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Works186 from public data
- Video Processing From Electro-Optical Sensors for Object Detection and Tracking in a Maritime Environment: A Survey493
A survey on maritime object detection and tracking approaches, which are essential for the development of a navigational system for autonomous ships, is presented, followed by an approach-based taxonomy wherein the advantages and limitations of each approach are compared.
- Illuminant estimation for color constancy: why spatial-domain methods work and the role of the color distribution367
It is shown that spatial information does not provide any additional information that cannot be obtained directly from the color distribution and that the indirect aim of spatial-domain methods is to obtain large color differences for estimating the illumination direction.
- Training-Based Spectral Reconstruction from a Single RGB Image222
A training-based method to reconstruct a scene’s spectral reflectance from a single RGB image captured by a camera with known spectral response, based on a radial basis function network that leverages RGB white-balancing to normalize the scene illumination to recover the scene reflectance.
- ElliFit: An unconstrained, non-iterative, least squares based geometric Ellipse Fitting method170
The results show that the proposed ellipse fitting method is quite selective to elliptic shapes only and provides accurate fitting results, indicating potential application in medical, robotics, object detection, and other image processing industrial applications.
- Neural Network Based Country Wise Risk Prediction of COVID-19163
A shallow long short-term memory (LSTM) based neural network to predict the risk category of a country and shows that the proposed pipeline outperforms state-of-the-art methods for data of 180 countries and can be a useful tool for such risk categorization.
- Edge curvature and convexity based ellipse detection method145
The proposed ellipse detection method performs far better than existing methods and is close to the ideal results, with precision, recall, and F-measure, all very close to 1.
- A novel framework for making dominant point detection methods non-parametric94
A theoretical bound of the maximum deviation of pixels obtained by digitization of a line segment is used for constructing a general framework to make most dominant point detection methods non-parametric.
- Object Detection in a Maritime Environment: Performance Evaluation of Background Subtraction Methods81
- Prediction of porosity and thermal diffusivity in a porous fin using differential evolution algorithm71
Two numerical examples provide engineering insight into the problem of designing porous fins using good thermal conductors like aluminum and copper along with the working of DE by using the differential evolution (DE)-based optimization technique.
- Physics-based machine learning for subcellular segmentation in living cells68
A physics-based simulation approach is used to train neural networks to automatically segment subcellular structures despite the optical artefacts, and extensive results on the segmentation of small vesicles and mitochondria in diverse and independent living- and fixed-cell datasets are shown.
- High spatially sensitive quantitative phase imaging assisted with deep neural network for classification of human spermatozoa under stressed condition60
A partially spatially coherent digital holographic microscope (PSC-DHM) for quantitative phase imaging (QPI) is developed in order to distinguish normal sperm cells from sperm cells under different stress conditions such as cryopreservation, exposure to hydrogen peroxide and ethanol.
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- Accurate detection of ellipses with false detection control at video rates using a gradient analysis54
A novel fast and robust ellipse detection method that adopts arcs selection, smart grouping, and repeated utilization of gradient information to significantly reduce the computations otherwise needed without compromising the detection effectiveness.
- Physics-Guided Loss Functions Improve Deep Learning Performance in Inverse Scattering53
New designs of loss functions are proposed which incorporate multiple-scattering based near-field quantities (such as scattered fields or induced currents within domain of interest) to solve electromagnetic inverse scattering problems.
- Single Image Dehazing for a Variety of Haze Scenarios Using Back Projected Pyramid Network51
A novel generative adversarial network architecture for this problem, namely back projected pyramid network (BPPNet), that gives good performance for a variety of challenging haze conditions, including dense haze and inhomogeneous haze.
- A COMPUTATIONAL MODEL OF MACHINE CONSCIOUSNESS51
A physical definition of consciousness is proposed with the hope to model it in intelligent machines and a concept of mental saccades that is useful for explaining the attention-switching and focusing mechanism from computational perspective is proposed.
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- MuSCoWERT: multi-scale consistence of weighted edge Radon transform for horizon detection in maritime images43
This paper presents a novel method called multi-scale consistence of weighted edge Radon transform, abbreviated as MuSCoWERT, which detects the long linear features consistent over multiple scales using multi- scale median filtering of the image followed byRadon transform on a weighted edge map and computing the histogram of the detected linear features.
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- Client Selection in Federated Learning under Imperfections in Environment40
A novel sampling method named the irrelevance sampling technique that selects a subset of clients based on quality and quantity of data on edge devices that achieves 50–80% faster convergence even in highly skewed data distribution in the presence of free riders.
- SoCodeCNN: Program Source Code for Visual CNN Classification Using Computer Vision Methodology40
This research is the first to propose a novel human-inspired approach to automatically convert program source-codes to visual images that could be then utilized for automated classification by visual convolutional neural network (CNN) based algorithm.
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Publication data from OpenAlex, with missing venues and authors filled in from Crossref; citation counts are the higher of OpenAlex and Semantic Scholar, last synced 2026-10-12. One-sentence summaries under some papers are written by Semantic Scholar’s model. Citation counts may be lower than on Google Scholar, which indexes more sources.
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