The AVR Reconstruction of Rectifying Gangteeth Based on Geometric Active Contour Model

2007 ◽  
Author(s):  
Jin-wen Zhu ◽  
Fai Ma
Keyword(s):  
Active Contour ◽  
Active Contour Model ◽  
Contour Model ◽  
Geometric Active Contour

A geometric active contour model without re-initialization for color images

2009 ◽  
Vol 27 (9) ◽  
pp. 1411-1417 ◽  
Author(s):  
Ying Zheng ◽  
Guangyao Li ◽  
Xiehua Sun ◽  
Xinmin Zhou
Keyword(s):  
Active Contour ◽  
Active Contour Model ◽  
Color Images ◽  
Contour Model ◽  
Geometric Active Contour

scholarly journals Microstructure identification based on vessel pores feature extraction of high-value hardwood species

BioResources ◽  
2021 ◽  
Vol 16 (3) ◽  
pp. 5329-5340
Author(s):  
Xiaoxia Yang ◽  
Ziyu Zhao ◽  
Zhongmin Wang ◽  
Zhedong Ge ◽  
Yucheng Zhou
Keyword(s):  
Neural Network ◽  
Support Vector Machine ◽  
Bp Neural Network ◽  
Active Contour ◽  
Active Contour Model ◽  
Support Vector ◽  
Binary Images ◽  
Contour Model ◽  
Hardwood Species ◽  
Geometric Active Contour

Because of the diversity of vessel pores in different hardwood species, they are important for wood species identification. In this paper, a Micro CT was used to collect wood images. The experiment was based on six wood types, Pterocarpus macrocarpus, Pterocarpus erinaceus, Dalbergia latifolia, Dalbergia frutescens var. tomentosa, Pterocarpus indicus, and Pterocarpus soyauxii. One-thousand cross-sectional images of 2042 px × 1640 px were collected for each species. One pixel represents 1.95 µm of the real physical dimension. The level set geometric active contour model was used to obtain the contour of the vessel pores. Combined with a variety of morphological processing methods, the binary images of the vessel pores were obtained. The features of the binary images were extracted for classification. Classifiers such as BP neural network and support vector machine were used, the number, roundness, area, perimeter, and other characteristic parameters of the vessel pores were classified, and the accuracy rate was more than 98.9%. The distribution and arrangement of the vessel pores of six kinds of hardwood were obtained through the level set geometric active contour model and image morphology. Then BP neural network and support vector machine were used for realizing the classification of hardwood species.

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