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检索条件"任意字段=15th Indian Conference on Computer Vision Graphics and Image Processing"

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Total cluster: A person agnostic clustering method for broadcast videos 14

Total cluster: A person agnostic clustering method for broad...

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

作者： Tapaswi, Makarand Parkhi, Omkar M. Rahtu, Esa Sommerlade, Eric Stiefelhagen, Rainer Zisserman, Andrew Computer Vision for Human Computer Interaction Karlsruhe Institute of Technology Germany Visual Geometry Group Department of Engineering Science University of Oxford United Kingdom Center for Machine Vision Research University of Oulu Finland

ISBN: (纸本)1595930361

the goal of this paper is unsupervised face clustering in edited video material - where face tracks arising from different people are assigned to separate clusters, with one cluster for each person. In particular we explore the extent to which faces can be clustered automatically without making an error. this is a very challenging problem given the variation in pose, lighting and expressions that can occur, and the similarities between different people. the novelty we bring is three fold: first, we show that a form of weak supervision is available from the editing structure of the material - the shots, threads and scenes that are standard in edited video;second, we show that by first clustering within scenes the number of face tracks can be significantly reduced with almost no errors;third, we propose an extension of the clustering method to entire episodes using exemplar SVMs based on the negative training data automatically harvested from the editing structure. the method is demonstrated on multiple episodes from two very different TV series, Scrubs and Buffy. For both series it is shown that we move towards our goal, and also outperform a number of baselines from previous works. Copyright is held by the authors.

关键词： Cluster analysis

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Detector-SegMentor Network for Skin Lesion Localization and Segmentation 7th

Detector-SegMentor Network for Skin Lesion Localization and ...

引用

7th National conference on computer vision, Pattern Recognition, image processing, and graphics, NCVPRIPG 2019

作者： Saini, S. Gupta, D. Tiwari, A.K. Department of Electrical Engineering Indian Institute of Technology Jodhpur Jodhpur India

ISBN: (纸本)9789811586965

Melanoma is a life-threatening form of skin cancer when left undiagnosed at the early stages. Although there are more cases of non-melanoma cancer than melanoma cancer, melanoma cancer is more deadly. Early detection of melanoma is crucial for the timely diagnosis of melanoma cancer and prohibit its spread to distant body parts. Segmentation of skin lesion is a crucial step in the classification of melanoma cancer from the cancerous lesions in dermoscopic images. Manual segmentation of dermoscopic skin images is very time consuming and error-prone resulting in an urgent need for an intelligent and accurate algorithm. In this study, we propose a simple yet novel network-in-network convolution neural network (CNN) based approach for segmentation of the skin lesion. A Faster Region-based CNN (Faster RCNN) is used for preprocessing to predict bounding boxes of the lesions in the whole image which are subsequently cropped and fed into the segmentation network to obtain the lesion mask. the segmentation network is a combination of the UNet and Hourglass networks. We trained and evaluated our models on ISIC 2018 dataset and also cross-validated on PH2 and ISBI 2017 datasets. Our proposed method surpassed the state-of-the-art with Dice Similarity Coefficient of 0.915 and Accuracy 0.959 on ISIC 2018 dataset and Dice Similarity Coefficient of 0.947 and Accuracy 0.971 on ISBI 2017 dataset. © 2020, Springer Nature Singapore Pte Ltd.

关键词： image segmentation

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Face recognition using entropy based face segregation as a pre-processing technique and conservative BPSO based feature selection 14

Face recognition using entropy based face segregation as a p...

引用

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

作者： Babu, S. Hitesh Birajdhar, Sachin A. Tambad, Samarth Dept. of Electronics and Communication Engg. M.S. Ramaiah Inst. of Tech. Bangalore560054 India

ISBN: (纸本)1595930361

the statistical appearance of the face can vary due to various factors such as pose, occlusion, expression and back-ground which makes it a challenging task to have an efficient Face Recognition (FR) system. this paper proposes 4 novel techniques viz., Entropy based Face Segregation (EFS) as pre-processing technique, Double Wavelet Noise Removal (DWNR) as pre-processing technique, 1D Stationary Wavelet Transform (SWT) as Feature Extractor and Conservative Binary Particle Optimization (CBPSO) as Feature Selector to enhance the performance of the system. EFS is used to segregate the facial region, thus removing the cluttered background. DWNR has unique combination of 2D Discrete Wavelet Transform (DWT), Wiener Filter and 2D SWT for image denoising and contrast enhancement. the pre-processed image is then fed to unique combination of 1D DWT, 1D SWT and 1D Discrete Cosine Transform (DCT) to extract essential features. CBPSO is used to select very optimum feature subset and significantly reduce the computation time. the proposed algorithm is experimented on four benchmark databases viz., Color FERET, CMU PIE, Pointing Head Pose and Georgia Tech. Copyright 2014 ACM.

关键词： Face recognition

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Domain Decomposition Based Preconditioned Solver for Bundle Adjustment 7th

Domain Decomposition Based Preconditioned Solver for Bundle ...

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7th National conference on computer vision, Pattern Recognition, image processing, and graphics, NCVPRIPG 2019

作者： Das, Shrutimoy Katyan, Siddhant Kumar, Pawan International Institute of Information Technology Hyderabad500032 India

ISBN: (纸本)9789811586965

We propose Domain Decomposed Bundle Adjustment (DDBA), a robust and efficient solver for the bundle adjustment problem. Bundle adjustment (BA) is generally formulated as a non-linear least squares problem and is solved by some variant of the Levenberg-Marquardt (LM) algorithm. Each iteration of the LM algorithm requires solving a system of normal equations, which becomes computationally expensive with the increase in problem size. the coefficient matrix of this system has a sparse structure which can be exploited for simplifying the computations in this step. We propose a technique for approximating the Schur complement of the matrix, and use this approximation to construct a preconditioner, that can be used with the Generalized Minimal Residual (GMRES) algorithm for solving the system of equations. Our experiments on the BAL dataset show that the proposed method for solving the system is faster than GMRES solve preconditioned with block Jacobi and more memory efficient than direct solve. © 2020, Springer Nature Singapore Pte Ltd.

关键词： computer vision

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Improved low resolution heterogeneous face recognition using re-ranking 6th

Improved low resolution heterogeneous face recognition using...

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

作者： Mudunuri, Sivaram Prasad Venkataramanan, Shashanka Biswas, Soma Department of Electrical Engineering Indian Institute of Science Bangalore India

ISBN: (纸本)9789811300196

Recently, near-infrared to visible light facial image matching is gaining popularity, especially for low-light and night-time surveillance scenarios. Unlike most of the work in literature, we assume that the near-infrared probe images have low-resolution in addition to uncontrolled pose and expression, which is due to the large distance of the person from the camera. To address this very challenging problem, we propose a re-ranking strategy which takes into account the relation of both the probe and gallery with a set of reference images. this can be used as an add-on to any existing algorithm. We apply it with one recent dictionary learning algorithm which uses alignment of orthogonal dictionaries. We also create a benchmark for this task by evaluating some of the recent algorithms for this experimental protocol. Extensive experiments are conducted on a modified version of the CASIA NIR VIS 2.0 database to show the effectiveness of the proposed re-ranking approach. © Springer Nature Singapore Pte Ltd. 2018.

关键词： Face recognition

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Neovascularization detection on retinal images 10

Neovascularization detection on retinal images

引用

10th indian conference on computer vision, graphics and image processing, ICVGIP 2016

作者： Kar, Sudeshna Sil Maity, Santi P. Maity, Seba Department of Information Technology Indian Institute of Engineering Science and Technology Shibpur Howrah711103 India Department of Electronics and Telecommunication Engineering College of Engineering and Management Kolaghat Kolaghat India

Proliferative Diabetic Retinopathy (PDR) is characterized by the growth of new abnormal, thin blood vessels called neovascu-larzation that spread along the retinal surface. An automated computer aided diagnosis system needs to identify neovasculars for PDR screening. Retinal images are often noisy and poorly illuminated. the thin vessels mostly appear to be disconnected and are inseparable from the background. this paper proposes a new method for neovascularization detection on retinal images. Blood vessels are extracted as thick, medium and thin types using multilevel thresholding on matched filter response. the total mutual information between the vessel density and the tortuosity of the thin vessel class is maximized to obtain the optimal thresholds to classify the normal and the abnormal vessels. Simulation results demonstrate that the proposed method outperforms the existing ones for neovascularization detection with an average accuracy of 97.54%. © Springer International Publishing AG 2017.

关键词： Blood vessels

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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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G3AN++: Exploring wide GANs with complementary feature learning for video generation 21

G3AN++: Exploring wide GANs with complementary feature learn...

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

作者： Gupta, Sonam Keshari, Arti Das, Sukhendu Indian Institute of Technology Madras Tamil Nadu Chennai India

ISBN: (纸本)9781450391276

Video generation task is a challenging problem which involves the modelling of complex real-world dynamics. Most of the existing methods have designed deep networks to tackle high-dimensional video data distributions. However, the utilization of wide networks is still under explored. Inspired by the success of wide networks in the image recognition literature, we present G3AN++, a three-stream generative adversarial network for video. the three streams are spatial, temporal and spatio-temporal processing branches. In pursuit of improving the quality of video generation, we make our network wider by splitting the spatial stream into two parallel identical branches learning complementary feature representations. We further introduce a novel adaptive masking layer to impose the complementary constraint. the masking layer encourages the parallel branches to learn distinct and richer visual features. Extensive quantitative and qualitative analysis demonstrates that our model outperforms the existing state-of-the-art methods by a significant margin on Weizmann Action, UvA-Nemo Smile and UCF101 Action datasets. Additional exploration reveals that G3AN++ is capable of disentangling the appearance and motion. We also show that the proposed method can be easily extended to solve the hard task of text-to-video generation. © 2021 ACM.

关键词： Generative adversarial networks

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Towards Realistic Underwater Dataset Generation and Color Restorationg± 13

Towards Realistic Underwater Dataset Generation and Color Re...

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

作者： Jain, Neham Matta, Gopi Raju Mitra, Kaushik IIT Madras Tamil Nadu Chennai India

ISBN: (纸本)9781450398237

Recovery of true color from underwater images is an ill-posed problem. this is because the wide-band attenuation coefficients for the RGB color channels depend on object range, reflectance, etc. which are difficult to model. Also, there is backscattering due to suspended particles in water. thus, most existing deep-learning based color restoration methods, which are trained on synthetic underwater datasets, do not perform well on real underwater data. this can be attributed to the fact that synthetic data cannot accurately represent real conditions. To address this issue, we use an image to image translation network to bridge the gap between the synthetic and real domains by translating images from synthetic underwater domain to real underwater domain. Using this multimodal domain adaptation technique, we create a dataset that can capture a diverse array of underwater conditions. We then train a simple but effective CNN based network on our domain adapted dataset to perform color restoration. Code and pre-trained models can be accessed at https://***/nehamjain10/TRUDGCR © 2022 ACM.

关键词： image reconstruction

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Particle coding for meshfree cutting of deformable assets 14

Particle coding for meshfree cutting of deformable assets

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

作者： Shrivastava, Prateek Das, Sukhendu VPLab. CSE IIT Madras India

ISBN: (纸本)1595930361

In this paper we propose a novel algorithm for cutting deformable (soft) assets, modeled using the meshfree method of Smoothed Particle Hydrodynamics (SPH). the key idea of the algorithm is to label all particles during the virtual cut operation to obtain particle codes. Since the traditional SPH formulations ignore particle separation due to cuts, we had to account for the change in topology due to virtual cuts in the SPH formulation. Virtual cut causes disruption in internal forces between separated particles. To avoid separated particles (i.e., particles belonging to different regions) affecting each other's dynamics, the generated particle codes are used for filtering the neighbours before SPH dynamics are computed. Assignment of particle codes eliminates the need for an external grid like structure for detecting newly generated cut surfaces. We exploit the region information extracted from the particle codes to obtain a color field for surface generation. We show that our algorithm can easily be integrated with existing SPH techniques. Our proposed method generates minimum overhead in scenarios where multiple cutting operations are performed on a deformable asset. Particle coding is applied across all particles in parallel and is hence computationally efficient when implemented on a GPU. Copyright 2014 ACM.

关键词： graphics processing unit

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