220:(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.
246:
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
104:(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
124:
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
151:
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
84:
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:
112:. 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
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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
172:
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.
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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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435:"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".
54:. 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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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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18:Evolutionary Algorithm for Landmark Detection
8:
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204:can also be useful to perform this task.
486:International Journal of Computer Vision
989:Applications of artificial intelligence
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733:3D reconstruction from multiple images
38:is the process of finding significant
753:Simultaneous localization and mapping
7:
533:10.1109/CISP-BMEI48845.2019.8966051
818:Automatic number-plate recognition
25:
161:scale-invariant feature transform
823:Automated species identification
994:Applications of computer vision
808:Audio-visual speech recognition
356:Zhang, Zhang & Du, pp. 1–4.
653:Recognition and categorization
1:
917:Optical character recognition
848:Content-based image retrieval
218:Convolutional Neural Networks
439:Clinical Oral Investigations
365:Zhang, Zhang & Du, p. 2.
347:Zhang, Zhang & Du, p. 1.
338:Zhang, Zhang & Du, p. 1.
46:purposes – for instance, in
409:(Thesis). RMIT University.
249:particle swarm optimization
231:and found its usage within
202:particle swarm optimization
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813:Automatic image annotation
648:Noise reduction techniques
451:10.1007/s00784-021-03990-w
405:WIJESINGHE, GAYAN (2005).
190:Artificial Neural Networks
965:
778:Free viewpoint television
508:10.1007/s11263-018-1097-z
247:certain accuracy. In the
129:in the image rather than
93:local model methods, and
843:Computer-aided diagnosis
905:Moving object detection
895:Medical image computing
658:Research infrastructure
628:Image sensor technology
244:Evolutionary algorithms
198:evolutionary algorithms
102:active appearance model
942:Video content analysis
910:Small object detection
689:Computer stereo vision
239:Evolutionary algorithm
110:Gauss–Newton algorithm
106:nonlinear optimization
50:or creating maps from
947:Video motion analysis
758:Structure from motion
704:3D object recognition
870:Foreground detection
853:Reverse image search
833:Bioimage informatics
803:Activity recognition
329:Wu & Ji, p. 119.
320:Wu & Ji, p. 119.
311:Wu & Ji, p. 118.
302:Wu & Ji, p. 118.
293:Wu & Ji, p. 117.
284:Wu & Ji, p. 116.
275:Wu & Ji, p. 116.
266:Wu & Ji, p. 115.
225:real-time efficiency
122:nonlinear regression
108:methods such as the
60:anatomical landmarks
937:Autonomous vehicles
875:Gesture recognition
738:2D to 3D conversion
227:on mobile devices'
952:Video surveillance
890:Landmark detection
798:3D pose estimation
783:Volumetric capture
743:Gaussian splatting
699:Object recognition
613:Commercial systems
180:There are several
56:facial recognition
36:landmark detection
976:
975:
885:Image restoration
828:Augmented reality
793:
792:
773:4D reconstruction
725:3D reconstruction
618:Feature detection
542:978-1-7281-4852-6
233:augmented reality
118:linear regression
16:(Redirected from
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900:Object detection
865:Face recognition
748:Shape from focus
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608:Digital geometry
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527:. pp. 1–6.
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445:(7): 4299–4309.
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196:algorithms, but
114:machine learning
97:-based methods.
80:Facial landmarks
52:satellite images
32:computer science
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932:Robotic mapping
880:Image denoising
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192:and especially
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838:Blob detection
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763:View synthesis
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492:(2): 115–142.
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184:for locating
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682:Technologies
668:Segmentation
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70:Applications
48:robot vision
44:navigational
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768:Visual hull
663:Researchers
374:Wu & Ji
91:constrained
983:Categories
638:Morphology
596:Categories
499:1805.05563
391:1907.06724
255:References
182:algorithms
95:regression
75:Navigation
999:Landmarks
551:210931275
516:255101562
477:235232149
411:CiteSeerX
186:landmarks
170:attentive
89:methods,
40:landmarks
27:Algorithm
673:Software
633:Learning
623:Geometry
603:Datasets
469:34046742
200:such as
127:wavelets
87:holistic
460:8310492
176:Methods
154:hemline
147:Fashion
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512:S2CID
494:arXiv
473:S2CID
386:arXiv
131:pixel
537:ISBN
465:PMID
229:GPUs
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455:PMC
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62:in
30:In
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