In this paper, a geometric approach for global selflocalization based on a world-model and active stereo vision is introduced. The method uses class specific object recognition a...
David Israel Gonzalez-Aguirre, Tamim Asfour, Eduar...
We present a novel approach for unsupervised discovery of repetitive objects from 3D point clouds. Our method assumes that objects are geometrically consistent, and uses multiple o...
This paper introduces a novel iterative approach to estimating local phase coherence in situations characterized by low signal-to-noise ratios. Local phase coherence is used for a...
— This paper presents a general framework for multi-sensor object recognition through a discriminative probabilistic approach modelling spatial and temporal correlations. The alg...
We study unsupervised learning of occluding objects in images of visual scenes. The derived learning algorithm is based on a probabilistic generative model which parameterizes obj...