This paper presents an active approach for part-based object detection, which optimizes the order of part filter evaluations and the time at which to stop and make a prediction. Statistics, describing the part responses, are learned from training data and are used to formalize the part scheduling problem as an offline optimization. Dynamic programming is applied to obtain a policy, which balanc...

For many medical applications including computer assisted surgery it is nec essary to perform scienti c computations such as mechanical deformation on anatomical structure models Such patient based anatomical models are often ex tracted from volumetric medical images as isosurfaces In this paper we introduce a new algorithm for the decimation of isosurfaces based on deformable models The method...

This paper presents the results of numerical modeling using the finite element method of three implants. Geometric models of individual parts of the analyzed system (implant, abutment and screw) and the standard models of the bone and the crown were built in the Ideas NX environment. On the basis of real geometric models the fully three-dimensional numerical models were built. The calculations ...

Deformable models are used extensively in image processing, computer vision, and medical imaging applications, particularly to delineate object boundaries. Problems associated with initialization and poor convergence to boundary concavities, however, have limited their utility. This chapter presents an external force for deformable models, largely solving both problems. This external force, whi...

Recovery of unstructured 3D data with deformable models has been the subject of many studies over the last ten years. In particular, in medical image understanding, deformable models are useful to get a precise representation of anatomical structures. However, general deformable models involve large linear systems to solve when dealing with high resolution 3D images. The advantage of parametric...

A geometric approach for 3D object segmentation and representation is presented. The segmentation is obtained by deformable surfaces moving towards the objects to be detected in the 3D image. The model is based on curvature motion and the computation of surfaces with minimal areas, better known as minimal surfaces. The space where the surfaces are computed is induced from the 3D image (volumetr...

Fisher Vectors and related orderless visual statistics have demonstrated excellent performance in object detection, sometimes superior to established approaches such as the Deformable Part Models. However, it remains unclear how these models can capture complex appearance variations using visual codebooks of limited sizes and coarse geometric information. In this work, we propose to interpret F...

his article describes how to use level sets to represent and compute deformable surfaces. A deformable surface is a sequence of surface models obtained by taking an initial model and incrementally modifying its shape. Typically, we can parameterize the deformation over time, and thus we can imagine that a surface moves or flows under the influence of a vector field. The surface flow, v, can be ...

Most deformable surfaces used in computer vision are restricted to a simple underlying mesh topology. This is a considerable limitation when dealing with complicated objects whose topology is not known in advance. In previous work we introduced a deformable surface that can take on arbitrary topology while maintaining geometric continuity throughout. In this paper we present some recent advance...

This lecture covers the related topics of feature extraction, shape fitting and image segmentation. Just about all quantitative analysis of medical images requires some form of segmentation or feature extraction. Segmentation [3] [12] [13] distinguishes structures, regions or tissue classes of interest from other detail in the images. Feature extraction can be used to identify specific structur...