Landmark detection

Landmark detection is the process in computer science of finding significant landmarks in an image. This orginially referred to finding landmarks for navigational purposes – for instance, in robot vision or creating maps from satellite images. Methods used in navigation have been extended to other fields, notably in facial recognition where it is used to identify key points on a face. It also has important applications in medicine, identifying anatomical landmarks in medical images.

Methods

There are several algorithms for locating landmarks in images. Nowadays evolutionary algorithms such as particle swarm optimization are so useful to perform this task. evolutionary algorithms generally have two phase, training and test.

Evolutionary algorithm

In the training phase, we try to learn the algorithm to locate landmark correctly. this phase performs in some iterations and finally in the last iteration we hope to obtain a system that can locate the landmark, correctly. in the particle swarm optimization there are some particles that search for the landmark. each particle uses a specific formula in each iteration to optimizes the landmark detecting.

Bibliography

• Falk Schwendicke, Akhilanand Chaurasia, Lubaina Arsiwala, Jae-Hong Lee, Karim Elhennawy, Paul-Georg Jost-Brinkmann, Flavio Demarco, Joachim Krois, "Deep learning for cephalometric landmark detection: systematic review and meta-analysis", Clinical Oral Investigations, vol. 25, pp. 4299–4309, 2021.
• Yue Wu, Qiang Ji, "Facial landmark detection: a literature survey", International Journal of Computer Vision, vol. 127, pp. 115–142, 2019.