Structure and motion estimation from apparent contours under circular motion

In this paper, we address the problem of recovering structure and motion from the apparent contours of a smooth surface. Fixed image features under circular motion and their relationships with the intrinsic parameters of the camera are exploited to provide a simple parameterization of the fundamental matrix relating any pair of views in the sequence. Such a parameterization allows a trivial initialization of the motion parameters, which all bear physical meaning. It also greatly reduces the dimension of the search space for the optimization problem, which can now be solved using only two epipolar tangents. In contrast to previous methods, the motion estimation algorithm introduced here can cope with incomplete circular motion and more widely spaced images. Existing techniques for model reconstruction from apparent contours are then reviewed and compared. Experiment on real data has been carried out and the 3D model reconstructed from the estimated motion is presented. © 2002 Elsevier Science B.V. All rights reserved.postprin

Robust recovery of shapes with unknown topology from the dual space

In this paper, we address the problem of reconstructing an object surface from silhouettes. Previous works by other authors have shown that, based on the principle of duality, surface points can be recovered, theoretically, as the dual to the tangent plane space of the object. In practice, however, the identification of tangent basis in the tangent plane space is not trivial given a set of discretely sampled data. This problem is further complicated by the existence of bi-tangents to the object surface. The key contribution of this paper is the introduction of epipolar parameterization in identifying a well-defined local tangent basis. This extends the applicability of existing dual space reconstruction methods to fairly complicated shapes, without making any explicit assumption on the object topology. We verify our approach with both synthetic and real-world data, and compare it both qualitatively and quantitatively with other popular reconstruction algorithms. Experimental results demonstrate that our proposed approach produces more accurate estimation, whilst maintaining reasonable robustness towards shapes with complex topologies. © 2007 IEEE.published_or_final_versio

A Survey of Methods for Volumetric Scene Reconstruction from Photographs

Scene reconstruction, the task of generating a 3D model of a scene given multiple 2D photographs taken of the scene, is an old and difficult problem in computer vision. Since its introduction, scene reconstruction has found application in many fields, including robotics, virtual reality, and entertainment. Volumetric models are a natural choice for scene reconstruction. Three broad classes of volumetric reconstruction techniques have been developed based on geometric intersections, color consistency, and pair-wise matching. Some of these techniques have spawned a number of variations and undergone considerable refinement. This paper is a survey of techniques for volumetric scene reconstruction

Model-Based Penetration Path Calculations for the Diagnosis of Multiple Trauma

In order to enhance TraumAID, a system that provides decision support in the initial definitive management of multiple trauma, TSARR (TraumAID System for Anatomical Representation and Reasoning) has been designed to address the need for greater depth in TraumAID\u27s understanding of anatomical reasoning. TSARR provides a framework for representing a three-dimensional model of relevant parts of the body; utilizing this model, TSARR is able to calculate three-dimensional representations of paths of injury, generated from wound locations input to the system. Using these paths, the system hypothesizes which anatomical structures in the patient might have been injured due to their location along a possible path of an injury. In the future, TraumAID will be able to utilize this information to focus its attention more accurately on specific areas of the body that have sustained injury. This work has been done in conjunction with the TraumAID project being conducted by Professor Bonnie Webber of the University of Pennsylvania and by Dr. John R. Clarke, MD, of the Medical College of Pennsylvania

3D Object Reconstruction using Multi-View Calibrated Images

In this study, two models are proposed, one is a visual hull model and another one is a 3D object reconstruction model. The proposed visual hull model, which is based on bounding edge representation, obtains high time performance which makes it to be one of the best methods. The main contribution of the proposed visual hull model is to provide bounding surfaces over the bounding edges, which results a complete triangular surface mesh. Moreover, the proposed visual hull model can be computed over the camera networks distributedly. The second model is a depth map based 3D object reconstruction model which results a watertight triangular surface mesh. The proposed model produces the result with acceptable accuracy as well as high completeness, only using stereo matching and triangulation. The contribution of this model is to playing with the 3D points to find the best reliable ones and fitting a surface over them

Identification and Reconstruction of Bullets from Multiple X-Rays

The 3D shape and position of objects inside the human body are commonly detected using Computed Tomography (CT) scanning. CT is an expensive diagnostic option in economically disadvantaged areas and the radiation dose experienced by the patient is significant. In this dissertation, we present a technique for reconstructing the 3D shape and position of bullets from multiple X-rays. This technique makes us of ubiquitous X-ray equipment and a small number of X-rays to reduce the radiation dose. Our work relies on Image Segmentation and Volume Reconstruction techniques. We present a method for segmenting bullets out of X-rays, based on their signature in intensity profiles. This signature takes the form of a distinct plateau which we model with a number of parameters. This model is used to identify horizontal and vertical line segments within an X-Ray corresponding to a bullet signature. Regions containing confluences of these line segments are selected as bullet candidates. The actual bullet is thresholded out of the region based on a range of intensities occupied by the intensity profiles that contributed to the region. A simple Volume Reconstruction algorithm is implemented that back-projects the silhouettes of bullets obtained from our segmentation technique. This algorithm operates on a 3D voxel volume represented as an octree. The reconstruction is reduced to the 2D case by reconstructing a slice of the voxel volume at a time. We achieve good results for our segmentation algorithm. When compared with a manual segmentation, our algorithm matches 90% of the bullet pixels in nine of the twelve test X-rays. Our reconstruction algorithm produces an acceptable results: It achieves a 70% match for a test case where we compare a simulated bullet with a reconstructed bullet

Robust surface modelling of visual hull from multiple silhouettes

Reconstructing depth information from images is one of the actively researched themes in computer vision and its application involves most vision research areas from object recognition to realistic visualisation. Amongst other useful vision-based reconstruction techniques, this thesis extensively investigates the visual hull (VH) concept for volume approximation and its robust surface modelling when various views of an object are available. Assuming that multiple images are captured from a circular motion, projection matrices are generally parameterised in terms of a rotation angle from a reference position in order to facilitate the multi-camera calibration. However, this assumption is often violated in practice, i.e., a pure rotation in a planar motion with accurate rotation angle is hardly realisable. To address this problem, at first, this thesis proposes a calibration method associated with the approximate circular motion. With these modified projection matrices, a resulting VH is represented by a hierarchical tree structure of voxels from which surfaces are extracted by the Marching cubes (MC) algorithm. However, the surfaces may have unexpected artefacts caused by a coarser volume reconstruction, the topological ambiguity of the MC algorithm, and imperfect image processing or calibration result. To avoid this sensitivity, this thesis proposes a robust surface construction algorithm which initially classifies local convex regions from imperfect MC vertices and then aggregates local surfaces constructed by the 3D convex hull algorithm. Furthermore, this thesis also explores the use of wide baseline images to refine a coarse VH using an affine invariant region descriptor. This improves the quality of VH when a small number of initial views is given. In conclusion, the proposed methods achieve a 3D model with enhanced accuracy. Also, robust surface modelling is retained when silhouette images are degraded by practical noise