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

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Comparison of foveal avascular zone in diabetic retinopathy, high myopia, and normal fundus images 30

Comparison of foveal avascular zone in diabetic retinopathy,...

引用

30th conference on Ophthalmic Technologies

作者： Balaji, Jothi J. Agarwal, Arpit Raman, Rajiv Lakshminarayanan, Vasudevan Department of Optometry Medical Research Foundation Chennai600 006 India Department of Chemical Engineering Indian Institute of Technology Kanpur208016 India Shri Bhagwan Mahavir Vitreoretinal Services Medical Research Foundation Chennai600 006 India Theoretical and Experimental Epistemology Lab School of Optometry and Vision Science University of Waterloo WaterlooONN2L 3G1 Canada

ISBN: (纸本)9781510631991

To quantitatively describe and evaluate a new image processing technique for estimating the Foveal Avascular Zone (FAZ) in subjects with Diabetic Retinopathy and myopes. From a total of 328 images obtained from Diabetic Retinopathy (113), myopes (120) and normal (93), the FAZ dimensions were quantified using a new image processing algorithm. These parameters were also determined manually and by the OCT manufacturer's inbuilt algorithm. In the new technique, the images were first pre-processed by using a DOG filter iteratively before being complemented followed by a Prewitt edge detection and repeated image dilation at angles of 00, 450 and 900. image closure was then applied followed by noise and small object removal which resulted in the segmented boundary. For deeper insight into shape change, in addition to the diameter of the FAZ other parameters such as the area, diameter, major axis, minor axis, orientation, perimeter vessel avascular density (VAD), Vessel diameter Index (VDI), etc. were obtained. The circularity index was calculated using the FAZ area and perimeter parameters. The mean FAZ diameter (mm) by the new automated technique, manual-segmentation (ground truth), and inbuilt instrument algorithm were 0.67 ± 0.87, 0.67 ± 0.72 and 0.61 ± 0.14. The mean of FAZ area (mm2) was 0.36 ± 0.10, 0.33 ± 0.09 and 0.43 ± 0.14 in normal, myopia and diabetic subjects respectively. The new technique shows considerable improvement in accuracy (mean ± SD) when compared to the inbuilt system segmentation and the ground truth (manual marking by an expert clinician). The study results show that the FAZ area in Diabetic Retinopathy is significantly different (p=0.003) when compared to myopic eyes (p=0.016) and normals. © COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.

关键词： Optical tomography

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A Deep Learning-based Model for Phase Unwrapping 2018

A Deep Learning-based Model for Phase Unwrapping

引用

proceedings of the 11th indian conference on computer vision, graphics and image processing

作者： G. E. Spoorthi Subrahmanyam Gorthi Rama Krishna Sai Gorthi Indian Institute of Technology Tirupati A.P

ISBN: (纸本)9781450366151

Phase unwrapping is an important problem in several applications that attempts to restore original phase from wrapped phase. In this paper, we propose a novel phase unwrapping model based on the deep convolutional neural network by formulating the phase unwrapping as a semantic segmentation problem. The proposed architecture consists of a convolutional encoder network and corresponding decoder network followed by a pixel-wise classification layer. One of the critical challenges in DCNN is availability of large set of labeled training data. This issue is effectively circumvented for the proposed framework through a generic simulation procedure that automatically generates large labeled data. Results from the proposed method are compared with widely used quality-guided phase unwrapping algorithm for various SNR values. It is found that the proposed method is performing well both in terms of accuracy and computational time, even in the presence strong noise. To the best of our knowledge, this is the first work that uses convolutional neural network for phase unwrapping, and this will hopefully pave the way to a new class of techniques for unwrapping the phase.

关键词： Decoder Deep Convolutional Neural Network (DCNN) Encoder Phase Unwrapping Semantic Segmentation

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Towards Automated Floorplan Generation 18

Towards Automated Floorplan Generation

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proceedings of the 11th indian conference on computer vision, graphics and image processing

作者： Kunal Chelani Chitturi Sidhartha Venu Madhav Govindu Indian Institute of Science Bengaluru India

ISBN: (纸本)9781450366151

In this paper, we propose a pipeline for generating a 2D floorplan using depth cameras. In our pipeline we use an existing approach to recovering the camera motion trajectories from the depth and RGB sequences. Given these motion estimates we construct a full 3D representation of the scanned indoor spaces. For generating a floorplan we need to abstract the large volumes of registered 3D data into a simplified rectilinear representation. We evaluate two approaches to solve this problem, viz slicing the reconstructed volume at a given height and direct segmentation of the 3D point cloud representation into individual planar segments. We also note that the fidelity of our estimated floorplan crucially depends on the accuracy of the estimation of the ground plane orientation. We examine the comparative accuracies of two ground plane estimation methods for each of the above mentioned approaches to rectilinear abstraction. Given the line drawing abstractions of the individual rooms, we merge them into a consistent floorplan. We present results on a real-world floorplan estimation problem and demonstrate its accuracy. Additionally, the implications of errors in the individual components of our pipeline are also studied.

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Multiple Kernel Fisher Discriminant Metric Learning for Person Re-identification 2018

Multiple Kernel Fisher Discriminant Metric Learning for Pers...

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proceedings of the 11th indian conference on computer vision, graphics and image processing

作者： T. M. Feroz Ali Rajbabu Velmurugan Kalpesh K. Patel Subhasis Chaudhuri Dept. of Electrical Engineering Indian Institute of Technology Bombay Mumbai India

ISBN: (纸本)9781450366151

Person re-identification addresses the problem of matching pedestrian images across disjoint camera views. Design of feature descriptor and distance metric learning are the two fundamental tasks in person re-identification. In this paper, we propose a metric learning framework for person re-identification, where the discriminative metric space is learned using Kernel Fisher Discriminant Analysis (KFDA), to simultaneously maximize the inter-class variance as well as minimize the intra-class variance. We derive a Mahalanobis metric induced by KFDA and argue that KFDA is efficient to be applied for metric learning in person re-identification. We also show how the efficiency of KFDA in metric learning can be further enhanced for person re-identification by using two simple yet efficient multiple kernel learning methods. We conduct extensive experiments on three benchmark datasets for person re-identification and demonstrate that the proposed approaches have competitive performance with state-of-the-art methods.

关键词： Kernel Fisher Discriminant Analysis Multiple Kernel Fisher Discriminant Analysis Person Re-identification Metric Learning

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On-Demand Augmentation of Contour Trees 18

On-Demand Augmentation of Contour Trees

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proceedings of the 11th indian conference on computer vision, graphics and image processing

作者： Mohit Sharma Vijay Natarajan Indian Institute of Science Bangalore

ISBN: (纸本)9781450366151

The contour tree represents the topology of level sets of a scalar function. Nodes of the tree correspond to critical level sets and arcs of the tree represent a collection of topologically equivalent level sets connecting two critical level sets. The augmented contour tree contains degree-2 nodes on the arcs that represent regular level sets. The degree-2 nodes correspond to regular points of the scalar function and other critical points that do not affect the number of level set components. The augmented contour tree is significantly larger in size and requires more effort to compute when compared to the contour tree. Applications of the contour tree to data exploration and visualization require the augmented contour tree. Current approaches propose algorithms to compute the contour tree and the augmented contour tree from scratch. Precomputing and storing the large augmented contour tree will not be necessary if the contour tree can be augmented on-demand. This paper poses the problem of computing the augmented contour tree given a contour tree as input. Computational experiments demonstrate that the on-demand augmentation can be computed fast while resulting in good memory savings.

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Symbol Spotting in Offline Handwritten Mathematical Expressions 4th

Symbol Spotting in Offline Handwritten Mathematical Expressi...

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4th Workshop on Document Analysis and Recognition, DAR 2018, held in Conjunction with the 11th indian conference on vision, graphics, and image processing, ICVGIP 2018

作者： Aggarwal, Ridhi Harit, Gaurav Tiwari, Anil Kumar Department of Computer Science and Engineering Indian Institute of Technology Jodhpur Jodhpur India Department of Electrical Engineering Indian Institute of Technology Jodhpur Jodhpur India

ISBN: (纸本)9789811393600

Recognition of touching characters in mathematical expressions is a challenging problem in the field of document image analysis. Various approaches for recognizing touching maths symbols have been reported in literature, but they mainly dealt with printed expressions and handwritten numeral strings. In this work, a new segmentation-free approach is proposed which matches convex shape portions of symbols occurring in various layout such as subscript, superscript, fraction etc. and is able to perform spotting of symbols present in a handwritten expression. Our contribution lies in the design of a novel feature which can handle touching symbols effectively in the presence of handwriting variations. This recognition-based approach helps in spotting symbols in an expression even in the presence of clutter created by the presence of other symbols. © Springer Nature Singapore Pte Ltd. 2019.

关键词： Character recognition

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Zero-shot Learning using Graph Regularized Latent Discriminative Cross-domain Triplets 2018

Zero-shot Learning using Graph Regularized Latent Discrimina...

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proceedings of the 11th indian conference on computer vision, graphics and image processing

作者： Omkar Gune Meet Vora Biplab Banerjee Subhasis Chaudhuri Indian Institute of Technology Bombay India Indian Institute of Technology Roorkee India

ISBN: (纸本)9781450366151

Zero-shot learning (ZSL) for visual recognition aims at identifying the previously unseen class samples given a trained model on the labeled visual samples of seen classes and additional class-level semantic side information for all classes. Often ZSL is tackled by learning an embedding function from the visual to semantic space or vice-versa. However, learning this mapping often results in loss of discriminative property of learned embedding space, thus severely compromising the recognition performance on the test samples. In order to ensure improved discrimination in the embedding space, we introduce a ZSL framework by leveraging the intuitive idea of cross-domain triplets based metric learning for learning such a space. Additionally, we introduce a novel graph Laplacian based regularizer which aligns the graph structures of the visual and semantic spaces in the learned embedding space. Simultaneously optimizing both the criteria results in a compact, discriminative, and meaningful embedding space, which is experimentally found to be superior to most of its existing counterparts on both the standard ZSL (AwA and CUB) and the challenging generalized ZSL (AwA1, AwA2, CUB) settings.

关键词： Zero-shot learning cross-domain alignment

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Dynamic image Networks for Human Fall Detection in 360-degree Videos 1

引用

3rd Workshop on computer vision Applications, WCVA 2018, held in conjunction with the 11th indian conference on computer vision, graphics and image processing, ICVGIP 2018

作者： Saurav, Sumeet Madhu Kiran, T.N.D. Sravan Kumar Reddy, B. Sanjay Srivastav, K. Singh, Sanjay Saini, Ravi CSIR-CEERI Pilani India Birla Institute of Technology and Sciences Pilani India

ISBN: (数字)9789811513879

ISBN: (纸本)9789811513862

Detection of falls of elderly people is a trivial yet an immediate problem due to the growing age of the population. This demands the need for autonomous self care systems for providing a quick assistance. The three basic approaches used for fall detection include non-invasive vision based devices, ambient based devices and wearable devices. The paper tries to improve upon the state-of-art of accuracy to 98% using vision based system. This was achieved through transfer learning by extending the idea of action recognition using dynamic images which is a standard RGB image containing the appearance and dynamics of a whole video sequence. Such information is vital in dealing with applications like human action recognition. Since we are also looking for a cheap and scalable solution, the use of a 360∘ camera seems reasonable and reliable. The top view provided by this camera gives a better perspective than any other alternatives by giving an un-obstructive view of the subjects. © Springer Nature Singapore Pte Ltd, 2019.

关键词： Fall detection

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Textual Content Retrieval from Filled-in Form images 4th

Textual Content Retrieval from Filled-in Form Images

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4th Workshop on Document Analysis and Recognition, DAR 2018, held in Conjunction with the 11th indian conference on vision, graphics, and image processing, ICVGIP 2018

作者： Ghosh, Soulib Bhattacharya, Rajdeep Majhi, Sandipan Bhowmik, Showmik Malakar, Samir Sarkar, Ram Department of Computer Science and Engineering Jadavpur University Kolkata India Department of Computer Science Asutosh College Kolkata India

ISBN: (纸本)9789811393600

Form processing refers to the process of extraction of information from filled-in forms. In this work, we have addressed three very crucial challenges of a form processing system, namely touching component separation, text non-text separation and handwritten-printed text separation. The proposed method is evaluated on a database having 50 filled-in forms written in Bangla, collected during an essay competition in a school. The experimental results are promising. © Springer Nature Singapore Pte Ltd. 2019.

关键词： Separation

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Bag of Visual Words based Correlation Filter Tracker (BoVW-CFT) 18

Bag of Visual Words based Correlation Filter Tracker (BoVW-C...

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proceedings of the 11th indian conference on computer vision, graphics and image processing

作者： Priya Mariam Raju Deepak Mishra Gorthi Rama Krishna Sai Subrahmanyam Indian Institute of Space Science and Technology Thiruvananthapuram Kerala Indian Institute of Technology Tirupati Andhra Pradesh

ISBN: (纸本)9781450366151

Accurate and robust visual object tracking is one of the most challenging computer vision problems. Recently, discriminative correlation filter trackers have shown promising results on benchmark datasets with continuous performance improvements in tracking accuracy and robustness. Still, these algorithms fail to track as the target object and background conditions undergo drastic changes over time. They are also incapable to resume tracking once the target is lost, limiting the ability to track long term. The proposed BoVW-CFT is a classifier-based generic technique to handle tracking uncertainties in correlation filter trackers. Tracking failures in correlation trackers are automatically identified and an image classifier with training, testing and online update stages is proposed as detector in the tracking scenario using Bag of Visual Words (BoVW) features. The proposed detector falls under the parts based model and is quite well suited in the tracking framework. Further, the online training stage in the proposed framework with updated model or training samples, incorporates temporal information, helping to detect rotated, blurred and scaled versions of the target. On detecting a target loss in the correlation tracker, the trained classifier, referred to as detector, is invoked to re-initialize the tracker with the actual target location. Therefore, for each tracking uncertainty, two output patches are obtained, one each from the base tracker and the classifier. The final target location is estimated using the normalized cross-correlation with the initial target patch. The method has the advantages of mitigating the model drift in correlation trackers and learns a robust model that tracks long term. Extensive experimental results demonstrate an improvement of 4.1% in the expected overlap, 1.86% in accuracy and 15.46% in robustness on VOT2016 and 1.82% in overlap precision, 2.32% in AUC and 2.87% in success rates on OTB100.

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