Abstract
Visual attention refers to the ability of a vision system to rapidly detect visually salient locations in a given scene. On the other hand, the selection of robust visual landmarks of an environment represents a cornerstone of reliable vision-based robot navigation systems. Indeed, can salient scene locations provided by visual attention be useful for robot navigation? This work investigates the potential and effectiveness of the visual attention mechanism to provide pre-attentive scene information to a robot navigation system. The basic idea is to detect and track the salient locations, or spots of attention by building trajectories that memorize the spatial and temporal evolution of these spots. Then, a persistency test, which is based on the examination of the lengths of built trajectories, allows the selection of good environment landmarks. The selected landmarks can be used for feature-based localization and mapping systems which helps mobile robot to accomplish navigation tasks.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Julesz, B., Bergen, J.: Textons, the fundamental elements in preattentive vision and perception of textures. Bell System Technical Journal 62(6), 1619–1645 (1983)
Wolfe, J.M.: Guided search 2.0: A revised model of visual search. Psychonomic Bulletin & Review 1, 202–238 (1994)
Tsotsos, J.K., Culhane, S.M., Wai, W.Y.K., Lai, Y.H., Davis, N., Nuflo, F.: Modeling visual attention via selective tuning. Artificial Intelligence 78, 507–545 (1995)
Koch, C.h., Ullman, S.: Shifts in selective visual attention: Towards the underlying neural circuitry. Human Neurobiology 4, 219–227 (1985)
Itti, L., Koch, Ch., Niebur, E.: A model of saliency-based visual attention for rapid scene analysis. IEEE Transactions on Pattern Analysis and Machine Intelligence (PAMI) 20(11), 1254–1259 (1998)
Ouerhani, N., Hugli, H.: Real-time visual attention on a massively parallel SIMD architecture. International Journal of Real Time Imaging 9(3), 189–196 (2003)
Ouerhani, N., Bracamonte, J., Hugli, H., Ansorge, M., Pellandini, F.: Adaptive color image compression based on visual attention. In: International Conference on Image Analysis and Processing (ICIAP 2001), pp. 416–421. IEEE Computer Society Press, Los Alamitos (2001)
Ouerhani, N., Hugli, H.: Maps: Multiscale attention-based presegmentation of color images. In: Griffin, L.D., Lillholm, M. (eds.) Scale-Space 2003. LNCS, vol. 2695, pp. 537–549. Springer, Heidelberg (2003)
Davison, A.J.: Mobile Robot Navigation Using Active Vision. PhD thesis, University of Oxford, UK (1999)
Se, S., Lowe, D., Little, J.: Global localization using distinctive visual features. International Conference on Intelligent Robots and Systems. In: IROS, pp. 226–231 (2002)
Clark, J.J., Ferrier, N.J.: Control of visual attention in mobile robots. In: IEEE Conference on Robotics and Automation, pp. 826–831 (1989)
Brunnstrom, K., Eklundh, J.O., Uhlin, T.: Active fixation for scene exploration. International Journal of Computer Vision 17, 137–162 (1994)
Itti, L.: Toward highly capable neuromorphic autonomous robots: beobots. In: SPIE 47 Annual International Symposium on Optical Science and Technology, vol. 4787, pp. 37–45 (2002)
Dissanayakeand Gamini, M.W.M., Newman, P., Clark, S., Durrant-Whyte, H.F., Csorba, M.: A solution to the simultaneous localization and map building (slam) problem. IEEE Transactions on Robotics and Automation 17, 229–241 (2001)
Lowe, D.: Distinctive image features from scale-invariant keypoints. International Journal of Computer Vision 60(2), 91–110 (2004)
Argyros, A.A., Bekris, C., Orphanoudakis, S.: Robot homing based on corner tracking in a sequence of panoramic images. In: Computer Vision and Pattern Recognition Conference (CVPR), pp. 11–13 (2001)
Harris, C.G., Stephens, M.: A combined corner and edge detector. In: Fourth Alvey Vision Conference, pp. 147–151 (1988)
Mikolajczyk, K., Schmid, C.: An affine invariant interest point detector. In: Heyden, A., Sparr, G., Nielsen, M., Johansen, P. (eds.) ECCV 2002. LNCS, vol. 2350, pp. 128–142. Springer, Heidelberg (2002)
Burt, P.J., Adelson, E.H.: The laplacian pyramid as a compact image code. IEEE Transactions on Communication 31(4), 532–540 (1983)
Tapus, A., Tomatis, N., Siegwart, R.: Topological global localization and mapping with fingerprint and uncertainty. International Symposium on Experimental Robotics (2004)
Fox, D., Thrun, S., Dellaert, F., Burgard, W.: Particle filters for mobile robot localization. In: Doucet, A., de Freitas, N., Gordon, N. (eds.) Sequential Monte-Carlo Methods in Practice. Springer, New York (2000)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2005 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Ouerhani, N., Hügli, H., Gruener, G., Codourey, A. (2005). A Visual Attention-Based Approach for Automatic Landmark Selection and Recognition. In: Paletta, L., Tsotsos, J.K., Rome, E., Humphreys, G. (eds) Attention and Performance in Computational Vision. WAPCV 2004. Lecture Notes in Computer Science, vol 3368. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-30572-9_14
Download citation
DOI: https://doi.org/10.1007/978-3-540-30572-9_14
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-24421-9
Online ISBN: 978-3-540-30572-9
eBook Packages: Computer ScienceComputer Science (R0)