Boosting objectness: Semi-supervised learning for object detection and segmentation in multi-view images

Abstract

This paper presents a method to detect and segment recurring object from multi-view images. Given a sequence of images of an object captured by multiple cameras, the method firstly detects sparse object-like regions utilizing generic region proposals. We propose a semi-supervised framework to exploit both appearance cues learned from rudimentary detections of object-like regions, and the intrinsic geometric structures within multi-view data. This framework generates a diverse set of object proposals in all views which underpins a robust object segmentation method to handle objects with complex shape and topologies, as well as scenarios where the object and background exhibit similar color distributions.