209:(CNNs), have revolutionized landmark detection by allowing computers to learn the features from large datasets of images. By training a CNN on a dataset of images with labeled facial landmarks, the algorithm can learn to detect these landmarks in new images with high accuracy even when they appear in different lighting conditions, at different angles, or in partially occluded views.
235:
at the training stage try to learn the method of correct determination of landmarks. This phase is an iterative process and, accordingly, is performed in several iterations. As a result of the completion of the last iteration, a system will be obtained that can correctly determine the landmark with a
93:(AAM) introduced in 1998. 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. Analytical methods apply
113:
and other fitting methods. 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. Other extensions to the basic AAM method analyse
140:
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
73:
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. Methods used fall in to three categories:
101:. 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. Learning-based fitting methods use
156:
methods. This has been helped along enormously by the publication of a number of large fashion datasets that can be used for training. These methods include regression-based models, constraint-based models, and
161:
models. The particular problems of fashion landmark detection (deformation) have led to pose estimation models which detect and take into account the pose of the model wearing the clothes.
201:
Deep learning has had a significant impact on autonomous facial landmark detection by enabling more accurate and efficient detection of landmarks in real-world photos. With traditional
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Schwendicke, Falk; Chaurasia, Akhilanand; Arsiwala, Lubaina; Lee, Jae-Hong; Elhennawy, Karim; Jost-Brinkmann, Paul-Georg; Demarco, Flavio; Krois, Joachim (2021).
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Kartynnik, Yury; Ablavatski, Artsiom; Grishchenko, Ivan; Grundmann, Matthias (2019). "Real-time Facial
Surface Geometry from Monocular Video on Mobile GPUs".
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Holistic methods are pre-progammed with statistical information on face shape and landmark location coefficients. The classic holistic method is the
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method, there are particles that search for landmarks, and each of them uses a certain formula in each iteration to optimize landmark detection.
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techniques, detecting facial landmarks could be challenging due to variations in lighting, head position, and occlusion, but
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of a dress. Fashion landmark detection is particularly difficult due to the extreme deformation that can occur in clothing.
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2019 12th
International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI)
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where it is used to identify key points on a face. It also has important applications in medicine, identifying
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Zhang, Yungang; Zhang, Cai; Du, Fei (2019). "A Brief Review of Recent
Progress in Fashion Landmark Detection".
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intensity. This helps with fitting unseen parts of the face which basic AAM finds troublesome.
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424:"Deep learning for cephalometric landmark detection: Systematic review and meta-analysis"
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Wu, Yue; Ji, Qiang (2019). "Facial
Landmark Detection: A Literature Survey".
43:. Methods used in navigation have been extended to other fields, notably in
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LANDMARK DETECTION ON CEPHALOMETRIC X-RAYS USING PARTICLE SWARM OPTIMISATION
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28:
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have been used in the past. However, it is now more common to use
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in an image. This originally referred to finding landmarks for
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In particular, solutions based on this approach have achieved
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techniques to predict the facial coefficients. These can use
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in images. Nowadays the task usually is solved using
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Some classical methods of feature detection such as
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193:can also be useful to perform this task.
475:International Journal of Computer Vision
978:Applications of artificial intelligence
248:
722:3D reconstruction from multiple images
27:is the process of finding significant
742:Simultaneous localization and mapping
7:
522:10.1109/CISP-BMEI48845.2019.8966051
807:Automatic number-plate recognition
14:
150:scale-invariant feature transform
812:Automated species identification
983:Applications of computer vision
797:Audio-visual speech recognition
345:Zhang, Zhang & Du, pp. 1–4.
642:Recognition and categorization
1:
906:Optical character recognition
837:Content-based image retrieval
207:Convolutional Neural Networks
428:Clinical Oral Investigations
354:Zhang, Zhang & Du, p. 2.
336:Zhang, Zhang & Du, p. 1.
327:Zhang, Zhang & Du, p. 1.
35:purposes – for instance, in
398:(Thesis). RMIT University.
238:particle swarm optimization
220:and found its usage within
191:particle swarm optimization
1004:
802:Automatic image annotation
637:Noise reduction techniques
440:10.1007/s00784-021-03990-w
394:WIJESINGHE, GAYAN (2005).
179:Artificial Neural Networks
954:
767:Free viewpoint television
497:10.1007/s11263-018-1097-z
236:certain accuracy. In the
118:in the image rather than
82:local model methods, and
832:Computer-aided diagnosis
894:Moving object detection
884:Medical image computing
647:Research infrastructure
617:Image sensor technology
233:Evolutionary algorithms
187:evolutionary algorithms
91:active appearance model
931:Video content analysis
899:Small object detection
678:Computer stereo vision
228:Evolutionary algorithm
99:Gauss–Newton algorithm
95:nonlinear optimization
39:or creating maps from
936:Video motion analysis
747:Structure from motion
693:3D object recognition
859:Foreground detection
842:Reverse image search
822:Bioimage informatics
792:Activity recognition
318:Wu & Ji, p. 119.
309:Wu & Ji, p. 119.
300:Wu & Ji, p. 118.
291:Wu & Ji, p. 118.
282:Wu & Ji, p. 117.
273:Wu & Ji, p. 116.
264:Wu & Ji, p. 116.
255:Wu & Ji, p. 115.
214:real-time efficiency
111:nonlinear regression
97:methods such as the
49:anatomical landmarks
926:Autonomous vehicles
864:Gesture recognition
727:2D to 3D conversion
216:on mobile devices'
941:Video surveillance
879:Landmark detection
787:3D pose estimation
772:Volumetric capture
732:Gaussian splatting
688:Object recognition
602:Commercial systems
169:There are several
45:facial recognition
25:landmark detection
965:
964:
874:Image restoration
817:Augmented reality
782:
781:
762:4D reconstruction
714:3D reconstruction
607:Feature detection
531:978-1-7281-4852-6
222:augmented reality
107:linear regression
995:
889:Object detection
854:Face recognition
737:Shape from focus
710:
597:Digital geometry
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516:. pp. 1–6.
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434:(7): 4299–4309.
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185:algorithms, but
103:machine learning
86:-based methods.
69:Facial landmarks
41:satellite images
21:computer science
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1002:
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921:Robotic mapping
869:Image denoising
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632:Motion analysis
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578:Computer vision
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203:computer vision
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181:and especially
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916:Remote sensing
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827:Blob detection
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752:View synthesis
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683:Motion capture
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481:(2): 115–142.
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405:10.1.1.72.3218
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126:Medical images
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53:medical images
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958:Main category
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911:Pose tracking
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197:Deep Learning
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183:Deep Learning
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173:for locating
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849:Eye tracking
705:Applications
671:Technologies
657:Segmentation
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416:Bibliography
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131:Cephalometry
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59:Applications
37:robot vision
33:navigational
24:
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757:Visual hull
652:Researchers
363:Wu & Ji
80:constrained
972:Categories
627:Morphology
585:Categories
488:1805.05563
380:1907.06724
244:References
171:algorithms
84:regression
64:Navigation
988:Landmarks
540:210931275
505:255101562
466:235232149
400:CiteSeerX
175:landmarks
159:attentive
78:methods,
29:landmarks
16:Algorithm
662:Software
622:Learning
612:Geometry
592:Datasets
458:34046742
189:such as
116:wavelets
76:holistic
449:8310492
165:Methods
143:hemline
136:Fashion
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536:S2CID
501:S2CID
483:arXiv
462:S2CID
375:arXiv
120:pixel
526:ISBN
454:PMID
218:GPUs
518:doi
493:doi
479:127
444:PMC
436:doi
51:in
19:In
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