Predicting object occupancy on the floor from RGBD images of indoor scenes

Bhobe, Sujay

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https://hdl.handle.net/2142/44195 Copy

Description

Title

Predicting object occupancy on the floor from RGBD images of indoor scenes

Author(s)

Bhobe, Sujay

Issue Date

2013-05-24T21:53:54Z

Director of Research (if dissertation) or Advisor (if thesis)

Hoiem, Derek W.

Department of Study

Electrical & Computer Eng

Discipline

Electrical & Computer Engr

Degree Granting Institution

University of Illinois at Urbana-Champaign

Degree Name

M.S.

Degree Level

Thesis

Keyword(s)

• object occupancy
• graph cuts
• alpha expansion
• label prediction
• overhead view
• scene understanding
• computer vision

Abstract

This thesis presents an approach to predict the occupied area on the floor in an image of an indoor scene. The goal is to be able to obtain navigable areas even in cluttered indoor environments. This algorithm could be used in the field of robotics where robots need to navigate through a room while being mindful of the surrounding objects. The results are quite close to the ground truth, as exemplified by the false positive, false negative, precision and recall rates. Using this algorithm improves the label predictions.

Graduation Semester

2013-05

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http://hdl.handle.net/2142/44195

Copyright and License Information

Copyright 2013 Sujay Uday Bhobe

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