Deformable models have shown great potential for image segmentation. They include discrete models whose combinatorial formulation leads to efficient and sometimes optimal minimization algorithms. In this paper, we propose a new discrete framework to deform any partition while preserving its topology. We show how to combine the use of multilabel simple points, topological maps and minimum-length...

This paper reviews model-based methods for non-rigid shape recognition. These methods model, match and classify non-rigid shapes, which are generally problematic for conventational algorithms using rigid models. Issues including model representation, optimization criteria formulation, model matching, and classi3cation are examined in detail with the objective to provide interested researchers a...

This paper makes a review on the current segmentation algorithms used for medical images. Algorithms are divided into three categories according to their main ideas: the ones based on threshold, the ones based on pattern recognition techniques and the ones based on deformable models. The main tendency of each category with their principle ideas, application field, advantages and disadvantages a...

I. INTRODUCTION We present a symmetry-based method that allows humans and robots to collaboratively manipulate deformable objects. The method does not require modeling and simulating deformation. Our method is based on the concept of diminishing rigidity, which we use to quickly compute an approximation to the Jacobian of the deformable object without using simulation. This Jacobian is used to ...

Abstract. X-ray tomographic image reconstruction consists of determining an object function from its projections. In many applications such as non-destructive testing, we look for a fault region (air) in a homogeneous, known background (metal). The image reconstruction problem then becomes the determination of the shape of the default region. Two approaches can be used: modeling the image as a ...

The robustness of shape recovery based on deformable models depends in general on the relative di erence of position and topology of the model with respect to the data a close initialization with correct topology guaranties a proper recovery of the object Furthermore the closeness of the initial model greatly in uences the time of computation needed for the recovery In this paper we propose a m...

In recent years several methods have been proposed for constructing statistical shape models to aid image analysis tasks by providing a-priori knowledge. Examples include principal component analysis (PCA) of manually or semi-automatically placed corresponding landmarks on the learning shapes (point distribution models, PDM), which is time consuming and subjective. However, automatically establ...

Statistical shape-and-texture appearance models use image morphing to define a rich, compact representation of object appearance. They are useful in a variety of applications including object recognition, tracking and segmentation. These techniques, however, have been limited to objects with Lambertian surface reflectance, simple geometry and topology. In this work we present new shape-and-text...

We propose a novel representation for characterizing image differences using a deformable technique for obtaining pixel-wise correspondences. This representation, which is based on a deformable 3D mesh in XYI-space, is then experimentally compared with two related correspondence methods: optical flow and intensity differences. Furthermore, we make use of a probabilistic similarity measure for d...

Deformable model tracking is a powerful methodology that allows us to track the evolution of high-dimensional parameter vectors from uncalibrated monocular video sequences. The core of the approach consists of using low-level vision algorithms, such as edge trackers or optical flow, to collect a large number of 2D displacements, or motion measurements, at selected model points and mapping them ...