Where to look next and what to look for

The authors (1996) introduced the use of multidimensional receptive field histograms for probabilistic object recognition. In this paper we reverse the object recognition problem by asking the question "where should we look?", when we want to verify the presence of an object, to track an object or to actively explore a scene. This paper describes the statistical framework from which we obtain a network of salient points for an object. This network of salient points may be used for fixation control in the context of active object recognition.

[1]  Bernt Schiele,et al.  Probabilistic object recognition using multidimensional receptive field histograms , 1996, Proceedings of 13th International Conference on Pattern Recognition.

[2]  Cordelia Schmid,et al.  Combining greyvalue invariants with local constraints for object recognition , 1996, Proceedings CVPR IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[3]  Michael J. Swain,et al.  Promising directions in active vision , 1993, International Journal of Computer Vision.

[4]  Bernt Schiele,et al.  Object Recognition Using Multidimensional Receptive Field Histograms , 1996, ECCV.

[5]  Haim J. Wolfson,et al.  Model-Based Object Recognition by Geometric Hashing , 1990, ECCV.

[6]  B. Schiele,et al.  The Robustness of Object Recognition to Rotation using Multidimensional Receptive Field Histograms , 1996 .