Biomimetic Pattern Recognition Research Articles

An approach for moving object recognition based on BPR and CI

A recognition and classification method of multiple moving objects in traffic based on the combination of the Biomimetic Pattern Recognition (BPR) and Choquet Integral (CI) is proposed. The recognition process consists of three stages. At the first stage, vehicles and pedestrians are detected in video images and the area, the shape and the velocity features are obtained by classical methods. At the second stage, BPR is used to classify the Zernike moments extracted at the first stage. At the last stage, CI is then adopted for multi-features fusion based on the output of BPR, and the area and the velocity features obtained at the first stage to improve the recognition accuracy. Experiment results show that this approach is efficient.

Geometrical learning, descriptive geometry, and biomimetic pattern recognition

Studies on learning problems from geometry perspective have attracted an ever increasing attention in machine learning, leaded by achievements on information geometry. This paper proposes a different geometrical learning from the perspective of high-dimensional descriptive geometry. Geometrical properties of high-dimensional structures underlying a set of samples are learned via successive projections from the higher dimension to the lower dimension until two-dimensional Euclidean plane, under guidance of the established properties and theorems in high-dimensional descriptive geometry. Specifically, we introduce a hyper sausage like geometry shape for learning samples and provides a geometrical learning algorithm for specifying the hyper sausage shapes, which is then applied to biomimetic pattern recognition. Experimental results are presented to show that the proposed approach outperforms three types of support vector machines with either a three degree polynomial kernel or a radial basis function kernel, especially in the cases of high-dimensional samples of a finite size.