%0 Journal Article %1 Lucas:2006:DEP:1288263.1288455 %A Lucas, Yves %A Domingues, Antonio %A Driouchi, Driss %A Treuillet, Sylvie %C New York, NY, United States %D 2006 %I Hindawi Publishing Corp. %J EURASIP J. Appl. Signal Process. %K ComputerVision algorithms benchmark evaluation performance statistics to_READ %P 212--212 %R 10.1155/ASP/2006/48012 %T Design of experiments for performance evaluation and parameter tuning of a road image processing chain %U http://dx.doi.org/10.1155/ASP/2006/48012 %V 2006 %X Tuning a complete image processing chain (IPC) is not a straightforward task. The first problem to overcome is the evaluation of the whole process. Until now researchers have focused on the evaluation of single algorithms based on a small number of test images and ad hoc tuning independent of input data. In this paper, we explain how the design of experiments applied on a large image database enables statistical modeling for IPC significant parameter identification. The second problem is then considered: how can we find the relevant tuning and continuously adapt image processing to input data? After the tuning of the IPC on a typical subset of the image database using numerical optimization, we develop an adaptive IPC based on a neural network working on input image descriptors. By testing this approach on an IPC dedicated-to-road obstacle detection, we demonstrate that this experimental methodology and software architecture can ensure continuous efficiency. The reason is simple: the IPC is globally optimized, from a large number of real images and with adaptive processing of input data.

@article{Lucas:2006:DEP:1288263.1288455, abstract = {Tuning a complete image processing chain (IPC) is not a straightforward task. The first problem to overcome is the evaluation of the whole process. Until now researchers have focused on the evaluation of single algorithms based on a small number of test images and ad hoc tuning independent of input data. In this paper, we explain how the design of experiments applied on a large image database enables statistical modeling for IPC significant parameter identification. The second problem is then considered: how can we find the relevant tuning and continuously adapt image processing to input data? After the tuning of the IPC on a typical subset of the image database using numerical optimization, we develop an adaptive IPC based on a neural network working on input image descriptors. By testing this approach on an IPC dedicated-to-road obstacle detection, we demonstrate that this experimental methodology and software architecture can ensure continuous efficiency. The reason is simple: the IPC is globally optimized, from a large number of real images and with adaptive processing of input data.}, acmid = {1288455}, added-at = {2013-10-22T15:59:03.000+0200}, address = {New York, NY, United States}, author = {Lucas, Yves and Domingues, Antonio and Driouchi, Driss and Treuillet, Sylvie}, biburl = {https://www.bibsonomy.org/bibtex/2d6f469541e30dfe068f1909130a7c3a0/alex_ruff}, description = {Design of experiments for performance evaluation and parameter tuning of a road image processing chain}, doi = {10.1155/ASP/2006/48012}, interhash = {ec531ec7c01ea3633c16f589abf6effb}, intrahash = {d6f469541e30dfe068f1909130a7c3a0}, issn = {1110-8657}, issue_date = {01 January}, journal = {EURASIP J. Appl. Signal Process.}, keywords = {ComputerVision algorithms benchmark evaluation performance statistics to_READ}, month = jan, numpages = {1}, pages = {212--212}, publisher = {Hindawi Publishing Corp.}, timestamp = {2013-10-22T15:59:03.000+0200}, title = {Design of experiments for performance evaluation and parameter tuning of a road image processing chain}, url = {http://dx.doi.org/10.1155/ASP/2006/48012}, volume = 2006, year = 2006 }