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
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Published in Scientific Reports, 2017
This paper characterizes the complex spatio-temporal dynamics of MT neurons
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Published in Computer Vision and Image Understanding, 2016
This review paper explores the mutual benefits of cognitive and computer vision
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Published in Signal Processing: Image Communication, 2015
We show how a V1-MT neural model can be adapted to handle real sequences.
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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.
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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.
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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
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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.
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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.
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