Wednesday, June 22, 2011

K. A. Raftopoulos: Visual Pathways for Shape Abstraction

Konstantinos A. Raftopoulos gave a presentation called "Visual Pathways for Shape Abstraction" on Friday 17.6.2011. The paper was written by him and Stefanos D. Kollias, both from National Technical University of Athens. The speech presented first the neuroscience background of shape recognition in cortical neurons which has motivated this research and all related work. The cortical neurons have orientation specific receptive fields (RF) which enables them to detect boundaries. How this builds up the perception of lines and curves is still an open question, but it hasn't stopped trials with experimental shape recognition algorithms build along on neural networks.

Raftopoulos and Kollias propose a method which combines the skeleton abstraction of the shape with curvature information. This curvature-skeleton conveys both local and global shape information, which results in improved recognition capability of neural network classifiers. The method builds up on several layers of neurons which try to imitate the boundary detection of cortical layers. Layers of perceptrons are used in parallel calculation to determine the edges and then the curvature. But with this novel work they have added an additional layer which combines the high points of curvature with the medial axis points of the shape.

The experimental results were conducted by training a network of two hidden layers to classify 2-D images into two categories. They used 500 grayscale images of hands and rabbits from KIMIA shilouette database which the network could classify correctly. Then the generalization ability was tested with applying partial occlusion, deformations and missing parts to 50 images. The recognition of these deformed images is usually a very difficult task but their network could achieve a correct recognition of 91.6 %. A network without the curvature-skeleton information could achieve only 61.5 %.

This presentation/paper was really intriguing to myself, especially because it incorporated so strong biological connections to the model with good results. Only shorthand is that they really hadn't compared (or just didn't show us) their methology to other state-of-the-art image recognition processes. Also using just two image categories seems to be tentativeness although the method seems very promising.

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