The IbyL code is avaible for download here.

I will be at NIPS 2014 Montreal, CA: see you there!

About me

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.

Inference by Learning: Speeding-up Graphical model inference

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.

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Global Matching via Energy Pyramid: Disparity map estimation from large scale stereo-pairs

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.

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