Left ventricle segmentation in fetal echocardiography using a multi-texture active appearance model based on the steered Hermite transform

2016 ◽  
Vol 137 ◽  
pp. 231-245 ◽  
Author(s):  
Lorena Vargas-Quintero ◽  
Boris Escalante-Ramírez ◽  
Lisbeth Camargo Marín ◽  
Mario Guzmán Huerta ◽  
Fernando Arámbula Cosio ◽  
...  
Keyword(s):  
Left Ventricle ◽  
Fetal Echocardiography ◽  
Appearance Model ◽  
Active Appearance Model ◽  
Hermite Transform ◽  
Model Based ◽  
Active Appearance ◽  
Ventricle Segmentation

scholarly journals A Modified Active Appearance Model Based on an Adaptive Artificial Bee Colony

2014 ◽  
Vol 2014 ◽  
pp. 1-16 ◽  
Author(s):  
Mohammed Hasan Abdulameer ◽  
Siti Norul Huda Sheikh Abdullah ◽  
Zulaiha Ali Othman
Keyword(s):  
Face Recognition ◽  
Model Fitting ◽  
Optimization Method ◽  
Appearance Model ◽  
Active Appearance Model ◽  
Abc Algorithm ◽  
Model Based ◽  
Bee Colony ◽  
Active Appearance ◽  
Recognition Technique

Active appearance model (AAM) is one of the most popular model-based approaches that have been extensively used to extract features by highly accurate modeling of human faces under various physical and environmental circumstances. However, in such active appearance model, fitting the model with original image is a challenging task. State of the art shows that optimization method is applicable to resolve this problem. However, another common problem is applying optimization. Hence, in this paper we propose an AAM based face recognition technique, which is capable of resolving the fitting problem of AAM by introducing a new adaptive ABC algorithm. The adaptation increases the efficiency of fitting as against the conventional ABC algorithm. We have used three datasets: CASIA dataset, property 2.5D face dataset, and UBIRIS v1 images dataset in our experiments. The results have revealed that the proposed face recognition technique has performed effectively, in terms of accuracy of face recognition.

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