As robots and other intelligent agents move from simple environments and problems to more complex, unstructured settings, manually programming their behavior has become …
As a fundamental and critical task in various visual applications, image matching can identify then correspond the same or similar structure/content from two or more images. Over the …
Comparing probability distributions is a fundamental problem in data sciences. Simple norms and divergences such as the total variation and the relative entropy only compare …
Seeking reliable correspondences between two feature sets is a fundamental and important task in computer vision. This paper attempts to remove mismatches from given putative …
Before the deep learning revolution, many perception algorithms were based on runtime optimization in conjunction with a strong prior/regularization penalty. A prime example of this …
D Pathak, P Mahmoudieh, G Luo… - Proceedings of the …, 2018 - openaccess.thecvf.com
The current dominant paradigm for imitation learning relies on strong supervision of expert actions to learn both'what'and'how'to imitate. We pursue an alternative paradigm wherein an …
Non‐rigid registration computes an alignment between a source surface with a target surface in a non‐rigid manner. In the past decade, with the advances in 3D sensing …
Feature matching, which refers to establishing reliable correspondences between two sets of feature points, is a critical prerequisite in feature-based image registration. This paper …
Feature matching, which refers to establishing reliable correspondence between two sets of features (particularly point features), is a critical prerequisite in feature-based registration. In …