Fast Color-Based Object Recognition Independent of Position and Orientation
Small mobile robots typically have little on-board processing power for time-consuming vision algorithms. Here we show how they can quickly extract very dense yet highly useful information from color images. A single pass through all pixels of an image serves to segment it into color-dependent regions and to compactly represent it by a short list of the average hues, saturations and color intensities of its regions; all other information is discarded. Experiments with two image databases show that in 90 % of all cases the remaining information is sufficient for a simple weighted voting algorithm to recognize objects shown in query images, independently of position and orientation and partial occlusions.
KeywordsMobile Robot Recognition Rate Recurrent Neural Network Color Information Image Code
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- 1.Williams, R.J., Zipser, D.: Gradient-based learning algorithms for recurrent networks and their computational complexity. In: Back-propagation: Theory, Architectures and Applications. Erlbaum, Hillsdale (1994)Google Scholar
- 4.Tuytelaars, T., Van Gool, L.: Wide baseline stereo matching based on local, affinely invariant regions. In: British Machine Vision Conference (2000)Google Scholar
- 5.Nene, S.A., Nayar, S.K.: A simple algorithm for nearest neighbor search in high dimensions. IEEE Transactions on Pattern Analysis and Machine Intelligence 19 (1997)Google Scholar
- 6.Smith, J.R., Chang, S.-F.: Visualseek: A fully automated content-based image query system. In: ACM Multimedia, pp. 87–98 (1996)Google Scholar
- 7.Shao, H., Svoboda, T., Van Gool, L.: ZuBuD — Zürich buildings database for image based recognition. Technical Report 260, Computer Vision Laboratory, Swiss Federal Institute of Technology (2003), Database downloadable from http://www.vision.ee.ethz.ch/showroom/
- 8.Nene, S., Nayar, S., Murase, H.: Columbia object image library: Coil-100. Technical Report CUCS-006-96, Department of Computer Science, Columbia University (1996)Google Scholar