Browsing by Author "Sumam David, S.S."

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3D Estimation and visualization of motion in a multicamera network for sports
(2011) Kumar, A.; Chavan, P.S.; Sharatchandra, V.K.; Sumam David, S.S.; Kelly, P.; Oâ€™Connor, N.E.
In this work, we develop image processing and computer vision techniques for visually tracking a tennis ball, in 3D, on a court instrumented with multiple low-cost IP cameras. The technique first obtains 2D ball tracking data from each camera view using 2D object tracking methods. Next, an automatic feature-based video synchronization method is applied. This technique uses the extracted 2D ball information from two or more camera views, plus camera calibration information. In order to find 3D trajectory, the temporal 3D locations of the ball is estimated using triangulation of correspondent 2D locations obtained from automatically synchronized videos. Furthermore, in order to improve the continuity of the tracked 3D ball during times when no two cameras have overlapping views of the ball location, we incorporate a physics-based trajectory model into the system. The resultant 3D ball tracks are then visualized in a virtual 3D graphical environment. Finally, we quantify the accuracy of our system in terms of reprojection error. Â© 2011 IEEE.
Accurate estimation of decay coefficients for dynamic range compressors in hearing aids and a hardware level comparison of different architectures
(Elsevier B.V., 2020) Deepu, S.P.; Ramesh Kini, M.R.; Sumam David, S.S.
Dynamic Range Compression (DRC) algorithm helps to protect the residual hearing ability of hearing aid users by compressing the signal levels which go above a particular threshold. This paper addresses two different aspects of DRC for hearing aid applications. In the first part, methods to estimate the decay coefficients corresponding to the required time constants for a feed-forward DRC architecture accurately, to meet the hearing aid specifications are proposed. The effect of compression on the attack and release time parameters are compensated with the new formula. The hardware implementation of four different DRC architectures is explained in the second part of the paper. The estimated decay coefficients for a test signal were used for the corresponding hardware implementations and verified the validity of proposed algorithmic modifications. The architectures were implemented using UMC 65 nm standard cell libraries and the power and error results were compared. The proposed methods to estimate the decay coefficients for both attack and release phases show close to 0 dB error from expected output values, while conventional methods are not meeting the specifications. Hardware implementation shows that there is not much improvement in power performance, between a lower resolution Look-Up Table (LUT) based logarithm implementation and a higher resolution one. From the results, we propose using the absolute level detector based DRC with higher resolution logarithm without a gain smoothing stage at the output for lowest power consumption and better approximation error performance. © 2020 Elsevier B.V.
Hardware implementation of dual-tree wavelet transform based image reconstruction
(Institute of Electrical and Electronics Engineers Inc., 2020) Sudhakar, H.; Kalam, L.M.; Muralitharan, S.; Deepu, S.P.; Sumam David, S.S.
Real-time implementations of image processing algorithms on embedded platforms are gaining importance. In this paper, we propose an Application Specific Integrated Circuit (ASIC) architecture for the perfect reconstruction of images using wavelets with a view to extending this to denoising and feature extraction of images. An architecture that implements the Dual-Tree Wavelet Transform is presented. The architecture features a 128x128 single-port block memory and its addressing schemes, a simple upsampling/downsampling method and a folding and adding mechanism. It is implemented using 180nm technology. The results show perfect reconstruction of 128x128 grayscale images with up to 1-bit error in pixel values when compared to the corresponding input images. Â© 2021 IEEE
Non-intrusive methods to detect air-gap eccentricity faults in three-phase induction motor
(Praise Worthy Prize S.r.l, 2020) Bindu, S.; Sumam David, S.S.; Thomas, V.V.
The focus of this paper is model-based behavioural analysis for the diagnosis of the air-gap eccentricity fault condition in the three-phase induction machine. To facilitate reliable and sensitive diagnosis, an online condition monitoring system has to depend on multiple signals and signatures. Air-gap mixed eccentricity fault signatures derived from three non-invasive methods such as stator line current, instantaneous power, and estimated air-gap torque are presented. Modified winding function theory and multiple coupled circuit approaches are used to model the machine. The sidebands present around the fundamental frequency in the spectrum of stator current, and around double the supply frequency in the spectrum of instantaneous power indicate the presence of air-gap mixed eccentricity. The low frequencies which exist near the DC component in the spectra of the air-gap torque and instantaneous power also indicate the same. These specific components were also observed in the high-frequency spectra around the principal slot harmonics. The modelling approach, the torque estimation approach, the simulation results at different severity and load conditions, and the experimentation result in a motor with prefabricated eccentricity are presented. © 2020 Praise Worthy Prize S.r.l.-All rights reserved.
Perception of Engineering Among Girls in India: Implications on Career Decisions
(Academic Conferences and Publishing International Limited, 2025) Geethalakshmi, P.M.; Thomas, V.V.; Sumam David, S.S.
India is witnessing a steady growth in the enrolment of girls into engineering education. There has been a change in the enrolment status from 9% in 2012 to almost 20% in 2023. This positive movement may be attributed to the affirmative actions taken by the Indian government in promoting engineering education among girls. These actions were conceived with a hope to reach critical mass to maintain the momentum. The vision thus is to facilitate participation of girls in technical domain as an organic movement. Taking this development into cognizance, this study examines the current perception of engineering among female science students (those opted for Physics, Mathematics, Chemistry in senior secondary classes) and their intended direction of higher education. Senior high school female science students from seven different high schools (n=150) were administered an open-ended question seeking their understanding on engineers and engineering. 137 students responded. The responses were analysed using content analysis. The analysis resulted in four categories viz., â€˜Impression of engineersâ€™, â€˜Impression of engineering careerâ€™, â€˜Association of utilitarian value, and â€˜Association of incomeâ€™. The theme which ran across these categories was â€˜relate, recognize and requirementâ€™. The study revealed that largely female students have expressed their intention of higher education, without exploring or seeking more clarity on the image which they hold to be correct. It could also be seen that there was no explicit mention of the gendered nature of engineering although it was implicit in their perception. We, thus propose interventions aiming at providing a comprehensive view on engineering field making it as an attractive and possible career option for female science students. Â© the authors, 2025. All Rights Reserved.
Perceptually lossless coder for volumetric medical image data
(Academic Press Inc. apjcs@harcourt.com, 2017) Chandrika, B.K.; Aparna., P.; Sumam David, S.S.
With the development of modern imaging techniques, every medical examination would result in a huge volume of image data. Analysis, storage and/or transmission of these data demands high compression without any loss of diagnostically significant data. Although, various 3-D compression techniques have been proposed, they have not been able to meet the current requirements. This paper proposes a novel method to compress 3-D medical images based on human vision model to remove visually insignificant information. The block matching algorithm applied to exploit the anatomical symmetry remove the spatial redundancies. The results obtained are compared with those of lossless compression techniques. The results show better compression without any degradation in visual quality. The rate-distortion performance of the proposed coders is compared with that of the state-of-the-art lossy coders. The subjective evaluation performed by the medical experts confirms that the visual quality of the reconstructed image is excellent. © 2017
Visually lossless coder for volumetric MRI and CT image data using wavelet transform
(Inderscience Publishers, 2017) Chandrika, B.K.; Aparna., P.; Sumam David, S.S.
Medical imaging modalities produce large volume of digital data each day in modern healthcare. Several techniques have been proposed for volumetric medical image data compression. In this paper, we present a novel wavelet-based visually lossless coding scheme for the compression of volumetric magnetic resonance imaging (MRI) and computed tomography (CT) images. A visual model is incorporated in the coder to identify and measure visually irrelevant information. Performance of the compression scheme is further improved by eliminating the slice redundancy. The obtained results show better compression ratio compared to results obtained with pixel-based visually lossless compression technique, without any degradation in visual quality. We compared the performance of proposed technique with standard state of the art compression codecs such as joint photographic experts group-lossless (JPEG-LS), JPEG-2000, JPEG-3D, H.264/MPEG-4 AVC, differential pulse code modulation (DPCM) and medical image lossless compression (MILC). Results show better compression ratio over that of standard lossless compression schemes without any perceivable distortion. © 2017 Inderscience Enterprises Ltd.
WeakSegNet: Combining Unsupervised, Few-Shot, and Weakly Supervised Methods for the Semantic Segmentation of Low-Magnification Effusion Cytology Images
(Institute of Electrical and Electronics Engineers Inc., 2025) Aboobacker, S.; Vijayasenan, D.; Sumam David, S.S.; Suresh, P.K.; Sreeram, S.
Effusion cytology analysis can be time consuming for cytopathologists, but the burden can be reduced through automatic malignancy detection. The main challenge in the automation process is pixel-wise labeling. We proposed WeakSegNet, a new model that addresses the challenge of semantic segmentation in low-magnification images by utilizing only four images with pixel-wise labels. WeakSegNet combines unsupervised, few-shot, and weakly supervised learning methods. In the first stage, an unsupervised model, DeepClusterSeg, learns the homogeneous structures from different images. The few-shot method uses only four images with pixel-wise labels to map homogeneous structures to the required classes. The final stage utilized image-level labels to predict precise classes using weakly supervised learning. We conducted our experiments using a dataset from KMC Hospital, MAHE, which consisted of 345 images. We performed 5-fold cross-validation to evaluate the results. Our proposed model achieved promising results, with an F-score of 0.85 and an IoU of 0.81 for the malignant class, surpassing the performance of the standard k-means algorithm with weakly supervised learning (F-scores of 0.65 and an IoU of 0.61). The semantic segmentation of low-magnification images using our approach eliminated 47% of the sub-regions that need to be scanned at high magnification. This innovative approach reduces the workload of cytopathologists and maintains a high accuracy in effusion cytology malignancy detection. © 2013 IEEE.