In this paper we introduce a representation for object verification and a system for object recognition based on local features, invariant moments, silhouette creation and a 'net' reduction for depth information. The results are then compared with some of the most recent approaches for object detection such as local features and orientation histograms. Additionally, we used depth information to create descriptors that can be used for 3D verification of detected objects. Moments are computed from a 3D set of points which are arranged to create a descriptive object model. This information showed to be of matter in the decision whether the object is present within the analyzed image segment, or not. © 2013 Springer-Verlag Berlin Heidelberg.
CITATION STYLE
Villavicencio, L., Lopez-Franco, C., Arana-Daniel, N., & Lopez-Franco, L. (2013). 3D representation for object detection and verification. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 7914 LNCS, pp. 54–63). https://doi.org/10.1007/978-3-642-38989-4_6
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