Paper Info
Title
Multiple Viewpoint Recognition and Localization
Abstract
This paper presents a novel approach for labeling objects based on multiple spatially-registered images of a scene. We argue that such a multi-view labeling approach is a better fit for applications such as robotics and surveillance than traditional object recognition where only a single image of each scene is available. To encourage further study in the area, we have collected a data set of well-registered imagery for many indoor scenes and have made this data publicly available. Our multi-view labeling approach is capable of improving the results of a wide variety of image-based classifiers, and we demonstrate this by producing scene labelings based on the output of both the Deformable Parts Model of [1] as well as a method for recognizing object contours which is similar to chamfer matching. Our experimental results show that labeling objects based on multiple viewpoints leads to a significant improvement in performance when compared with single image labeling.
Year
DOI
Venue
2010
10.1007/978-3-642-19315-6_36
Asian Conference on Computer Vision
Keywords
Field
DocType
multiple viewpoint recognition,traditional object recognition,object contour,novel approach,deformable parts model,multiple viewpoint,better fit,multiple spatially-registered image,indoor scene,single image,object recognition
Computer vision,Null Object pattern,Pattern recognition,Chamfer matching,Viewpoints,Computer science,Artificial intelligence,Robotics,Cognitive neuroscience of visual object recognition,Image labeling
Conference
Volume
ISSN
Citations 
6492
0302-9743
12
PageRank 
References 
Authors
0.85
22
5
Name
Order
Citations
PageRank
Scott Helmer117611.49
David Meger240332.90
Marius Muja31335.85
James J. Little42430269.59
D. G. Lowe5157181413.60