We describe a hierarchical probabilistic model for the detection and recognition of objects in cluttered, natural scenes. The model is based on a set of parts which describe the e...
Erik B. Sudderth, Antonio B. Torralba, William T. ...
We present a framework for learning object representations for fast recognition of a large number of different objects. Rather than learning and storing feature representations s...
This paper explores how to exploit shape information to perform object class recognition. We use a sparse partbased model to describe object categories defined by shape. The spars...
Josephine Sullivan, Oscar M. Danielsson, Stefan Ca...
This paper addresses the problem of recognizing freeform 3D objects in point clouds. Compared to traditional approaches based on point descriptors, which depend on local informati...
Bertram Drost, Markus Ulrich, Nassir Navab, Slobod...
—Inexpensive RGB-D cameras that give an RGB image together with depth data have become widely available. We use this data to build 3D point clouds of a full scene. In this paper,...
Hema Swetha Koppula, Abhishek Anand, Thorsten Joac...