We introduce a non-linear shape prior for the deformable model framework that we learn from a set of shape samples using recent manifold learning techniques. We model a category of shapes as a finite dimensional manifold which we approximate using Diffusion maps, that we call the shape prior manifold. Our method computes a Delaunay triangulation of the reduced space, considered as Euclidean, an...

This paper presents a novel shape-guided multi-region variational region growing framework for extracting simultaneously thoracic and abdominal organs on 3D infants whole body MRI. Due to the inherent low quality of these data, classical segmentation methods tend to fail at the multi-segmentation task. To compensate for the low resolution and the lack of contrast and to enable the simultaneous ...

People readily perceive patterns of shading as 3‐D shapes. Owing to the generalised bas‐ relief ambiguity when extracting shape from shading, people must simultaneously estimate the shape of the surface and the nature of the light source. In many cases cues in the image will be insufficient to resolve all of the ambiguities present, and in such cases the human visual system may employ one of a ...

We present a generative approach to model-based motion segmentation by incorporating a statistical shape prior into a novel variational segmentation method. The shape prior statistically encodes a training set of object outlines presented in advance during a training phase. In a region competition manner the proposed variational approach maximizes the homogeneity of the motion vector field esti...

Boundary incompleteness raises great challenges to automatic prostate segmentation in ultrasound images. Shape prior can provide strong guidance in estimating the missing boundary, but traditional shape models often suffer from hand-crafted descriptors and local information loss in the fitting procedure. In this paper, we attempt to address those issues with a novel framework. The proposed fram...

We present and analyze an infinite dimensional Bayesian inference formulation, and its numerical approximation, for the inverse problem of inferring the shape of an obstacle from scattered acoustic waves. Given a Gaussian prior measure on the shape space, whose covariance operator is the inverse of the Laplacian, the Bayesian solution of the inverse problem is the posterior measure given by the...

Some 350 Trichoderma isolates were obtained from soil samples collected from different parts of Iran. Cultures were purified on 2% water agar by hyphal tip method prior to identification. Obtained isolates were identified using morphological features including colony characters (pigmentation and growth rate on PDA) and microscopic characters such as shape of conidiophores, shape and size of con...

We present a graph cuts-based image segmentation technique that incorporates an elliptical shape prior. Inclusion of this shape constraint restricts the solution space of the segmentation result, increasing robustness to misleading information that results from noise, weak boundaries, and clutter. We argue that combining a shape prior with a graph cuts method suggests an iterative approach that...

A novel trainable snake model, called EigenSnake, is presented in the Bayesian framework. In the EigenSnake, prior knowledge of a specific object shape, such as that of face outlines and facial features, is derived from a training set of the shape and incorporated into a Bayesian snake model in the form of the prior distribution. Further, a “shape space”, which is constructed on the basis of a ...

We propose a novel variational approach based on a level set formulation of the Mumford-Shah functional and shape priors. We extend the functional by a labeling function which indicates image regions in which the shape prior is enforced. By minimizing the proposed functional with respect to both the level set function and the labeling function, the algorithm selects image regions where it is fa...