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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,...

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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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Convolutional Ensembling based Few-Shot Defect Detection Technique 22

Convolutional Ensembling based Few-Shot Defect Detection Tec...

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

作者： Soumyajit Karmakar Abeer Banerjee Prashant Gidde Sumeet Saurav Sanjay Singh Computer Science and Engineering Indian Institute of Information Technology Guwahati IN Advanced Information Technologies Group CSIR-CEERI IN Advanced Information Technologies Group CSIR-CEERI Pilani India

ISBN: (纸本)9781450398220

Over the past few years, there has been a significant improvement in the domain of few-shot learning. This learning paradigm has shown promising results for the challenging problem of anomaly detection, where the general task is to deal with heavy class imbalance. Our paper presents a new approach to few-shot classification, where we employ the knowledge base of multiple pre-trained convolutional models that act as the backbone for our proposed few-shot framework. Our framework uses a novel ensembling technique for boosting the accuracy while drastically decreasing the total parameter count, thus paving the way for real-time implementation. We perform an extensive hyperparameter search using a power-line defect detection dataset and obtain an accuracy of 92.30% for the 5-way 5-shot task. Without further tuning, we evaluate our model on competing standards with the existing state-of-the-art methods and outperform them.

关键词： Few-Shot Classification Anomaly Detection Ensemling Strategy

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Spectrum-inspired Low-light image Translation for Saliency Detection 22

Spectrum-inspired Low-light Image Translation for Saliency D...

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

作者： Kitty Varghese Sudarshan Rajagopalan Mohit Lamba Kaushik Mitra Indian institute of technology Madras India Madras Institute of Technology India Indian Institute of Technology Madras India IIT Madras India

ISBN: (纸本)9781450398220

Saliency detection methods are central to several real-world applications such as robot navigation and satellite imagery. However, the performance of existing methods deteriorate under low-light conditions because training datasets mostly comprise of well-lit images. One possible solution is to collect a new dataset for low-light conditions. This involves pixel-level annotations, which is not only tedious and time-consuming but also infeasible if a huge training corpus is required. We propose a technique that performs classical band-pass filtering in the Fourier space to transform well-lit images to low-light images and use them as a proxy for real low-light images. Unlike popular deep learning approaches which require learning thousands of parameters and enormous amounts of training data, the proposed transformation is fast and simple and easy to extend to other tasks such as low-light depth estimation. Our experiments show that the state-of-the-art saliency detection and depth estimation networks trained on our proxy low-light images perform significantly better on real low-light images than networks trained using existing strategies.

关键词： Low-light and salient object detection

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Design of a System and Method for Optimal selection of Tumor Slice using Linear Ultrasound Imaging for Histopathology 22

Design of a System and Method for Optimal selection of Tumor...

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

作者： Abhishek Kumar Debdoot Sheet Indian Institute of Technology Kharagpur IN

ISBN: (纸本)9781450398220

In excision biopsy, a tumor mass is surgically removed from the body. Subsequently, it is sliced at an appropriate location and investigated microscopically through a process called histopathology. Any bias in tumor slicing severely influences histopathology outcomes, such as if malignant foci do not appear in the sliced location, then, the tumor would be accidentally reported non-malignant. The standard approach adopted to solve this challenge by a histopathologist is to overcome this bias by slicing at multiple locations for their investigation. Till now, this process has been manual, time-consuming, and error-prone. We aim to design a system and develop a data-driven deep learning approach to assist histopathologists by providing them with a representative slice location for reporting to increase their efficiency and accuracy. We have developed a low cost linear gantry scanner that can acquire images and integrated with a deep learning model to predict the optimal slice representative of pathology in a tumor mass. We achieve an F1 score of 0.97 and an accuracy of 97.5% in predicting an optimal slice using this approach.

关键词： Deep learning Excision biopsy Histopathology Imaging gantry assembly Ultrasound imaging

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I’m GROOT: a multi head multi GRaph netwOrk recognizing surgical actiOn Triplets✱ 22

I’m GROOT: a multi head multi GRaph netwOrk recognizing sur...

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

作者： Raviteja Sista Rachana Sathish Riya Agrawal Utpal De Partha Chakrabarti Debdoot Sheet Indian Institute of Technology Kharagpur IN Nrs IN NRS Medical College IN

ISBN: (纸本)9781450398220

Laparoscopic cholecystectomy is a widely performed minimally invasive surgical procedure that imposes many challenges to the operating surgeon. While we strive to understand and automate such surgeries, the key is to identify the actions involved in it. An action involves a set of tools and a target anatomy, together forming the action triplets. However, the relations between the triplets and their constituents are sparse, making it challenging to learn their relations. In this paper, we propose a graph neural network based approach to exploit these underlying sparse relations in the data. We portray the proposed method’s ability to uniformly learn multiple tasks and classify triplets with an mAP of 0.261. In addition, we experimentally show the inability of fully connected and convolution layers to learn these sparse relations when trained on 40 laparoscopic videos and validated using five videos. Codes will be available at : https://***/iitkliv/groot.

关键词： Sparse relations Graph neural networks Multi task learning Surgical action triplets Laparoscopic cholecystectomy

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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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A Neural Network Based Approach for Geo-Localizing Events in Crowd Sourced Videos 2018

A Neural Network Based Approach for Geo-Localizing Events in...

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

作者： Amit More Subhasis Chaudhuri Indian Institute of Technology Bombay Mumbai Maharastra

ISBN: (纸本)9781450366151

Popular events are often video recorded simultaneously by a general crowd using smartphones. In the present work, we propose a robust recurrent neural network (RNN) based approach for geo-localizing these events using sensor data collected by user smartphones while recording such events. For this task we use GPS and compass sensors, which are commonly available on the smartphones. The circular nature (modulo 2π) of the orientation data from compass limits the ability of the classical neural networks (NN) to geo-localize these events. We mitigate this issue by incorporating circular nodes in our network and show the performance improvements. We train the proposed NN model using simulated data and apply it directly on real data. We train several RNN models using this strategy and show our analyses. The proposed work outperforms all previous approaches in terms of event geo-localization accuracy.

关键词： Event Geo-localization Neural Network Compass Data Smartphone Circular Nodes GPS data

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Knowledge representation for vision: an associative network for single object representation and recognition

Knowledge representation for vision: an associative network ...

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International conference on Pattern Recognition

作者： S. Basu A. Gupta N. Sarkar D.D. Majumder Techno Macbintel Private Limited Calcutta India National Centre for Knowledge Based Computing Indian Statistical Institute ECSU Calcutta India

VIKNET (visual knowledge network), an extension of associative networks that is specifically designed for knowledge representation for recognition of 3-D objects, is introduced. The design criteria, largely influenced by early vision processing, are discussed. VIKNET is formally described as an algebraic structure, with network functions defined on it. The input image, also in the form of a network, is recognized by a partial matching algorithm. A completely worked out example demonstrates the efficacy of the scheme even for shapes subjected to viewing angle transforms.< >

关键词： Knowledge representation Shape computer networks Transfer functions image recognition Feature extraction Layout Color

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FAST AND ACCURATE BILATERAL FILTERING USING GAUSS-POLYNOMIAL DECOMPOSITION

FAST AND ACCURATE BILATERAL FILTERING USING GAUSS-POLYNOMIAL...

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IEEE International conference on image processing

作者： Kunal N. Chaudhury Department of Electrical Engineering Indian Institute of Science

ISBN: (纸本)9781479983407

The bilateral filter is a versatile non-linear filter that has found diverse applications in image processing, computer vision, computer graphics, and computational photography. A common form of the filter is the Gaussian bilateral filter in which both the spatial and range kernels are Gaussian. A direct implementation of this filter requires O(σ~2) operations per pixel, where σ is the standard deviation of the spatial Gaussian. In this paper, we propose an accurate approximation algorithm that can cut down the computational complexity to O(1) per pixel for any arbitrary σ (constant-time implementation). This is based on the observation that the range kernel operates via the translations of a fixed Gaussian over the range space, and that these translated Gaussians can be accurately approximated using the socalled Gauss-polynomials. The overall algorithm emerging from this approximation involves a series of spatial Gaussian filtering, which can be efficiently implemented (in parallel) using separability and recursion. We present some preliminary results to demonstrate that the proposed algorithm compares favorably with some of the existing fast algorithms in terms of speed and accuracy.

关键词： Bilateral filter Approximation Gauss-polynomial Convolution Fast algorithm bilateral filters Convolution approximation Gauss pixel Filters Gaussian filtering Fasting Filtration Recursion Approximation algorithms

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Synthetic Data Approach for Unconstrained Low-Resolution Face Recognition in Surveillance Applications✱ 22

Synthetic Data Approach for Unconstrained Low-Resolution Fac...

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

作者： Ravindra Kumar Soni Neeta Nain Department of Computer Science & Engineering Malaviya National Institute of Technology Jaipur India

ISBN: (纸本)9781450398220

Unconstrained low-resolution (LR) face recognition is still a challenging problem in computer vision. In real-world scenarios, the gallery images are generally of high-resolution (HR), while the probe images may be of low resolution. In surveillance applications, the challenge of matching LR to HR is more common because the probe images are captured in low resolution while gallery images are of high resolution. LR to HR face matching is challenging because, in the embedding space, there is a need for a common subspace for mapping the LR and HR embeddings. In LR to LR face matching, where probe and gallery both belong to low-resolution, face identification is more difficult because very less visual information is present in the images. In LR to LR face matching, the challenge becomes very hard if faces are tiny in size and belong to low-resolution. In this paper, we implement a deep learning pipeline for matching the LR to HR and LR to LR faces. Due to the absence of LR and HR images of the same identity in the real-world datasets, we have also generated the LR images from HR images using the synthetic data approach. Extensive experimental analyses have been made to compare the performance to other state-of-the-art models.

关键词： Deep learning

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