Active appearance model and deep learning for more accurate prostate segmentation on MRI

2016 ◽  
Author(s):  
Ruida Cheng ◽  
Holger R. Roth ◽  
Le Lu ◽  
Shijun Wang ◽  
Baris Turkbey ◽  
...  
Keyword(s):  
Deep Learning ◽  
Appearance Model ◽  
Active Appearance Model ◽  
Prostate Segmentation ◽  
Active Appearance

scholarly journals USING SUPERVISED DEEP LEARNING FOR HUMAN AGE ESTIMATION PROBLEM

2017 ◽  
Vol XLII-2/W4 ◽  
pp. 97-100 ◽  
Author(s):  
K. A. Drobnyh ◽  
A. N. Polovinkin
Keyword(s):  
Deep Learning ◽  
Age Estimation ◽  
Appearance Model ◽  
Active Appearance Model ◽  
Estimation Algorithms ◽  
Broad Variety ◽  
Human Age Estimation ◽  
Deep Learning Features ◽  
Active Appearance ◽  
Improved Accuracy

Automatic facial age estimation is a challenging task upcoming in recent years. In this paper, we propose using the supervised deep learning features to improve an accuracy of the existing age estimation algorithms. There are many approaches solving the problem, an active appearance model and the bio-inspired features are two of them which showed the best accuracy. For experiments we chose popular publicly available FG-NET database, which contains 1002 images with a broad variety of light, pose, and expression. LOPO (leave-one-person-out) method was used to estimate the accuracy. Experiments demonstrated that adding supervised deep learning features has improved accuracy for some basic models. For example, adding the features to an active appearance model gave the 4% gain (the error decreased from 4.59 to 4.41).

scholarly journals The use of an active appearance model for automated prostate segmentation in magnetic resonance

2013 ◽  
Vol 52 (7) ◽  
pp. 1374-1377 ◽  
Author(s):  
Anne Sofie Korsager ◽  
Ulrik Landberg Stephansen ◽  
Jesper Carl ◽  
Lasse Riis Østergaard
Keyword(s):  
Magnetic Resonance ◽  
Appearance Model ◽  
Active Appearance Model ◽  
Prostate Segmentation ◽  
Active Appearance

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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