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{Characterization of 3-D Volumetric Probabilistic Scenes for Object Recognition}


Conference Paper


This paper presents a new volumetric representation for categorizing objects in large-scale 3-D scenes reconstructed from image sequences. This work uses a probabilistic volumetric model (PVM) that combines the ideas of background modeling and volumetric multi-view reconstruction to handle the uncertainty inherent in the problem of reconstructing 3-D structures from 2-D images. The advantages of probabilistic modeling have been demonstrated by recent application of the PVM representation to video image registration, change detection and classification of changes based on PVM context. The applications just mentioned, operate on 2-D projections of the PVM. This paper presents the first work to characterize and use the local 3-D information in the scenes. Two approaches to local feature description are proposed and compared: 1) features derived from a PCA analysis of model neighborhoods; and 2) features derived from the coefficients of a 3-D Taylor series expansion within each neighborhood. The resulting description is used in a bag-of-features approach to classify buildings, houses, cars, planes, and parking lots learned from aerial imagery collected over Providence, RI. It is shown that both feature descriptions explain the data with similar accuracy and their effectiveness for dense-feature categorization is compared for the different classes. Finally, 3-D extensions of the Harris corner detector and a Hessian-based detector are used to detect salient features. Both types of salient features are evaluated through object categorization experiments, where only features with maximal response are retained. For most saliency criteria tested, features based on the determinant of the Hessian achieved higher classification accuracy than Harris-based features.

Author(s): Restrepo, M I and Mayer, B A and Ulusoy, A O and Mundy, J L
Book Title: Selected Topics in Signal Processing, IEEE Journal of
Volume: 6
Number (issue): 5
Pages: 522--537
Year: 2012
Month: September

Department(s): Perceiving Systems
Bibtex Type: Conference Paper (inproceedings)
Paper Type: Journal

DOI: 10.1109/JSTSP.2012.2201693
Attachments: pdf


  title = {{Characterization of 3-D Volumetric Probabilistic Scenes for Object Recognition}},
  author = {Restrepo, M I and Mayer, B A and Ulusoy, A O and Mundy, J L},
  booktitle = {Selected Topics in Signal Processing, IEEE Journal of},
  volume = {6},
  number = {5},
  pages = {522--537},
  month = sep,
  year = {2012},
  month_numeric = {9}