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Kaushik Mitra
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Kaushik Mitra
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Kaushik Mitra
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Mitra, Kaushik
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4 results
Now showing 1 - 4 of 4
- PublicationJoint optic disc and cup segmentation using fully convolutional and adversarial networks(01-01-2017)
;Shankaranarayana, Sharath M. ;Ram, Keerthi; Glaucoma is a highly threatening and widespread ocular disease which may lead to permanent loss in vision. One of the important parameters used for Glaucoma screening in the cup-to-disc ratio (CDR), which requires accurate segmentation of optic cup and disc. We explore fully convolutional networks (FCNs) for the task of joint segmentation of optic cup and disc. We propose a novel improved architecture building upon FCNs by using the concept of residual learning. Additionally, we also explore if adversarial training helps in improving the segmentation results. The method does not require any complicated preprocessing techniques for feature enhancement. We learn a mapping between the retinal images and the corresponding segmentation map using fully convolutional and adversarial networks. We perform extensive experiments of various models on a set of 159 images from RIM-ONE database and also do extensive comparison. The proposed method outperforms the state of the art methods on various evaluation metrics for both disc and cup segmentation. - PublicationLearning light field reconstruction from a single coded image(13-12-2018)
;Vadathya, Anil Kumar ;Cholleti, Saikiran ;Ramajayam, Gautham ;Kanchana, VijayalakshmiLight field imaging is a rich way of representing the 3D world around us. However, due to limited sensor resolution capturing light field data inherently poses spatio-Angular resolution trade-off. In this paper, we propose a deep learning based solution to tackle the resolution trade-off. Specifically, we reconstruct full sensor resolution light field from a single coded image. We propose to do this in three stages 1) reconstruction of center view from the coded image 2) estimating disparity map from the coded image and center view 3) warping center view using the disparity to generate light field. We propose three neural networks for these stages. Our disparity estimation network is trained in an unsupervised manner alleviating the need for ground truth disparity. Our results demonstrate better recovery of parallax from the coded image. Also, we get better results than dictionary learning approaches on simulated data. - PublicationCompressive image recovery using recurrent generative model(20-02-2018)
;Dave, Akshat ;Kumar, Anil ;Vadathya,Reconstruction of signals from compressively sensed measurements is an ill-posed problem. In this paper, we leverage the recurrent generative model, RIDE, as an image prior for compressive image reconstruction. Recurrent networks can model long-range dependencies in images and hence can handle global multiplexing in compressive imaging. We perform MAP inference with RIDE using back-propagation to the inputs and projected gradient method. We propose an entropy thresholding based approach for preserving texture in images well. Our approach shows superior reconstructions compared to recent global reconstruction approaches like D-AMP and TVAL3 on both simulated and real data. - PublicationHand gesture sequence recognition using inertial motion units (IMUs)(13-12-2018)
;Kavarthapu, Dilip ChakravarthyUnlike approaches that classify single gesture at a time, we propose a deep learning based technique that can classify multiple gestures in one shot. This is specially suitable for applications that involves seamless gesture sequences such as sign language recognition, touch-less car assistance systems and gaming systems. We propose a Long Short Term Memory(LSTM) based deep network on the lines of an Encoder-Decoder architecture that classifies gesture sequence accurately in one go. We also show an empirical training strategy for our architecture which can achieve good results even with limited amount of collected data. Results from the experiments performed on labelled datasets from Inertial Motion Units (IMU) proves the efficiency and usefulness of the proposed method.