LIAMA中法联合实验室招收实习生，具体内容见下面，有兴趣的同学请将简历发送到联系人邮箱：Franck.Davoine@gmail.com
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Internship proposal: 2012.
Target level: bachelor’s or master's research project.
Effort: not less than 20 hours per week
Remuneration: start from 1500 RMB per month, might be raised up to 2500 RMB per month according to his/her effort and output.
Duration: 6 month or more, start from the end of February.
Contact: Franck DAVOINE (Franck.Davoine@gmail.com), Researcher of the French National Center for Scientific Research (CNRS), Peking University.
(Franck DAVOINE: http://liama.ia.ac.cn/wiki/user:fdavoine:home)

The internship will be done in Beijing, within the project-team " 3D Multimodal Perception and Reasoning " (MPR) of the Sino-French laboratory LIAMA. The candidate will work in the Key Lab. of Machine Perception (MOE) of Peking University. (MPR: http://www.liama.ac.cn/mpr)

Title: Detection and segmentation of planar objects in driving urban scenes.

Keywords: Machine learning, classification/clustering, images, stereo-vision, motion, driving scenes, intelligent vehicles.

Introduction: The extraction of planar surfaces like the road or facades in out-door driving environments is an important topic that can contribute to understand the scene, usually composed of static objects (trees, traffic signs, cars, pedestrians, etc.) and dynamic event (moving objects, interactions between them). Recent studies for road extraction have been proposed based on mono- or stereo-cameras and multi-layer laser sensors for road plan extraction.

Schedule arrangement: We will first propose the candidate to study some state-of-the-art methods for road plane extraction. He will then focus on one of them using stereo disparity maps. In a third time, he will study and develop a method to segment the road area considering a learning approach and a set of features such as intensity, color, orientation of gradients or higher level ones extracted at different scales. The selection of good features is one of the important goals of the project. Segmentation will have to be robust to appearance variations caused by reflections on the road, illumination variations, presence of shadows, physical degradations, marks or paintings, etc. The global system should be computationally efficient, for a possible integration in test-bed vehicles of two laboratories (in Beijing and in Compiegne in France).

Requirements: good English language abilities both written and spoken, knowledge in the fields of machine learning, pattern recognition and/or computer vision. Solid programming skills; the project involves programming in Matlab and C/C++

Location: Key Laboratory of Machine Perception (MOE), Peking University.

Preparation: Franck is now in France, and he'll be back on January 5th. You can send him your CV and motivations by email now, before a possible interview after Spring festival (10 Feb.).
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