Chondrodystrophic dwarfism and multiple malformations in two sisters.

A genetic skeletal dysplasia with dwarfism, scoliosis and multiple skeletal defects was observed in two sisters. Only nine cases with similar features have been reported in the literature

Rigid and Non-rigid Shape Matching for Mechanical Components Retrieval

Reducing the setup time for a new production line is critical to the success of a manufacturer within the current competitive and cost-conscious market. To this end, being able to reuse already available machines, toolings and parts is paramount. However, matching a large warehouse of previously engineered parts to a new component to produce, is often more a matter of art and personal expertise rather than predictable science. In order to ease this process we developed a database retrieval approach for mechanical components that is able to deal with both rigid matching and deformable shapes. The intended use for the system is to match parts acquired with a 3D scanning system to a large database of components and to supply a list of results sorted according with a metric that expresses a structural distance. © 2012 IFIP International Federation for Information Processing

From Image Synthesis to Image Analysis: Using Human Animation Models to Guide Feature Extraction

We show that we can effectively fit complex animation models to noisy image data. Our approach is based on robust least-squares adjustment and takes advantage of three complementary sources of information: stereo data, silhouette edges and 2--D feature points. In this way, complete head models---including ears and hair---can be acquired with a cheap and entirely passive sensor, such as an ordinary video camera. The motion parameters of limbs can be similarly captured. They can then be fed to existing animation software to produce synthetic sequences

Modeling Human Bodies from Video Sequences

In this paper, we show that, given video sequences of a moving person acquired with a multi-camera system, we can track joint locations during the movement and recover shape information. We outline techniques for fitting a simplified model to the noisy 3--D data extracted from the images and a new tracking process based on least squares matching is presented. The recovered shape and motion parameters can be used to either reconstruct the original sequence or to allow other animation models to mimic the subject's actions. Our utlimate goal is to automate the process of building complete and realistic animation models of humans, given a set of video sequences. Keywords: Human Animation, Image Sequences, 3--D Tracking, Stereo, Model Fitting 1. INTRODUCTION Synthetic modeling of human bodies and the simulation of motion is a longstanding problem in animation and much work is involved before a near-realistic performance can be achieved. At present, it takes an experienced designer a very ..

Monocular body pose estimation by color histograms and point tracking

Abstract. Accurate markerless motion capture systems rely on images that allow segmentation of the person in the foreground. While the accuracy of such approaches is comparable to marker based systems, the segmentation step makes strong restrictions to the capture environment, e.g. homogenous clothing or background, constant lighting etc. In our approach a template model is fitted to images by an Analysis-by-Synthesis method, which doesn’t need explicit segmentation or homogenous clothing and gives reliable results even with non-static cluttered background

MARKERLESS FULL BODY SHAPE AND MOTION CAPTURE FROM VIDEO SEQUENCES

We develop a framework for 3–D shape and motion recovery of articulated deformable objects. We propose a formalism that incorporates the use of implicit surfaces into earlier robotics approaches that were designed to handle articulated structures. We demonstrate its effectiveness for human body modeling from video sequences. Our method is both robust and generic. It could easily be applied to other shape and motion recovery problems.

View Invariant Gait Recognition

Recognition by gait is of particular interest since it is the biometric that is available at the lowest resolution, or when other biometrics are (intentionally) obscured. Gait as a biometric has now shown increasing recognition capability. There are many approaches and these show that recognition can achieve excellent performance on large databases. The majority of these approaches are planar 2D, largely since the early large databases featured subjects walking in a plane normal to the camera view. To extend deployment capability, we need viewpoint invariant gait biometrics. We describe approaches where viewpoint invariance is achieved by 3D approaches or in 2D. In the first group the identification relies on parameters extracted from the 3D body deformation during walking. These methods use several video cameras and the 3D reconstruction is achieved after a camera calibration process. On the other hand, the 2D gait biometric approaches use a single camera, usually positioned perpendicular to the subject’s walking direction. Because in real surveillance scenarios a system that operates in an unconstrained environment is necessary, many of the recent gait analysis approaches are orientated towards viewinvariant gait recognition