Contour Based Image Segmentation

Artificial intelligence and deep learning systems used for image analysis have a significant limitation: there is no way to explain how their decisions are made. Additionally, deep learning systems are heavily dependent upon RGB color which is highly variable necessitating the need for thousands of training vectors. Vy has developed a powerful set of algorithms collectively called Shape Based Modeling Segmentation (SBMS.) We apply transparent and auditable mathematical models (Bézier curves and decision trees) to collect hard data from visual imagery that significantly increases the speed and accuracy of object recognition. The premise of this project is that by combining deep learning with SBMS abstraction a synergistic effect will result in improved performance in convergence speed (number of training vectors) and improved accuracy, as reflected in the precision of object classification and in correct detection of edge location. In Phase I of this project we achieved an order of magnitude improvement in convergence for three object classes (planes, trains, and cars) resulting in the system learning more quickly with fewer training examples. In Phase II, we intend to develop a generalized approach for many object classes that is suitable for commercial development.