This paper shows how it is possible to recognize and localize objects in compressed images. The compression method we choose is based on the extraction of the quincunx multiscale edges. The edges of the object and the scene are both computed, and then matched using the censored Hausdorff distance. This distance is computed by double truncation of the classical Hausdorff distance. The localization is based on a coarse-to-fine method. Robustness to noise and possible occlusions of the objects is shown. This algorithm is fast on a workstation and we have implemented it on a massively parallel computer, demonstrating real-time feasibility.
['Robert Azencott', 'F. Durbin', 'Jose Paumard']
Robust recognition of buildings in compressed large aerial scenes