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projects:year9:9a.014.sbu

9a.014.SBU - Object Recognition Under Various Illumination Conditions

Project - Summary

PROJECT GOALS & NOVELTY OF APPROACH

  • Build a robust and real-time object recognition model that generalizes well under various challenging illumination conditions such as shadows or non-uniform lighting.
  • Take physical properties of the objects and scenes, such as 3D geometry, material, light color into account to remove the illumination factors from an image.
  • Collect a dataset of large number of object classes with non-uniform illumination.
  • Design and validate an object recognition neural network which incorporates the physical properties of objects.

Project - Team

Team Member Role Email Phone Number Academic Site/IAB
Dimitris Samaras PI samaras@cs.stonybrook.edu (631) 632-8464 Stony Brook University
Arie Kaufman Co-PI ari@cs.stonybrook.edu (631) 632-8441 Stony Brook University
Sagnik Das Student sadas@cs.stonybrook.edu N/A Stony Brook University
ShahRukh Athar Student sathar@cs.stonybrook.edu (631) 398-2919 Stony Brook University
Shawn Mathew Student shawmathew@cs.stonybrook.ed (516) 306-1876 Stony Brook University
Georgi Georgiev Student ggeorgi@cs.stonybrook.edu N/A Stony Brook University
Jingyi Xu Student jingyixu@cs.stonybrook.edu N/A Stony Brook University
Miroslav Trajkovic Project Mentor Miroslav.Trajkovic@zebra.com N/A Zebra Technologies
Funded by: Zebra Technologies

Project - Novelty of Approach

Stony Brook project will provide convolutional neural network (CNN) framework to be used to recognize items in near real-time for partially obstructed items typically seen in real-world applications across various lighting conditions, while current state of the art employs computer vision techniques.

Project - Deliverables

Deliverables
1 Joint estimation using graphical models and novel shadow cues
2 Inference on not only illumination and cast shadows but also on the scene's geometry
3 Single image shadow detection based on learning

Project - Benefits to IAB

  • Object recognition model with application in real-scenarios where non-uniform illumination effects its performance.
  • Shadow, shading, and illuminant removal framework will be useful to pre-process images for various other tasks.
  • Proposed dataset will be helpful for not only object recognition but also to learn different tasks such as shadow removal, object segmentation, etc.

Project - Documents

projects/year9/9a.014.sbu.txt · Last modified: 2022/08/31 15:37 by sally.johnson