Faculty Publications

Now showing 1 - 4 of 4

Guided depth image reconstruction from very sparse measurements
(SPIE spie@spie.org, 2018) Balure, C.S.; Bhavsar, A.; Ramesh Kini, M.
Depth images captured from modern depth cameras generally suffer from low spatial resolution, noise, and missing regions. These kinds of images cannot be used directly in applications related to depth images, e.g., robot navigation, 3DTV, and augmented reality, which basically need high-resolution input images with no noise o missing regions to function properly. To address the problem of low spatial resolution, noise degradation, and missing regions in depth images, we propose methods based on a guidance color image for depth reconstruction (DR) from sparse depth inputs and depth image super-resolution (SR). We also suggest a scenario wherein these problems can be integrated and addressed simultaneously. Further, we also demonstrate applications of the proposed approach for depth image denoising and depth image inpainting. In our approach, the guidance color image is used for obtaining the segment cues by applying mean-shift (MS) or simple linear iterative clustering (SLIC) segmentation on it. These strong segment cues help in aiding the DR and SR problems by considering the corresponding segments in the input depth image, and estimate the unknown pixels by either plane fitting or median filling approaches. Furthermore, we explore both direct and pyramidal (hierarchical) approaches for SR and DR-SR for higher upsampling factor. As such, our approaches are relatively simpler than some of the contemporary methods, yet the experimental results of the proposed methods show superior performance as compared with some other state-of-the-art DR and SR methods. © 2018 SPIE and IS&T.
AMMDAS: Multi-modular generative masks processing architecture with adaptive wide field-of-view modeling strategy
(Institute of Electrical and Electronics Engineers Inc., 2020) Desanamukula, V.S.; Chilukuri, P.K.; Padala, P.; Padala, P.; Pvgd, P.R.
The usage of transportation systems is inevitable; any assistance module which can catalyze the flow involved in transportation systems, parallelly improving the reliability of processes involved is a boon for day-to-day human lives. This paper introduces a novel, cost-effective, and highly responsive Post-active Driving Assistance System, which is "Adaptive-Mask-Modelling Driving Assistance System" with intuitive wide field-of-view modeling architecture. The proposed system is a vision-based approach, which processes a panoramic-front view (stitched from temporal synchronous left, right stereo camera feed) & simple monocular-rear view to generate robust & reliable proximity triggers along with co-relative navigation suggestions. The proposed system generates robust objects, adaptive field-of-view masks using FRCNN+Resnet-101_FPN, DSED neural-networks, and are later processed and mutually analyzed at respective stages to trigger proximity alerts and frame reliable navigation suggestions. The proposed DSED network is an Encoder-Decoder-Convolutional-Neural-Network to estimate lane-offset parameters which are responsible for adaptive modeling of field-of-view range (1570-2100) during live inference. Proposed stages, deep-neural-networks, and implemented algorithms, modules are state-of-the-art and achieved outstanding performance with minimal loss(L{p, t}, L?, LTotal) values during benchmarking analysis on our custom-built, KITTI, MS-COCO, Pascal-VOC, Make-3D datasets. The proposed assistance-system is tested on our custom-built, multiple public datasets to generalize its reliability and robustness under multiple wild conditions, input traffic scenarios & locations. © 2020 Institute of Electrical and Electronics Engineers Inc.. All rights reserved.
Photonics radar based LSS targets’ postures’ m-D and cadence frequency imaging using empirical wavelet transform technique
(Taylor and Francis Ltd., 2024) Akhter, N.; Raj, A.A.B.; Krishnan, K.
Developing a photonics radar to detect the combined multiple simultaneous-behaviours/postures of low-slow-small (LSS) aerial targets and appropriately extracting their Doppler profile have become significant to enable air surveillance/security and guidance for unmanned aviation. A CW photonic radar of 5.3 GHz, and an empirical wavelet transform (EWT) based radar signal processing algorithm are developed and their sensing/measurement capabilities are tested with representative LSS target models: two/three blades propeller systems, a warhead-like cone-shaped target and a bionic bird. Open field experiments with different simultaneous-behaviours of these LSS targets are conducted, and performance validation of radar, in terms of micro-Doppler signature extractions, cadence frequency imaging and targets' behavioural parameters measurement, is analysed, quantitatively investigated and the results are presented. All the measurement results keep an average of 99% of agreement with the standard measurement values that evidence the compatibility of developed photonics radar for precision sensing of multiple simultaneous-behaviours/postures of LSS aerial targets. © 2023 The Royal Photographic Society.
Haar wavelet-based Galerkin method with its feasibility, consistency, and application to unmanned vehicle navigation around moving obstacles
(Elsevier Ltd, 2025) Madankar, S.R.; Setia, A.; M, M.; Agarwal, R.P.
In this study, we propose a novel Haar wavelet-based Galerkin method to solve nonlinear optimal control problems with applications to unmanned vehicle navigation. The method addresses the critical challenge of optimizing energy consumption while ensuring safe navigation in dynamic environments with multiple moving obstacles. By leveraging the computational efficiency and scalability of Haar wavelets, combined with the robustness of the Galerkin approach, we demonstrate convergence to the optimal solution under feasibility and consistency conditions. Comprehensive numerical simulations, including diverse and complex obstacle scenarios, validate the method's practicality. Through detailed trajectory, speed, and direction analyses, we highlight the approach's ability to adapt to real-world navigation challenges, making it a promising tool for autonomous system optimization. © 2025 European Control Association

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