Browsing by Author "Huang, Chung-Lin"

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Efficient Triple Output Network for Vertebral Segmentation and Identification
(2019-01-01)
Chuang, Cheng-Hung
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Lin, Chih-Yang
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Tsai, Yuan-Yu
;
Lian, Zhen-You
;
Xie, Hong-Xia
Precise vertebral segmentation provides the basis for spinal image analyses and interventions, such as vertebral compression fracture detection and other abnormalities. Deep learning is a popular and useful paradigm for medical image process. In this paper, we proposed an iterative vertebrae instance segmentation model, which has good generalization ability for segmenting all types of vertebrae, including cervical, thoracic, and lumbar vertebrae. In experimental results, our model not only used 17% less memory but also achieves better performance on vertebrae segmentation compared to existing methods. The existing method provides only two output for segmentation and classification respectively. However, with more memory available, our model is capable of providing third output for accurate anatomical prediction under the same amount of memory.
Iris recognition method
(2008-04-24)
Hwang, Wen-Liang
;
Lin, Jian-Liang
;
Huang, Chung-Lin
;
Che, Wei-Gang
The present invention disclose an iris recognition method, which utilizes a matching pursuit algorithm to simplify the extraction and reconstruction of iris features and reduce the memory space required by each iris feature vector without the penalty of recognition accuracy. The iris recognition method of the present invention comprises an iris-localization component and a pattern matching component. The iris-localization component locates the iris region via the color difference between different portions of the eyeball. The primary iris features are extracted from iris information and transformed into a sequence of iris feature vectors by a matching pursuit algorithm. Thus, the iris image can be represented by a sequence of atoms, and each atom contains base, amplitude and location. Then, the comparison between the feature vectors of two irises is performed to determine whether the two irises match.