3D Shape Similarity

Microsoft Kinect, Google's Project Tango and Lytro's light field camera are all examples of 3D depth sensing reaching the consumer market. As this technology becomes more widestream, new signal processing techniques are needed to exploit this data. Recent techniques in 3D shape similarity assesment present exciting opportunities to develop new algorithms in this area. The aim of this project is to create a tutorial to these techniques using an iPython notebook. Particular emphasis will be placed on an excellent review paper [1] and book [2]. Seneca, the Roman philosopher, said “While we teach, we learn,” and creating a tutorial on these topics is an excellent exercise to understand a complex subject that is at the heart of many modern computer vision techniques. <br><br> [1] S. Biasotti et al "Recent Trends, Applications, and Perspectives in 3D Shape Similarity Assessment", 2015. <br><br> [2] A. Bronstein, M. Bronstein, R. Kimmel "Numerical Geometry of Non-Rigid Shapes", 2008. <br><br> LCAV1556050917


Advisor(s):
Scholefield, Adam James
Year:
2016
Keywords:
Note:
MASTER_SEMESTER
Laboratories:


Note: The status of this file is: Involved Laboratories Only


 Record created 2017-01-12, last modified 2018-03-17

2 Jupiter notebook:
Download fulltextZIP
n/a:
Download fulltextPDF
some pic used at notebook:
Download fulltextZIP
Rate this document:

Rate this document:
1
2
3
 
(Not yet reviewed)