Abstract
In this paper we propose a novel approach for visual object recognition. The main idea is to consider the object recognition task as an active process which is guided by multi-cue attentional indexes, which at the same time correspond to object’s parts. In this method, a visual attention mechanism is carried out. It does not correspond to a different stage (or module) of the recognition process; on the contrary, it is inherent in the recognition strategy itself. Recognition is achieved by means of a sequential search of object’s parts: parts selection depends on the current state of the recognition process. The detection of each part constraints the process state in order to reduce the search space (in the overall feature space) for future parts matching. As an illustration, some results for face and pedestrian recognition are presented.
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
Papageorgiou, C., Oren, M., Poggio, T.: A general framework for object detection. In: ICCV 1998: Proceedings of the Sixth International Conference on Computer Vision, p. 555. IEEE Computer Society Press, Washington, DC, USA (1998)
Schneiderman, H., Kanade, T.: Object detection using the statistics of parts. International Journal of Computer Vision 56, 151–177 (2004)
Serre, T., Wolf, L., Poggio, T.: A new biologically motivated framework for robust object recognition. Technical report (2004)
Viola, P., Jones, M.: Rapid object detection using a boosted cascade of simple features. In: Conference on computer vision and pattern recognition, vol. 1, pp. 511–518
Leung, T., Burl, M., Perona, P.: Finding faces in cluttered scenes using random labelled graph matching. In: fifth Intl. Conf. on Computer Vision, pp. 637–644 (1995)
Burl, M., Weber, M., Perona, P.: A probabilistic approach to object recognition using local photometry and global geometry. In: Burkhardt, H., Neumann, B. (eds.) ECCV 1998. LNCS, vol. 1407, pp. 628–641. Springer, Heidelberg (1998)
Mohan, A., Papageorgiou, C., Poggio, T.: Example-based object detection in images by components. IEEE Transactions on Pattern Analysis and Machine Intelligence 23, 349–361 (2001)
Felzenszwalb, P., Huttenlocher, D.: Pictorial structures for object recognition (2003)
Tsotsos, J., Culhane, S., Yan Key Wai, W., Lai, Y., Davis, N., Nuflo, F.: Modeling visual attention via selective tuning. Artificial intelligence 78, 507–545 (1995)
Takacs, B., Wechsler, H.: A dynamical and multiresolution model of visual attention and its application to facial landmark detection. Computer Vision and Image Understanding 70, 63–73 (1998)
Itti, L., Koch, C., Niebur, E.: A model of saliency-based visual attention for rapid scene analysis. IEEE transactions on pattern analysis and machine intelligence 20, 1254–1259 (1998)
Draper, B.A., Lionelle, A.: Evaluation of selective attention under similarity transformations. Computer vision and image understanding. 100, 152–171 (2005)
Walther, D., Itti, L., Riesenhuber, M., Poggio, T., Koch, C.: Attentional selection for object recognition- a gentle way. In: Second IEEE International Workshop, BMCV, pp. 472–479 (2002)
Sun, Y., Fisher, R.: Object-based visual attention for computer vision. Informatics research report EDI-INF-RR-0213 (June 2004)
Frintrop, S., Rome, E.: (Simulating visual attention for object recognition)
Machrouh, J., Tarroux, P.: Attentional mechanisms for interactive image exploration. EURASIP Journal on Applied Signal Processing 2005, 2391–2396 (2005)
Murphy, M., Torralba, A., Freeman, W.: Using the forest to see the trees: a graphical model relating features, objects, and scenes. In: Advances in Neural Information Processing Systems, vol. 16, MIT Press, Cambridge (2003)
Ramström, O., Christensen, H.: Object detection using background context. In: Procedings of the 17th International Conference on Pattern Recognition (ICPR 2004), IEEE Computer Society Press, Los Alamitos (2004)
Dickinson, S., Christensen, H., Tsotsos, J., Olofsson, G.: Active object recognition integrating attention and viewpoint control. Computer vision and image understanding 67, 239–260 (1997)
Autio, I., Lindgren, K.: Attention-driven parts-based object detection (2004)
Deco, G., Schürmann, B.: A hierarchical neural system with attentional top-down enhancement of the spatial resolution for object recognition. Vision Research 40, 2845–2859 (2000)
Trujillo, N., Chapuis, R., Chausse, F., Blanc, C.: On road simultaneous vehicle recognition and localization by model based focused vision. In: IAPR Conference on Machine Vision Applications 2005,Tsukuba, Japan (2005)
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 2007 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Trujillo, N., Chapuis, R., Chausse, F., Naranjo, M. (2007). Object Recognition: A Focused Vision Based Approach. In: Bebis, G., et al. Advances in Visual Computing. ISVC 2007. Lecture Notes in Computer Science, vol 4842. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-76856-2_62
Download citation
DOI: https://doi.org/10.1007/978-3-540-76856-2_62
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-76855-5
Online ISBN: 978-3-540-76856-2
eBook Packages: Computer ScienceComputer Science (R0)