Home | ResearchPublications|  Teaching | Students | Links | Engineering | Bio

          Patrice Brault's Homepage      

L2S, CentraleSupelec, Paris-Saclay University.        

____________________________________

                                                                         

                                                                                                   
                                                                         
    



CNRS Engineer-Researcher,

L2S, Laboratory of Signals and Systems, CNRS UMR 8506
CentraleSupelec, Plateau de Moulon, 3 rue Joliot-Curie,
91192, Gif sur Yvette Cedex,  FRANCE.

Room : C4.23 Phone:  +33 1 69 85 17 73
Fax :
E-mail:
patrice.brault@l2s.centralesupelec.fr

Researcher ID :

GoogleScholar : https://scholar.google.fr/citations?user=Ps34jaoAAAAJ&hl=fr

ResearchGate :  https://www.researchgate.net/profile/Patrice_Brault

IEEE Senior Member (SM'2009)


ORCID 0000-0002-3653-7307

 

---------------------------------------------------------------------------

        

       

  1. Instantiated segmentation by RCNN mask_Cars on Highway.mp4  (download to view)
  2. Instantiated segmentation by R-CNN mask.mp4  (download to view)
  3. Semantic segmentation initiated by Optical Flow.mp4
  4. Image of an instantiated semantic segmentation with representation of the speed vectors (argument and module) by a color (after the color-speed graphic representation below)

In 1, 2 and 3 the semantic value is given by the name of the object : car, truck, pedestrian, bag, bicycle. The real number indicates the degree of confidence in the semantic value.

In 1 and 2, the objects are instantiated by a different color.

In 3, the color indicates the orientation and the absolute value of the speed given by the Optical Flow.

   This one initiates and speeds up the segmentation which results in a joint Motion Estimation-Semantic segmentation.

                

                     Color-speed graphic representation


-------------------------------------------------------------------------------------

Master 2 Internship subjects proposals ( 2022-2023)
    1)  Semantic instantiated segmentation initiated by a motion estimation (ME).
This subject started beeing explored in 2019. The semantic segmentation has been realized by means of learning methods : first an RCNN architecture then the YOLO algorithm. This last one enables a fast instantiated segmentation. In order to accelerate these segmentations, we initiate them by a non-learning method of motion estimation : the classical optical flow algorithm (OF). The interest now is to implement a learned motion estimation and integrate it in the same deep neural network. Experiments have been done on road trafic sequences and city pedestrians, bicycles and cars  ones (see above on this page).

Semantic_Segmentation_and_MotionEstim_with DNNs_M2R Subject_2019-2020.pdf




   
    2) Image Quality Assessment (IQA) : Objective Contrast Measurement  by learning (Deep Neural Nets)
We, in a former approach, have used a mathematical approximation by a 3 variables polynomial, and an appropriate parametrization, of the MOS (Mean Objective Score) curve. The goal was to be able to reproduce, with a good confidence, the MOS given by a group of human subjects, by using only the intrinsic parameters of the image (the variables of the polynomial) that are : the Dynamic Range Occupation (DRO) of the Luminance, the Histogram Shape Deformation (HSD) during contrast enhancement and the Pixel Uniformity (PU) in a close neighborhood inside the image.
In most cases this model (Maximum Contrast Minimum Artifact, MCMA) provides and objective result of the contrast quality evaluation close to the MOS.
More recently we turned to use Learning to reach the same, or better, results.  Interesting results have been obtained with VGG16, a CNN architecture, and TID2013 and CEED2016 databases. The BRISQUE evaluator has also been used. We observed that in some cases the result is NOT in concordance with the results of the MOS. In fact this may come from the perceptual property of the human eye and Human Visual System (HVS).  In fact it seems that three other factors should be taken into account in the computation of this task with a neural network, that are : the spatial information (SI), the colorfulness (CF) and the Global Contrast Factor (GCF)

            Objective_Constrast_Measurement_M2R_subject_2019-20.pdf

    3) Color restoration for underwater images and videos
An imperfectly solved problem in underwater images is to compensate the effect of depth on images, that tends to cancel all colors and turn the major color to blue or green with depth. A good solution has been to use an immerged multi color chart ( for example the Mac Adam ellipse) to quantify the effect of depth and colors filtering by the water. Then it is possible to make a quite good correction on the images and videos.
We now would like to test a learning algorithm to see if it is possible to do the color correction without using this color chart.


You are welcome to send your application to : patrice.brault@centralesupelec.fr

------------------------------------------------------------------

---------

PhD  subjects proposals (2022 in co-supervision)

 

You are welcome to send your application to : patrice.brault@centralesupelec.fr