SanchitAggarwalSanchit Aggarwal
 
Areas of Interest: Machine Learning, Pattern Recognition, Computer Vision.
 
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Address: CVIT, IIIT-Hyderabad
 
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Personal Home Page: https://sites.google.com/site/researchsanchit/

Publications

  • Sudipto Banerjee, Sanchit Aggarwal, Anoop M. Namboodiri - Fine Pose Estimation of Known Objects in Cluttered Scene Images Proceedings of the 3rd IAPR Asian Conference on Pattern Recognition, 03-06 Nov 2015, Kuala Lumpur, Malaysia. [PDF]

  • Sanchit Aggarwal, Anoop M. Namboodiri and C.V. Jawahar - Estimating Floor Regions in Cluttered Indoor Scenes from First Person Camera View Proceedings of the 22nd International Conference on Pattern Recognition, 24-28 Aug 2014, Stockholm, Sweden. [PDF]

Projects

finepose Fine Pose Estimation of Known Objects in Cluttered Scene Images

People Involved :Sudipto Banerjee, Sanchit Aggarwal, Anoop M. Namdoodiri

 

 

 

 

 

KoustavGhosalKoustav Ghosal

Areas of Interest: Tracking, Content Based Multimedia Retrieval, Subspace Learning, Deep Neural Networks
 
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Address: CVIT, IIIT-H
 
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Personal Home Page: http://researchweb.iiit.ac.in/~koustav.ghosal/

Publications

  • Koustav Ghosal, Ameya Prabhu, Riddhiman Dasgupta, Anoop M. Namboodiri - Learning Clustered Sub-spaces for Sketch-based Image Retrieval Proceedings of the 3rd IAPR Asian Conference on Pattern Recognition, 03-06 Nov 2015, Kuala Lumpur, Malaysia. [PDF]

  • Koustav Ghosal, Anoop M. Namboodiri - A Sketch-Based Approach to Video Retrieval using Qualitative Features Proceedings of the Ninth Indian Conference on Computer Vision, Graphics and Image Processing, 14-17 Dec 2014, Bangalore, India. [PDF]

Projects

spiral1 Motion Trajectory Based Video Retrieval

People Involved : Koustav Ghosal & Anoop Namboodiri

Extracting robust motion trajectories from videos is an active problem in Computer Vision. The task is more challenging in unconstrained videos in presence of dynamic background, blur, camera motion and affine irregularities. But once extracted, one can retrieve the videos based on them by giving an online (temporal) sketch as a query. But different individuals have different perception about motion trajectories and hence sketch differently. Through this work we are trying to extract robust trajectories from unconstrained videos and then model the sketch in a way so that the perceptual variability among different sketches is removed.