In this work, we introduce the notion of

which is a measure of the ambiguity of the posterior distribution produced by a recognition experiment. Higher entropies reflect greater ambiguity.

For the problem at hand, the entropy map is parameterized on a tessellated viewsphere with the object at origin. Off-line during training, image measures, {x}, are sampled at each coordinate of the viewsphere for each object in the database. A Bayesian learning strategy (see

Figure 2: One the left, we can see the tesselated viewsphere about the database object, created during training. At each segment, two motion sweeps lead to the computation of 2 optical flow images. Recognition is performed on each flow image, d, and the result is a discrete posterior distribution, P(O|d), depicting the confidence in each of the objects in the database: A, B, C. The entropy of this distribution, H(P(O|d)), is stored at each location. The resulting entropy map can be very informative in the context of planning gaze for object recognition. It provides a

Pose can be estimated at minimal expense by retaining the location information along with the image measures acquired during training. For example, appearance-based methods can be used to index these measures using the data acquired on-line [Nayar:96]. In fact, the implementation described in

The gaze planning strategy itself must be sufficiently robust to accommodate errors in pose determination and entropy map selection. Errors in the former are accommodated in part by smoothing the entropy map and a strategy that avoids placement in the vicinity of singularities and discontinuities. A partial solution to the selection problem is effected by choosing a next best view that minimizes the entropy on the most likely object hypothesis map, while simultaneously minimizing the entropy on any other likely candidates' maps. Over time the expectation is that confidence in an incorrectly chosen hypothesis will decrease as further evidence is uncovered.

[Eggert:92] D. Eggert, K. Bowyer, C. Dyer, H. Christensen, and D. Goldgof, "The scale space aspect graph" In

[Kriegman:89] D. Kriegman and J. Ponce, "Computing exact aspect graphs of curved objects: Solids of revolution", In

[Nayar:96] S.K. Nayar, H. Murase, and S.A. Nene,

**A poster presentation on this
topic was given at the Seventh IEEE International Conference on Computer
Vision, Kerkyra, Greece, Sept. 1999. **

**A presentation related to this topic was given at the
Second IEEE Workshop on Perception for Mobile Agents, in association with the
1999 IEEE Computer Society Conference on Computer Vision and Pattern
Recognition, Fort Collins, Colorado, June 1999.
**