We describe a general method for building cascade classifiers from part-based deformable models such as pictorial structures. We focus primarily on the case of star-structured mod...
Pedro Felzenszwalb, Ross Girshick, David McAlleste...
We propose a novel framework for 3D reassembly, the task of assembling a solid object from its broken pieces. The primary challenge in this under-explored problem is to robustly e...
Devi Parikh, Rahul Sukthankar, Tsuhan Chen, Mei Ch...
Abstract— This paper discusses the generation of informationrich, arbitrarily-large synthetic data sets which can be used to (a) efficiently learn tests that correlate a set of ...
Haralampos-G. D. Stratigopoulos, Salvador Mir, Yio...
Abstract. Reasoning on programs and automated deduction often require the manipulation of in nite sets of objects. Many formalisms have been proposed to handle such sets. Here we d...
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. ...