Image Composition for Object Pop-out

Hongwen Kang, Alexei A. Efros, Martial Hebert, and Takeo Kanade
2nd International IEEE Workshop on 3D Representation for Recognition (3dRR-09), September, 2009.


Download
  • Adobe portable document format (pdf) (8MB)
Copyright notice: This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. These works may not be reposted without the explicit permission of the copyright holder.

Abstract
We propose a new data-driven framework for novel object detection and segmentation, or “object pop-out”. Traditionally, this task is approached via background subtraction, which requires continuous observation from a stationary camera. Instead, we consider this an image matching problem. We detect novel objects in the scene using an unordered, sparse database of previously captured images of the same general environment. The problem is formulated in a new image composition framework: 1) given an input image, we find a small set of similar matching images; 2) each of the matches is aligned with the input by proposing a set of homography transformations; 3) regions from different transformed matches are stitched together into a single composite image that best matches the input; 4) the difference between the input and the composite is used to “pop-out” new or changed objects.

Notes

Text Reference
Hongwen Kang, Alexei A. Efros, Martial Hebert, and Takeo Kanade, "Image Composition for Object Pop-out," 2nd International IEEE Workshop on 3D Representation for Recognition (3dRR-09), September, 2009.

BibTeX Reference
@inproceedings{Kang_2009_6446,
   author = "Hongwen Kang and Alexei A. Efros and Martial Hebert and Takeo Kanade",
   title = "Image Composition for Object Pop-out",
   booktitle = "2nd International IEEE Workshop on 3D Representation for Recognition (3dRR-09)",
   month = "September",
   year = "2009",
}