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. © 2011 Springer-Verlag Berlin Heidelberg.
CITATION STYLE
Helmer, S., Meger, D., Muja, M., Little, J. J., & Lowe, D. G. (2011). Multiple viewpoint recognition and localization. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 6492 LNCS, pp. 464–477). https://doi.org/10.1007/978-3-642-19315-6_36
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