Facial feature localization based on an improved active shape model

Zheng, Zhonglong; Jia, Jiong; Duanmu, Chunjiang; Liu, XinHong; Yang, Jie

doi:10.1016/j.ins.2007.12.014

Cited by 36 publications

(22 citation statements)

References 26 publications

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“…The number of feature points on each CT image must be consistent. The labeling is important, and each label represents a particular part of the aorta or its boundary [6,7]. In general, we choose points marking parts of the object with particular application-dependent significance, points marking application-dependent things, and other points that can be interpolated from points of types above [8].…”

Section: Implementation Methodsmentioning

confidence: 99%

Visual Three-Dimensional Reconstruction of Aortic Dissection Based on Medical CT Images

Duan

Chen

Wang

et al. 2017

International Journal of Digital Multimedia Broadcasting

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With the rapid development of CT technology, especially the higher resolution of CT machine and a sharp increase in the amount of slices, to extract and three-dimensionally display aortic dissection from the huge medical image data became a challenging task. In this paper, active shape model combined with spatial continuity was adopted to realize automatic reconstruction of aortic dissection. First, we marked aortic feature points from big data sample library and registered training samples to build a statistical model. Meanwhile, gray vectors were sampled by utilizing square matrix, which set the landmarks as the center. Posture parameters of the initial shape were automatically adjusted by the method of spatial continuity between CT sequences. The contrast experiment proved that the proposed algorithm could realize accurate aorta segmentation without selecting the interested region, and it had higher accuracy than GVF snake algorithm (93.29% versus 87.54% on aortic arch, 94.30% versus 89.25% on descending aorta). Aortic dissection membrane was extracted via Hessian matrix and Bayesian theory. Finally, the three-dimensional visualization of the aortic dissection was completed by volume rendering based on the ray casting method to assist the doctors in clinical diagnosis, which contributed to improving the success rate of the operations.

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Section: Implementation Methodsmentioning

confidence: 99%

Visual Three-Dimensional Reconstruction of Aortic Dissection Based on Medical CT Images

Duan

Chen

Wang

et al. 2017

International Journal of Digital Multimedia Broadcasting

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“…Literature [7][8][9] proposed a method based on shape templates, including several complex methods such as active contour model (ACM), active shape model (ASM) and active appearance model (AAM). In addition, the literature [10] proposed a novel multi-type shape guided fuzzy clustering algorithm that can successfully segment the lip region with teeth and whiskers.…”

Section: Figure I Schematic Diagram Of Lip Reading Systemmentioning

confidence: 99%

A Multi-Scale Lip Segmentation Method Based on Markov Random Field

Lu¹,

Zhu²,

Liu³

2018

Proceedings of the 2018 2nd International Conference on Artificial Intelligence: Technologies and Applications (ICAITA 2018)

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Abstract-In order to perform lip segmentation in a lipreading system, we propose a new method based on maximum a posterior and Markov random field (MAP-MRF) framework to statistically model observed images of different texture areas in this paper. First, we establish a multi-scale model to capture the characteristics of each sub-region in the image and use the tree structure in the wavelet domain to calculate the probability of tree nodes at different scales. Thus, the number of layer can be considered as one segment cluster. Then, we utilize MRF to translate the lip segmentation problem into labeling optimization issue. Finally, the Bayesian criteria and the extended expectation maximum (EM) algorithm are applied to estimate child node parameters. The experimental results of this method are more robust than the traditional iterative condition model (ICM).

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“…Similarly, AAM-based methods also build a statistical model to represent an object. The model is based not only on the shape, but also on the appearance of the object [2], [20], [21]. Nevertheless, the final segmentation accuracy of such a method depends on the initial template position.…”

Section: A Review Of Related Workmentioning

confidence: 99%

Lip Segmentation under MAP-MRF Framework with Automatic Selection of Local Observation Scale and Number of Segments

Cheung

Cao

et al. 2014

IEEE Trans. on Image Process.

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Abstract-This paper addresses the problem of segmenting lip region from frontal human face image. Supposing each pixel of the target image has an optimal local scale from the segmentation viewpoint, we treat the lip segmentation problem as a combination of observation scale selection and observed data classification. Accordingly, we propose a hierarchical multiscale Markov random field (MRF) model to represent the membership map of each input pixel to a specific segment and localscale map simultaneously. Subsequently, lip segmentation can be formulated as an optimal problem in the maximum a posteriori (MAP)-MRF framework. Then, we present a rival-penalized iterative algorithm to implement the segmentation, which is independent of the number of predefined segments. The proposed method mainly features two aspects: 1) its performance is independent of the predefined number of segments, and 2) it takes into account the local optimal observation scale for each pixel. Finally, we conduct the experiments on four benchmark databases, i.e. AR, CVL, GTAV, and VidTIMIT. Experimental results show that the proposed method is robust to the segment number that changes with a speaker's appearance, and can enhance the segmentation accuracy by taking advantage of the local optimal observation scale information.Index Terms-Lip segmentation, MAP-MRF framework, number of segments, local scale selection.

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Facial feature localization based on an improved active shape model

Cited by 36 publications

References 26 publications

Visual Three-Dimensional Reconstruction of Aortic Dissection Based on Medical CT Images

Visual Three-Dimensional Reconstruction of Aortic Dissection Based on Medical CT Images

A Multi-Scale Lip Segmentation Method Based on Markov Random Field

Lip Segmentation under MAP-MRF Framework with Automatic Selection of Local Observation Scale and Number of Segments

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