Abstract A new crack identification method that estimates the cracks in invisible locations based on surface deformation measured by digital image correlation (DIC) is developed. An inverse problem setup to estimate such from deformations. The has an ill-condition because of noise contained Our proposed regularization uses prior information and Expectation a Posteriori (EAP) estimation. Prior i...

This paper presents a novel active surface segmentation algorithm using a multiscale shape representation and prior. We define a parametric model of a surface using spherical wavelet functions and learn a prior probability distribution over the wavelet coefficients to model shape variations at different scales and spatial locations in a training set. Based on this representation, we derive a pa...

Although image linear feature is important in many applications, it is difficult to extract precisely, because back ground is complex and feature is not always significant. In this paper, we propose a level set based automatic linear feature extraction method using prior shapes. We introduce a variable shape model which together with the level set function for segmentation and extract linear fe...

We consider the problem of learning an object model for feature matching. The matching system is Bayesian in nature with separate likelihood and prior parts. The likelihood is based on Gabor filter responses, which are modelled as probability distributions in the filter response vector space. The prior model for the object shape is learnt in two stages: in the first stage we assume only the mea...

We describe a level set formulation using both shape and motion prior, for both segmentation and region tracking in high frame rate echocardiographic image sequences. The proposed approach uses the following steps: registration of the prior shape, level set segmentation constrained through the registered shape and region tracking. Registration of the prior shape is expressed as a rigid or an af...

In this paper, we propose a method to segment multiple rodent brain structures simultaneously. This method combines deformable models and hierarchical shape priors within one framework. The deformation module employs both gradient and appearance information to generate image forces to deform the shape. The shape prior module uses Principal Component Analysis to hierarchically model the multiple...

Every biological or artificial visual system faces the problem that images are highly ambiguous, in the sense that every image depicts an infinite number of possible 3D arrangements of shapes, surface colors, and light sources. When estimating 3D shape from shading, the human visual system partly resolves this ambiguity by relying on the light-from-above prior, an assumption that light comes fr...

Accurate three-dimensional (3D) image segmentation techniques have become increasingly important for medical image analysis in general, and for spinal vertebrae image analysis in particular. The complexity of vertebrae shapes, gaps in the cortical bone and internal boundaries pose significant challenge for image analysis. In this paper, we describe a level set image segmentation framework that ...