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Research:
Traffic signs detection and recognition:
This work is attached to this research.
Our contribution is threefold:
- Proposition of a new naive detection based on color and shape retrieval.
- Using the information of the detection, we propose to recognize traffic sign using machine learning methods.
- In order to perform to accelerate the process of detection-recognition, we propose to introduce a tracking module information already extracted in the two previous stages.
This work has been developed by students of Ms in Computer Vision of UB and Guillaume Lemaître under supervision of Yohan Fougerolles.
Traffic signs detection and recognition:
We provide a tool allowing to manually annotate the prostate using three different labels:
- Prostate
- Cancer
- Benign
This tool can allow to create in a simple way ground-truth for segmentation and registration task by segmenting the prostate but also classification by detecting prostate cancer on MRI images. Three main options are available with this tool:
- Possibility to export the annotation in XML format.
- Possibility ti export the annotation in PNG format.
- Possibility to anonymized the DICOM images.
Source code is available at the following web page: [here].
Cycling:
I proposed some enhancements to the software GoldenCheetah. GoldenCheetah is an open-source cross platform software which allows to manage cycling data in order to train and have a rational approach to optimize the performances in cycling. A french article is making a description of this software [here].
Release & developer version can be found [here].
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