Object Shapes from Regular Curves through Sparse Representations

Robotics coupled with image processing algorithms have led to more advanced control systems in varied applications. Object detection systems are used in multiple places like warehouses where detecting the object faster, makes efficient warehouse management. In this work, an object detection algorithm which uses shape as an object representation is presented. The algorithm uses semi-local contour grouping which is based on gradient maps generated from G-Let filters. The algorithm offers a computationally simpler solution as the original gradient map is reduced to a sparse representation using different thresholding techniques. The sparse representation of the object is modeled as a neighbor graph and the shape is constructed using alpha-hull of the nearest neighbor graph. The construction of a gradient map and working with its local curvatures simplifies the shape detection problem, from building hierarchical structures of local-to-global contour relationships to nearest neighbor calculations on a graph and fixing their boundary.