Diaphragm dome surface segmentation in CT data sets: a 3D active appearance model approach

2002 ◽  
Author(s):  
Reinhard Beichel ◽  
Georg Gotschuli ◽  
Erich Sorantin ◽  
Franz W. Leberl ◽  
Milan Sonka
Keyword(s):  
Appearance Model ◽  
Active Appearance Model ◽  
Data Sets ◽  
Surface Segmentation ◽  
Active Appearance ◽  
Ct Data ◽  
Model Approach

Segmentation of cardiac MR images: an active appearance model approach

2000 ◽  
Author(s):  
Steven C. Mitchell ◽  
Boudewijn P. F. Lelieveldt ◽  
Rob J. van der Geest ◽  
Jorrit Schaap ◽  
Johan H. C. Reiber ◽  
...  
Keyword(s):  
Appearance Model ◽  
Active Appearance Model ◽  
Mr Images ◽  
Cardiac Mr ◽  
Active Appearance ◽  
Model Approach

Geometrical Features and Active Appearance Model Applied to Facial Expression Recognition

2016 ◽  
Vol 16 (04) ◽  
pp. 1650019 ◽  
Author(s):  
Flávio Altinier Maximiano da Silva ◽  
Helio Pedrini
Keyword(s):  
Gabor Filter ◽  
Support Vector ◽  
Appearance Model ◽  
Active Appearance Model ◽  
Data Sets ◽  
Expression Recognition ◽  
Basic Emotions ◽  
Training Support ◽  
In The Wild ◽  
Active Appearance

One of the most effective ways of expressing emotion is through facial expressions. This work proposes and discusses a geometrical descriptor based on the calculation of distances from coordinates of facial fiducial points, which are used as features for training support vector machines (SVM) to classify emotions. Three data sets are studied and six basic emotions are considered in our experiments. In comparison to other approaches available in the literature, the results obtained with our geometrical descriptor demonstrated to be very competitive, achieving high classification F-score rates. Additionally, we evaluate whether the combination of our geometrical descriptor with an appearance feature, the Gabor filter, allows emotions to be even more distinguishable for the classifier. The result is positive for two out of three data sets. Finally, to simulate in-the-wild scenarios, an active appearance model (AAM) is trained to position the fiducial points on the correct facial locations, instead of using the ones provided by the data sets. As the fitting error is considered acceptable, the former experiments are also conducted with the new data generated by the AAM. The results show a small drop on the F-score values when compared to the data originally provided by the data sets,but are still satisfactory.

MAPPING 2D TO 3D FORENSIC FACIAL RECOGNITION VIA BIO- INSPIRED ACTIVE APPEARANCE MODEL

2015 ◽  
Vol 78 (2-2) ◽  
Author(s):  
Siti Zaharah Abd. Rahman ◽  
Siti Norul Huda Sheikh Abdullah ◽  
Lim Eng Hao ◽  
Mohammed Hasan Abdulameer ◽  
Nazri Ahmad Zamani ◽  
...  
Keyword(s):  
Forensic Analysis ◽  
Support Vector ◽  
Appearance Model ◽  
Active Appearance Model ◽  
Video Footage ◽  
Digital Forensic ◽  
Facial Information ◽  
Active Appearance ◽  
2D Model ◽  
2D To 3D

This research done is to solve the problems faced by digital forensic analysts in identifying a suspect captured on their CCTV. Identifying the suspect through the CCTV video footage is a very challenging task for them as it involves tedious rounds of processes to match the facial information in the video footage to a set of suspect’s images. The biggest problem faced by digital forensic analysis is modeling 2D model extracted from CCTV video as the model does not provide enough information to carry out the identification process. Problems occur when a suspect in the video is not facing the camera, the image extracted is the side image of the suspect and it is difficult to make a matching with portrait image in the database. There are also many factors that contribute to the process of extracting facial information from a video to be difficult, such as low-quality video. Through 2D to 3D image model mapping, any partial face information that is incomplete can be matched more efficiently with 3D data by rotating it to matched position. The first methodology in this research is data collection; any data obtained through video recorder. Then, the video will be converted into an image. Images are used to develop the Active Appearance Model (the 2D face model is AAM) 2D and AAM 3D. AAM is used as an input for learning and testing process involving three classifiers, which are Random Forest, Support Vector Machine (SVM), and Neural Networks classifier. The experimental results show that the 3D model is more suitable for use in face recognition as the percentage of the recognition is higher compared with the 2D model.

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