Object Recognition in Indoor Video Sequences by Classifying Image Segmentation Regions Using Neural Networks
This paper presents the results obtained in a real experiment for object recognition in a sequence of images captured by a mobile robot in an indoor environment. The purpose is that the robot learns to identify and locate objects of interest in its environment from samples of different views of the objects taken from video sequences. In this work, objects are simply represented as an unstructured set of spots (image regions) for each frame, which are obtained from the result of an image segmentation algorithm applied on the whole sequence. Each spot is semi-automatically assigned to a class (one of the objects or the background) and different features (color, size and invariant moments) are computed for it. These labeled data are given to a feed-forward neural network which is trained to classify the spots. The results obtained with all the features, several feature subsets and a backward selection method show the feasibility of the approach and point to color as the fundamental feature for discriminative ability.
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- 1.Pope, A.R.: Model-Based Object Recognition. A survey of recent research., University of British Columbia, Vancouver, Canada, Technical Report 94-04 (January 1994)Google Scholar
- 4.Singh, S., Markou, M., Haddon, J.: Detection of new image objects in video sequences using neural networks. In: Nasrabadi, N.M., Katsaggelos, A.K. (eds.) Proc. SPIE. Applications of Artificial Neural Networks in Image Processing V, vol. 3962, pp. 204–213 (2000)Google Scholar
- 5.Fay, R., Kaufmann, U., Schwenker, F., Palm, G.: Learning object recognition in a neurobotic system. In: Groß, H.-M., Debes, K., Böhme, H.-J. (eds.) 3rd Workshop on SelfOrganization of AdaptiVE Behavior (SOAVE 2004), Fortschritt -Berichte VDI, Reihe 10 Informatik / Kommunikation, vol. 743, pp. 198–209. VDI, Düsseldorf (2004)Google Scholar
- 6.Wang, W., Zhang, A., Song, Y.: Identification of objects from image regions. In: IEEE International Conference on Multimedia and Expo (ICME 2003), Baltimore, July 6-9 (2003)Google Scholar
- 7.Felzenszwalb, P., Huttenlocher, D.: Efficiently computing a good segmentation. In: IEEE Conference on Computer Vision and Pattern Recognition, 98–104 (1998)Google Scholar
- 9.Fiesler, E., Beale, R. (eds.): Handbook of Neural Computation. IOP Publishing Ltd and Oxford University Press (1997)Google Scholar
- 10.Bishop, C.M.: Neural Networks for Pattern Recognition. Oxford University Press, Oxford (1995)Google Scholar
- 11.Rumelhart, D.E., McClelland, J.L., PDP Research Group (eds.): Parallel Distributed Processing: Explorations in the Microstructure of Cognition. MIT Press, Cambridge (1986)Google Scholar
- 12.Romero, E., Sopena, J.M., Navarrete, G., Alquézar, R.: Feature selection forcing overtraining may help to improve performance. In: Proc. Int. Joint Conference on Neural Networks, IJCNN 2003, Portland, Oregon, vol. 3, pp. 2181–2186 (2003)Google Scholar