Active Shape Models and the Shape Approximation Problem.

Hill,; Cootes,; Taylor, Chris

doi:10.5244/c.9.16

Cited by 24 publications

(29 citation statements)

References 3 publications

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“…• Similarly, a projection of the shape model that minimizes only the distances between landmark points perpendicular to the contour [105] can be used instead of Eq. (6.5).…”

Section: Discussionmentioning

confidence: 99%

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Computer aided diagnosis in chest radiography

Mouton¹,

Douglas

2003

Academic Radiology

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“…• Similarly, a projection of the shape model that minimizes only the distances between landmark points perpendicular to the contour [105] can be used instead of Eq. (6.5).…”

Section: Discussionmentioning

confidence: 99%

“…In practice it can be desirable to minimize only the distance between true and model positions in the direction perpendicular to the object contour because deviation along the contour does not change whether pixels are inside or outside the object. In [105] it is demonstrated how to perform this projection on the contour.…”

Section: Improving Asmsmentioning

confidence: 99%

Computer aided diagnosis in chest radiography

Mouton¹,

Douglas

2003

Academic Radiology

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“…A statistical model is a deformable model which in a compact way describes the information contained in a training dataset. These models were successfully applied to the segmentation of bony structures, e.g., vertebrae (Hill et al, 1996), spine (Smyth et al, 1997), knee joint (Cootes et al, 1998), hand (Mahmoodi et al, 2000), rib cage (van Ginneken and Haar Romeny, 2000) and hip/pelvis ).…”

Section: Introductionmentioning

confidence: 99%

Segmentation of Anatomical Structures by Connected Statistical Models

Bukovec¹,

Likar²,

Pernuš³

2011

Image Anal Stereol

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This paper presents a framework for the segmentation of anatomical structures in medical imagery by connected statistical models. The framework is based on three types of models: first, generic models which operate directly on image intensities, second, connecting models that impose restrictions on the spatial relationship of generic models, and third, a supervising model that represents an arbitrary number of generic and connecting models. In this paper, the statistical model of appearance is used as the generic model, whiles the statistical model of topology, obtained by applying principal component analysis (PCA) on aligned pose and shape parameters of the generic model, is used as the connecting model. The performance of such connected statistical model is demonstrated on anterior-posterior (AP) X-ray images of the hips and pelvis and compared to the modelling by one and six unconnected generic models. The most accurate and robust results were obtained by two-level hierarchical modelling, wherein connected statistical models were used first, followed by unconnected statistical models.

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“…face alignment (FA) is a critical task in many face related computer vision areas such as 3D face labeling, expression analysis and face recognition. For face alignment, there are two fundamental approaches, Active Shape Model (ASM) [1] and Active Appearance Model (AAM) [2]. Many variations of these two methods have been developed to improve their robustness and accuracy [3][4][5].…”

Section: Introductionmentioning

confidence: 99%

Multi-View Face Alignment Using 3D Shape Model for View Estimation

Lao

2009

Advances in Biometrics

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Abstract. For multi-view face alignment (MVFA), the non-linear variation of shape and texture, and the self-occlusion of facial feature points caused by view change are the two major difficulties. The state-of-the-art MVFA methods are essentially view-based approaches in which views are divided into several categories such as frontal, half profile, full profile etc. and each of them has its own model in MVFA. Therefore the view estimation problem becomes a critical step in MVFA. In this paper, a MVFA method using 3D face shape model for view estimation is presented in which the 3D shape model is used to estimate the pose of the face thereby selecting its model and indicating its selfoccluded points. Experiments on different datasets are reported to show the improvement over previous works. [10] are developed which are mainly 2D approaches with no appealing to 3D face information. Due to the intrinsic difficulties caused by face appearance changes in 2D face images of a 3D face, MVFA is still not a solved problem. KeywordsThe state-of-the-art MVFA methods are essentially view-based approaches in which views are divided into several categories such as frontal, half profile, full profile etc. and each of them has its own shape and texture models. Since the texture model used in local search of each label point of a particular shape model depends on

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Active Shape Models and the Shape Approximation Problem.

Cited by 24 publications

References 3 publications

Computer aided diagnosis in chest radiography

Computer aided diagnosis in chest radiography

Segmentation of Anatomical Structures by Connected Statistical Models

Multi-View Face Alignment Using 3D Shape Model for View Estimation

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