Authors:
Toru Kotsuka
;
Daisuke Deguchi
;
Ichiro Ide
and
Hiroshi Murase
Affiliation:
Nagoya University, Japan
Keyword(s):
In-vehicle Camera, Temporal Object Removal, Adaptive Reference Image Selection.
Related
Ontology
Subjects/Areas/Topics:
Computer Vision, Visualization and Computer Graphics
;
Image Enhancement and Restoration
;
Image Formation and Preprocessing
;
Image Generation Pipeline: Algorithms and Techniques
Abstract:
In recent years, image inpainting is widely used to remove undesired objects from an image. Especially, the removal of temporal objects, such as pedestrians and vehicles, in street-view databases such as Google Street View has many applications in Intelligent Transportation Systems (ITS). To remove temporal objects, Uchiyama et al. proposed a method that combined multiple image sequences captured along the same route. However, when spatial alignment inside an image group does not work well, the quality of the output image of this method is often affected. For example, large temporal objects existing in only one image create regions that do not correspond to other images in the group, and the image created from aligned images becomes distorted. One solution to this problem is to select adaptively the reference image containing only small temporal objects for spatial alignment. Therefore, this paper proposes a method to remove temporal objects by integration of multiple image sequences
with an adaptive reference image selection mechanism.
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