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.
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 coefficients. 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] Learning-based fitting methods use machine learning techniques to predict the facial coefficients. These can use linear regression, nonlinear regression and other fitting methods.[6] In general, the analytic fitting methods are more accurate and do not need training, while the learning-based fitting methods are faster, but need to be trained.[7] Other extensions to the basic AAM method analyse wavelets in the image rather than pixel intensity. This helps with fitting unseen parts of the face which basic AAM finds troublesome.[8]
Medical images
Cephalometry
Fashion
The purpose of landmark detection in fashion images is for classification purposes. This aids in the retrieval of images with specified features from a database or general search. An example of a fashion landmark is the location of the hemline of a dress.[9]
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.[10]
References
- ^ Wu & Ji, p. 115.
- ^ Wu & Ji, p. 116.
- ^ Wu & Ji, p. 116.
- ^ Wu & Ji, p. 117.
- ^ Wu & Ji, p. 118.
- ^ Wu & Ji, p. 118.
- ^ Wu & Ji, p. 119.
- ^ Wu & Ji, p. 119.
- ^ Zhang, Zhang & Du, p. 1.
- ^ LANDMARK DETECTION ON CEPHALOMETRIC X-RAYS USING PARTICLE SWARM OPTIMISATION GAYAN WIJESINGHE (2005) by Supervisors Vic , Ciesielski , Xiaodong Li
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.
- Yungang Zhang, Cai Zhang, Fei Du, "A brief review of recent progress in fashion landmark detection", 12th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI), pp. 1–6, 2019.