraints. It is a natural extension of Cross-view Quadratic Discriminant Analysis (XQDA) from linear to non-linear model using kernels. In addition to outperforming XQDA, the proposed method is computationally very efficient compared to its baselines. Extensive experiments on four benchmark datasets show that our method attains competitive performance against state-of-the-art methods. Our code is available at https://***/ferozalitm/Efficient-Kernel-XQDA. © 2021 ACM.

关键词： Benchmarking

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Deformable deep networks for instance segmentation of overlapping multi page handwritten documents 21

Deformable deep networks for instance segmentation of overla...

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

作者： Aitha, Sowmya Bollampalli, Sindhu Sarvadevabhatla, Ravi Kiran CVIT IIIT Hyderabad Hyderabad500032 India

ISBN: (纸本)9781450391276

Digitizing via scanning the physical artifact often forms the first primary step in preserving historical handwritten manuscripts. To maximally utilize scanner surface area and minimize manual labor, multiple manuscripts are usually scanned together into a scanned image. Therefore, the first crucial task in manuscript content understanding is to ensure that each of the individual manuscripts within a scanned image can be isolated (segmented) on a per-instance basis. Existing deep network based approaches for manuscript layout understanding implicitly assume a single or two manuscripts per image. Since this assumption may be routinely violated, there is a need for a precursor system which extracts individual manuscripts before downstream processing. Another challenge is the highly curved and deformed boundaries of manuscripts, causing them to often overlap with each other. To tackle such challenges, we introduce a new document image dataset called IMMI (Indic Multi Manuscript images). To improve the efficiency of dataset and aid deep network training, we also propose an approach which generates synthetic images to augment sourced non-synthetic images. We conduct experiments using modified versions of existing document instance segmentation frameworks. The results demonstrate the efficacy of the new frameworks for the task. Overall, our contributions enable robust extraction of individual historical manuscript pages. This in turn, could potentially enable better performance on downstream tasks such as region-level instance segmentation within handwritten manuscripts and optical character recognition. © 2021 ACM.

关键词： image enhancement

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Enhancing label transfer in non-parametric scene parsing by superpixel-based dense alignment 21

Enhancing label transfer in non-parametric scene parsing by ...

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

作者： Bhowmick, Alexy Saharia, Sarat Hazarika, Shyamanta M. Dept. of Computer Science and Engineering Assam Don Bosco University Assam Guwahati781017 India Dept. of Computer Science and Engineering Tezpur University Assam Tezpur784028 India Biomimetic Robotics and Artificial Intelligence Lab Dept. of Mechanical Engineering IIT Guwahati Assam Guwahati781039 India

ISBN: (纸本)9781450391276

Contemporary (parametric) scene parsing methods are learning-based and mostly operate in a closed-universe scenario. We introduce a non-parametric scene parsing framework that is model-free, data-driven, and scales naturally to growing data. The scene parsing performance in the non-parametric approach depends on reliable dense correspondence or alignment across scenes for label transfer. Incorrect correspondence is known to affect the scene parsing results adversely. We propose a label transfer approach that relies on the dense correspondence of super-pixel pairs (in a query and candidate image) matched by a homogeneous kernel map to guide semantic label transfer. The aggregation (fusing) of multiple labels is done through a simple heuristic aggregation scheme (simple majority voting). The Markov Random Field (MRF) provides a principled probabilistic framework for combining the disparate information in the smoothing stage and ensures plausible labeling results. Evaluation results show that our non-parametric system obtains competitive scene parsing performance on the standard SIFT Flow and MSRC-21 datasets. © 2021 ACM.

关键词： Superpixels

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Chart classification: An empirical comparative study of different learning models 21

Chart classification: An empirical comparative study of diff...

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

作者： Thiyam, Jennil Singh, Sanasam Ranbir Bora, Prabin K. IIT Guwahati India

ISBN: (纸本)9781450391276

Charts are powerful tools for visualizing and comparing data. Representation of information through charts grows with time due to its easy and aesthetically attractive structure. With the increase in the number of documents with various chart types, chart classification has become an important task for downstream applications such as chart data recovery, chart replenishment, etc. Though there have been various studies reported in the literature on chart classification using different classification methods, three of the important concerns are small dataset size, a small number of chart types, and inconsistencies in the performance reported in different studies. Motivated by the above concerns, this paper curates a large dataset of real chart images (110k samples) with a large number of chart types (24 charts types) and evaluates 21 different machine learning models. To the best of our knowledge, this is the largest (in sample size and chart types) real chart dataset reported in the literature to date. We further study - (i) the effect of dataset size on the classification model, (ii) the nature of chart noises and their influences on classification performance, and (iii) confusing chart pairs leading to misclassification. © 2021 ACM.

关键词： image classification

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Feature generation for long-tail classification 21

Feature generation for long-tail classification

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

作者： Vigneswaran, Rahul Law, Marc T. Balasubramanian, Vineeth N. Tapaswi, Makarand Indian Institute of Technology Hyderabad India NVIDIA IIIT Hyderabad India

ISBN: (纸本)9781450391276

The visual world naturally exhibits an imbalance in the number of object or scene instances resulting in a long-tailed distribution. This imbalance poses significant challenges for classification models based on deep learning. Oversampling instances of the tail classes attempts to solve this imbalance. However, the limited visual diversity results in a network with poor representation ability. A simple counter to this is decoupling the representation and classifier networks and using oversampling only to train the classifier. In this paper, instead of repeatedly re-sampling the same image (and thereby features), we explore a direction that attempts to generate meaningful features by estimating the tail category's distribution. Inspired by ideas from recent work on few-shot learning [53], we create calibrated distributions to sample additional features that are subsequently used to train the classifier. Through several experiments on the CIFAR-100-LT (long-tail) dataset with varying imbalance factors and on mini-imageNet-LT (long-tail), we show the efficacy of our approach and establish a new state-of-the-art. We also present a qualitative analysis of generated features using t-SNE visualizations and analyze the nearest neighbors used to calibrate the tail class distributions. Our code is available at https://***/rahulvigneswaran/TailCalibX. © 2021 ACM.

关键词： Deep learning

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