Research
My research interests primarily revolve around computer vision applications, strongly focusing on using generative models for downstream tasks such as
crowd counting, crowd localization, and monocular 3D object detection.
I am particularly interested in exploring the potential of diffusion models to enhance the accuracy and efficiency of these tasks, especially when applied to monocular images.
My overarching goal is to contribute to the advancement of computer vision techniques that can handle complex scenarios involving crowds and vehicles, with an emphasis on noise reduction,
localization, and object detection.
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News
- March, 2024: One Paper accepted at IEEE FG'24.
- February, 2024: Two Papers accepted at CVPR'24.
- May, 2023: One Paper accepted by JFST.
- September, 2022: Joined as a PhD student at Vision & Image Understanding Lab, Johns Hopkins University.
- January, 2022: One Paper is Accepted by IEEE Access.
- December, 2021: One Paper is Accepted by IEEE JSTARS.
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Selected Publications
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CrowdDiff: Multi-hypothesis Crowd Density Estimation using Diffusion Models
Authors : Yasiru Ranasinghe, Nithin Gopalakrishnan Nair, Wele Gedara Chaminda Bandara, and Vishal M. Patel
CVPR, 2024  
The paper proposes a novel approach to perform crowd counting with multi-hypothesis aggregation using denoising diffusion probabilistic models. The approach outperforms existing state-of-the-art methods for crowd counting.
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MonoDiff: Monocular 3D Object Detection and Pose Estimation with Diffusion Models
Authors : Yasiru Ranasinghe, Deepti Hegde, and Vishal M. Patel
CVPR, 2024  
The paper proposes using diffusion models to perform monocular 3D object detection and pose estimation. MonoDiff
does not require additional modalities to generate intermediate representations to produce box parameters.
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Crowd Detection via Point Localization with Diffusion Models
Authors : Yasiru Ranasinghe, and Vishal M. Patel
IEEE FG, 2024  
The paper proposes using diffusion models to localize crowd in images and count the number of people in a scene.
The proposed method generates the head locations as a generative task without a separate detector for point proposals.
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Transmittance Multispectral Imaging for Reheated Coconut Oil Differentiation
Authors : DYL Ranasinghe , HK Weerasooriya, S Herath, MP Bandara Ekanayake, HMVR Herath, GMRI Godaliyadda, and Terrence Madhujith
IEEE Access, 2022  
The article process a novel non invasive method for food quality analysis, specifically in terms of adulteration, using multispectral images and statistical signal processing and image processing. The proposed method yielded statistically significant results and with significant practical implications.
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Constrained Nonnegative Matrix Factorization for Blind Hyperspectral Unmixing Incorporating Endmember Independence
Authors : EMMB Ekanayake, HMHK Weerasooriya, DYL Ranasinghe , S Herath, B Rathnayake, GMRI Godaliyadda, MPB Ekanayake, and HMVR Herath
IEEE Journal of Seleted Topics in Applied Earth Observation and Remote Sensing, 2021  
The paper proposes a novel algorithm to extract endmembers and abundances of hyperspectral remote sensing data. We introduced a regularizer for the nonnegative matrix factorization which improves independence of endmember statistics. The algorithm illustrated superior performance interms of endmber extraction from hyperspectral data.
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Convolutional Autoencoder for Blind Hyperspectrl Unmixing
Authors : Yasiru Ranasinghe, Sanjaya Herath, Kavinga Weerasooriya, Mevan Ekanayake, Roshan Godaliyadda, Parakrama Ekanayake, and Vijitha Herath
IEEE International Conference on Industrial and Information Systems, 2020  
The paper process a convolutional autoencoder to realize the nonnegative matrix factorization using deep learning architectures. The proposed methods is applied to extract endmembers and abundances of hyperspectral remote sensing data. The proposed method produced state-of-the-art results on abundance estimation and competitive results in terms of endmember extraction.
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Hyperspectral Imaging Based Method to Identify Potential Limestone Deposis
Authors : DYL Ranasinghe, HMS Lakmal, HMHK Weerasooriya, EMMB Ekanayake, GMRI Godaliyadda, HMVR Herath, and MPB Ekanayake
IEEE International Conference on Industrial and Information Systems, 2019  
The paper proposed an algorithm to determine the availability of surface limestone using hyperspectral satellite imagery of an area. We incorporate traditional image and signal processing, and statistical data analysis techniques in the algorithm. Further, we generated a self-supervisied representation for the hyperspectral signature of limestone in the absence of a groundtruth to improve classification accuracy and spatial continuity of the probability map.
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Template taken from here. Last updated October 2023.
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