The IbyL code is avaible for download here.
I will be at NIPS 2014 Montreal, CA: see you there!
I am a PhD student with Ecole des Ponts ParisTech / University Paris-Est and a research analyst with the Geological and Planetary Sciences division at Caltech. You can download a short resume here.
My reserach interests are stereo-vision, discrete optimization, graphical models, and learning.
You can email me at: b co ne jo (at) cal tech (dot) edu
I am looking for a summer intership on one of the following subjects: stereo-vision, high order MRF inference, structured learning, or any engineering project related to computer vision and applied mathematics.
We propose a general and versatile framework that significantly speeds-up graphical model optimization while maintaining an excellent solution accuracy. The proposed approach, refereed as Inference by Learning or in short as IbyL, relies on a multi-scale pruning scheme that progressively reduces the solution space by use of a coarse-to-fine cascade of learnt classifiers. We thoroughly experiment with classic computer vision related MRF problems, where our novel framework constantly yields a significant time speed-up (with respect to the most efficient inference methods) and obtains a more accurate solution than directly optimizing the MRF.
We define a global matching framework based on energy pyramid, the Global Matching via Energy Pyramid (GM-EP) algorithm, which estimates the disparity map from a single stereo-pair by solving an energy minimization problem. We efficiently address this minimization by globally optimizing a coarse to fine sequence of sparse Conditional Random Fields (CRF) directly defined on the energy. This global discrete optimization approach guarantees that at each scale we obtain a near optimal solution, and we demonstrate its superiority over state of the art image pyramid approaches through application to real stereo-pairs. We conclude that multiscale approaches should be build on energy pyramids rather than on image pyramids.