Object recognition using invariant object boundary representations and neural network models

@article{Bebis1992ObjectRU,
  title={Object recognition using invariant object boundary representations and neural network models},
  author={George Bebis and George Papadourakis},
  journal={Pattern Recognition},
  year={1992},
  volume={25},
  pages={25-44}
}
Abstract Object recognition is an essential part of any high-level computer vision system. In this paper, several approaches for classifying two-dimensional objects which are based on the use of both invariant boundary transformations and artificial neural networks (ANNs) were implemented and compared. Specifically, the centroidal profile, the cumulative angular and the curvature representations were used. Two different ANN learning approaches were considered. The first involved supervised… CONTINUE READING

Citations

Publications citing this paper.
SHOWING 1-10 OF 16 CITATIONS

Silhouette recognition using high-resolution pursuit

VIEW 5 EXCERPTS
CITES METHODS & BACKGROUND
HIGHLY INFLUENCED

Object recognition by sub-scene graph matching

  • Proceedings 2000 ICRA. Millennium Conference. IEEE International Conference on Robotics and Automation. Symposia Proceedings (Cat. No.00CH37065)
  • 2000

Robust shape classi"cation

VIEW 2 EXCERPTS
CITES METHODS & BACKGROUND

A spatiotemporal neural network for recognizing partially occluded objects

  • IEEE Trans. Signal Processing
  • 1998
VIEW 3 EXCERPTS
CITES BACKGROUND & METHODS

Development of an image analysis system with advanced visual technique

  • IGARSS '98. Sensing and Managing the Environment. 1998 IEEE International Geoscience and Remote Sensing. Symposium Proceedings. (Cat. No.98CH36174)
  • 1998