Master Assignment
point cloud classification using context based feature engineering
Student project on bat sound analysis
Type: Master EE/CS
Practical information
- Student profile: HBO-ICT, Applied Computer Science, MSc computer science. The student must have basic knowledge of Data science. The student must have basic knowledge of Data science experience with scikit-learn or similar framework is preferable.
- Period: Feb 2021 - July 2021
- (Possible) Compensation: 230 euro per month (before taxes) when carrying out this assignment at Ambient Intelligence (as internship or graduation project).
- More information: saxion.nl/ami
Student: (Unassigned)
If you are interested please contact :
Description:
Digitalization of rail-road infrastructure is aimed at the improvement of maintenance and construction activities. Currently, inspections are done manually, with a domain expert classifying objects.
Strukton Rail works with point clouds, which are sets of spatial data points captured by 3D scanning techniques such as lidar. These point clouds contain many million points of data, resulting in 3D representations of the railway environment. Point cloud data can be used to create machine learning models that can classify the object in rail infrastructure automatically. The task involves feature engineering counting to various aspects of points data.
Objective:
In this project, the aim is to investigate feature engineering techniques and their automation to develop models for an automatic classification of point cloud data.
- Prepare a state of the art for feature engineering for point cloud classification.
- Development of algorithm for automatic feature extraction for point cloud classification.
- Empirical evaluation of various classification algorithm as a candidate to classify point cloud dataset.