Section: New Results

Pattern recognition and statistical learning

Machine identification of biological shapes

Participants : Asma Rejeb Sfar, Donald Geman, Nozha Boujemaa.

Stored images of biological objects are accumulating at a staggering rate due to new sensor technologies, expanding use in medical diagnostics, web-based search engines and growing demands for web-based services in traditional sciences such as botany. These developments have been accompanied by an increasing demand for the automated analysis of these data, such as counting cell types, detecting lesions and other abnormalities in medical images, and identifying botanical shapes.

All these tasks have one feature in common: massive diversity among the shapes. Indeed, such shapes display enormous within-class variation and are generally highly deformable. Also, they often exhibit a hierarchical organization resulting from evolutionary processes.

There is currently no existing methodology in image analysis and computer vision which can be applied to a multi-class shape recognition problem of this complexity. Consequently, there is a need for a new, generic methodology for categorizing hierarchically-structured families of deformable shapes, particularly when both the number of categories and the within-category variation are very large.

We proposed a coarse-to-fine (CTF) approach in both shape representation and image parsing. The representation is hierarchical in both class and pose.

We focused on botanical shapes, specifically categorizing simple leaves according to species. So, we determined a suitable representation for the pose of a simple leaf and designed and tested a two-stage pose detector. Then, we constructed classifiers based on the plant taxonomy.

Results will be submitted for publication.