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A list of all the posts and pages found on the site. For you robots out there is an XML version available for digesting as well.
Pages
Kartheek Medathati
Founder of Handybot, building physical AI for semi-structured environments. Research background in biologically inspired vision and perception for embodied & AR systems.
Posts
Smithsonian Open Access
Published:
Embed 3D assets interactively into a webpage.
Paper Corner
Published:
Papers
Semantic Segmentation and Matting
Published:
Introduction
Perceptual Image Quality
Published:
Introduction
Computer Vision with Open Vocabulary
Published:
Introduction
Computational Humor
Published:
Introduction
portfolio
Agent4Agents
A governance substrate for multi-agent and human software development, keeping agentic coding on-track and accountable.
Video Stitch
Create a png image summarizing white board notes from short tutorial videos.
Optical Flow
Collection of resources for dense optical flow estimation.
publications
Synthetic zooming of tomographic images by combination of lattices
Published in 2009 IEEE Nuclear Science Symposium Conference Record (NSS/MIC), 2010
We propose a method for synthetic zooming of tomographic images by applying super resolution technique on reconstructed data via a union of rotated lattices.
Recommended citation: N. Dixit, N. V. Kartheek Medathati, J. Sivaswamy. *Synthetic zooming of tomographic images by combination of lattices.* 2009 IEEE NSS/MIC, pp. 3770–3776. doi:10.1109/NSSMIC.2009.5401886 https://ieeexplore.ieee.org/document/5401886
Robust matching of multi-modal retinal images using radon transform based local descriptor
Published in IHI 10: Proceedings of the 1st ACM International Health Informatics Symposium, 2010
A novel Radon Transform based descriptor is proposed, which is invariant to illumination, rotation and partially to scale.
Recommended citation: Yogesh Babu Bathina, N. V. Kartheek Medathati, Jayanthi Sivaswamy. *Robust matching of multi-modal retinal images using radon transform based local descriptor.* IHI '10, ACM, 2010, pp. 765–770. doi:10.1145/1882992.1883108 https://dl.acm.org/doi/10.1145/1882992.1883108
Local descriptor based on texture of projections
Published in ICVGIP:Proceedings of the Seventh Indian Conference on Computer Vision, Graphics and Image Processing, 2010
In this paper we propose a novel descriptor that better captures shape information by analysing texture of the radon transform
Recommended citation: N. V. Kartheek Medathati, Jayanthi Sivaswamy. *Local descriptor based on texture of projections.* ICVGIP '10, ACM, 2010, pp. 398–404. doi:10.1145/1924559.1924612 https://dl.acm.org/doi/10.1145/1924559.1924612
Detection and discrimination of disease-related abnormalities based on learning normal cases
Published in Pattern Recognition, 2012
In this paper, we observe the key challenges for representation and feature extraction schemes to be met for detection of abnormalities by learning normal cases.
Recommended citation: K. Sai Deepak, N. V. Kartheek Medathati, Jayanthi Sivaswamy. *Detection and discrimination of disease-related abnormalities based on learning normal cases.* Pattern Recognition, 45(10):3707–3716, 2012. doi:10.1016/j.patcog.2012.03.020 https://www.sciencedirect.com/science/article/abs/pii/S0031320312001537
Decoding MT motion response for optical flow estimation: An experimental evaluation
Published in 23rd European Signal Processing Conference (EUSIPCO), 2015
Decoding the motion energies is of natural interest for developing biologically inspired computer vision algorithms for dense optical flow estimation. Here, we address this problem by evaluating four strategies for motion decoding: intersection of constraints, linear decoding through learned weights on MT responses, maximum likelihood and regression with neural network using multi scale-features.
Recommended citation: M. Chessa, N. V. Kartheek Medathati, G. S. Masson, F. Solari, P. Kornprobst. *Decoding MT motion response for optical flow estimation: An experimental evaluation.* 23rd EUSIPCO, 2015, pp. 2241–2245. doi:10.1109/EUSIPCO.2015.7362783 https://ieeexplore.ieee.org/document/7362783
What can we expect from a V1-MT feedforward architecture for optical flow estimation?
Published in Signal Processing: Image Communication, 2015
We show how a V1-MT neural model can be adapted to handle real sequences.
Recommended citation: Fabio Solari, Manuela Chessa, N. V. Kartheek Medathati, Pierre Kornprobst. *What can we expect from a V1-MT feedforward architecture for optical flow estimation?* Signal Processing: Image Communication, 39(B):342–354, 2015. doi:10.1016/j.image.2015.04.006 https://www.sciencedirect.com/science/article/abs/pii/S0923596515000673
Bio-inspired computer vision: Towards a synergistic approach of artificial and biological vision
Published in Computer Vision and Image Understanding, 2016
A review paper exploring the mutual benefits of biological and computer vision — how methods used to understand the visual cortex can make machine vision more robust.
Recommended citation: N. V. Kartheek Medathati, Heiko Neumann, Guillaume S. Masson, Pierre Kornprobst. *Bio-inspired computer vision: Towards a synergistic approach of artificial and biological vision.* Computer Vision and Image Understanding, Volume 150, 2016, Pages 1–30. DOI: 10.1016/j.cviu.2016.04.009 https://doi.org/10.1016/j.cviu.2016.04.009
Recurrent network dynamics reconciles visual motion segmentation and integration.
Published in Scientific Reports, 2017
This paper characterizes the complex spatio-temporal dynamics of MT neurons
Recommended citation: N. V. K. Medathati, J. Rankin, A. I. Meso, P. Kornprobst, G. S. Masson. *Recurrent network dynamics reconciles visual motion segmentation and integration.* Scientific Reports 7, 11270 (2017). doi:10.1038/s41598-017-11373-z https://www.nature.com/articles/s41598-017-11373-z
Towards inferring cognitive state changes from pupil size variations in real world conditions
Published in ETRA: Eye Tracking Research and Applications, 2020
In this paper we study how far we are from estimating cognitive state changes from eye movements in real world environments using a variety of visual search tasks
Recommended citation: Naga Venkata Kartheek Medathati, Ruta Desai, James Hillis. *Towards inferring cognitive state changes from pupil size variations in real world conditions.* ETRA '20 Full Papers, ACM, 2020, Article 22, 1–10. doi:10.1145/3379155.3391319 https://dl.acm.org/doi/10.1145/3379155.3391319
talks
EmoLuck: Seat belt for social networks
Published:
Advancing Human Centered AI
Published:
teaching
Teaching experience 1
Undergraduate course, University 1, Department, 2014
This is a description of a teaching experience. You can use markdown like any other post.
Teaching experience 2
Workshop, University 1, Department, 2015
This is a description of a teaching experience. You can use markdown like any other post.