Landmark detection
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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.
Applications
Navigation
Facial landmarks
Finding facial landmarks is an important step in facial identification of people in an image. Facial landmarks can also be used to extract information about mood and intention of the person.[1] Methods used fall in to three categories: holistic methods, constrained local model methods, and regression-based methods.[2]
Holistic methods are pre-progammed with statistical information on face shape and landmark location within it. The classic holistic method is the active appearance model (AAM) introduced in 1998.[3] Since then there has been a number of extensions and improvements to the method. These are largely improvements to the fitting algorithm and can be classified into two groups: analytical fitting methods, and learning-based fitting methods.[4] Analytical methods apply nonlinear optimization methods such as the Gauss–Newton algorithm. This algorithm is very slow but better ones have been proposed such as the project out inverse compositional (POIC) algorithm and the simultaneous inverse compositional (SIC) algorithm.[5]
Medical images
Cephalometry
Fashion
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.[6]
References
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.