Convex Segments for Convex Objects Using DNN Boundary Tracing and Graduated Optimization

Image segmentation often involves objects of interest that are biologically known to be convex shaped. While typical deep-neural-networks (DNNs) for object segmentation ignore object properties relating to shape, the DNNs that employ shape information fail to enforce hard constraints on shape. We design a brand-new DNN framework that guarantees convexity of the output object-segment by leveraging fundamental geometrical insights into the boundaries of convex-shaped objects. Moreover, we design our framework to build on typical existing DNNs for per-pixel segmentation, while maintaining simplicity in loss-term formulation and maintaining frugality in model size and training time. Results using six publicly available datasets demonstrates that our DNN framework, with little overheads, provides significant benefits in the robust segmentation of convex objects in out-of-distribution images.