  Slope difference distribution (SDD) is computed for the one-dimensional
curve. It is not only robust to calculate the partitioning point to separate
the curve logically, but also robust to calculate the clustering center of each
part of the separated curve. SDD has been proposed for image segmentation and
it outperforms all existing image segmentation methods. For verification
purpose, we have made the Matlab codes of comparing SDD method with existing
image segmentation methods freely available at Matlab Central. The contour of
the object is similar to the histogram of the image. Thus, feature detection by
SDD from the contour of the object is also feasible. In this letter, SDD
features are defined and they form the sparse representation of the object
contour. The reference model of each object is built based on the SDD features
and then model matching is used for on line object recognition. The
experimental results are very encouraging. For the gesture recognition, SDD
achieved 100% accuracy for two public datasets: the NUS dataset and the
near-infrared dataset. For the object recognition, SDD achieved 100% accuracy
for the Kimia 99 dataset.

Computer Vision and Pattern Recognition;Machine Learning;Image and Video Processing