Bootstrap-based method for occlusion removal in case of multiple sensors

Authors:
Maodong Jiang;Layachi Bentabet
Affiliations:
University of Sherbrooke, Sherbrooke, Canada;Bishop's University, Sherbrooke, Canada
Venue:
SPPRA '07 Proceedings of the Fourth IASTED International Conference on Signal Processing, Pattern Recognition, and Applications
Year:
2007

Citing 6
Cited 0

Digital image processing (2nd ed.)

Digital image processing (2nd ed.)
Degraded Image Analysis: An Invariant Approach

IEEE Transactions on Pattern Analysis and Machine Intelligence
Image inpainting

Proceedings of the 27th annual conference on Computer graphics and interactive techniques
On the Removal of Shadows from Images

IEEE Transactions on Pattern Analysis and Machine Intelligence
An embedded camera lens distortion correction method for mobile computing applications

IEEE Transactions on Consumer Electronics
Region filling and object removal by exemplar-based image inpainting

IEEE Transactions on Image Processing

Quantified Score

Hi-index	0.01

Visualization

Abstract

In this paper, a new method for occlusion removal, in the presence of multiple images describing the same scene, is proposed. Our approach integrates the information from the non occluded sensors into the occluded ones. A data fusion step provides a global clustering of the scene, where each cluster is represented by a Dirichlet distribution. This information is re-expressed in terms of the characteristics of the occluded sensor using the bootstrap technique. In order to preserve local image characteristics, we propose to achieve the restoration using an effective combination of exemplar-based inpainting and moving blocks bootstrap. The effectiveness of our method is demonstrated on synthetic and radar and SPOT images.