Browsing by Author "Narendra Rao, T.J."

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Deep Learning Based Sub-Retinal Fluid Segmentation in Central Serous Chorioretinopathy Optical Coherence Tomography Scans
(Institute of Electrical and Electronics Engineers Inc., 2019) Narendra Rao, T.J.; Girish, G.N.; Kothari, A.R.; Rajan, J.
Development of an automated sub-retinal fluid segmentation technique from optical coherence tomography (OCT) scans is faced with challenges such as noise and motion artifacts present in OCT images, variation in size, shape and location of fluid pockets within the retina. The ability of a fully convolutional neural network to automatically learn significant low level features to differentiate subtle spatial variations makes it suitable for retinal fluid segmentation task. Hence, a fully convolutional neural network has been proposed in this work for the automatic segmentation of sub-retinal fluid in OCT scans of central serous chorioretinopathy (CSC) pathology. The proposed method has been evaluated on a dataset of 15 OCT volumes and an average Dice rate, Precision and Recall of 0.91, 0.93 and 0.89 respectively has been achieved over the test set. Â© 2019 IEEE.
Smart visual surveillance technique for better crime management
(CRC Press, 2017) Narendra Rao, T.J.; Rajan, J.
In the last few years, with concerns about terrorism and global security on the rise, it has become vital to have inplace threat detection devices to detect and recognize suspicious activities and alert the authorities to take appropriate counter actions. Visual Sensor Networks consisting of surveillance cameras are in wide use due to their highly effective monitoring ability which is beyond human capacity. Anomalous event detection is the foremost objective of any automated visual surveillance system. In this chapter, a research work on the development of a smart visual surveillance approach is presented. A detailed discussion of a contextual information based anomalous activity detection and localization algorithm (MSTC) is the main focus of the chapter. It is a modified version of the Spatio-Temporal Compositions (STC) concept introduced earlier. Certain deviations have been incorporated with an objective to reduce time complexity and achieve better performance. In MSTC, the unusual event detection is based on the likelihood of the different regions or ensembles of each query being normal. This requires probabilistic modeling of the arrangement of volumes in the ensembles. The inference mechanisms identify the compositions to be anomalous, which have lower likelihood of occurrence than a threshold. The redundancy of volumes and ensembles has been avoided through their construction in a non-overlapped manner, which aids in lowering the computational complexity of the codebook construction and probabilistic modeling steps. The codebook construction process employed is less complex and has proved to be reliable during experimentation. Three adaptive inference mechanisms determine the decision making threshold for anomalous event detection, given the context. Further, towards making the surveillance approach energy efficient, the concepts of event-driven processing of the query videos and anomaly-driven transmission of only the anomalous frames, implemented in the work have also been included in the chapter. Visual enhancement of the unusual events to aid in surveillance application is another feature discussed. All these software components together demonstrate intelligent functioning in this approach towards smart visual surveillance. © 2018 by Taylor & Francis Group, LLC.