A statistical shape model for deformable surface

This short paper presents a deformable surface registration scheme which is based on the statistical shape modelling technique. The method consists of two major processing stages, model building and model fitting. A statistical shape model is first built using a set of training data. Then the model is deformed and matched to the new data by a modified iterative closest point (ICP) registration process. The proposed method is tested on real 3-D facial data from BU-3DFE database. It is shown that proposed method can achieve a reasonable result on surface registration, and can be used for patient position monitoring in radiation therapy and potentially can be used for monitoring of the radiation therapy progress for head and neck patients by analysis of facial articulation

Consistent ICP for the registration of sparse and inhomogeneous point clouds

In this paper, we derive a novel iterative closest point (ICP) technique that performs point cloud alignment in a robust and consistent way. Traditional ICP techniques minimize the point-to-point distances, which are successful when point clouds contain no noise or clutter and moreover are dense and more or less uniformly sampled. In the other case, it is better to employ point-to-plane or other metrics to locally approximate the surface of the objects. However, the point-to-plane metric does not yield a symmetric solution, i.e. the estimated transformation of point cloud p to point cloud q is not necessarily equal to the inverse transformation of point cloud q to point cloud p. In order to improve ICP, we will enforce such symmetry constraints as prior knowledge and make it also robust to noise and clutter. Experimental results show that our method is indeed much more consistent and accurate in presence of noise and clutter compared to existing ICP algorithms

Point cloud segmentation using hierarchical tree for architectural models

Recent developments in the 3D scanning technologies have made the generation of highly accurate 3D point clouds relatively easy but the segmentation of these point clouds remains a challenging area. A number of techniques have set precedent of either planar or primitive based segmentation in literature. In this work, we present a novel and an effective primitive based point cloud segmentation algorithm. The primary focus, i.e. the main technical contribution of our method is a hierarchical tree which iteratively divides the point cloud into segments. This tree uses an exclusive energy function and a 3D convolutional neural network, HollowNets to classify the segments. We test the efficacy of our proposed approach using both real and synthetic data obtaining an accuracy greater than 90% for domes and minarets.Comment: 9 pages. 10 figures. Submitted in EuroGraphics 201

Dense Motion Estimation for Smoke

Motion estimation for highly dynamic phenomena such as smoke is an open challenge for Computer Vision. Traditional dense motion estimation algorithms have difficulties with non-rigid and large motions, both of which are frequently observed in smoke motion. We propose an algorithm for dense motion estimation of smoke. Our algorithm is robust, fast, and has better performance over different types of smoke compared to other dense motion estimation algorithms, including state of the art and neural network approaches. The key to our contribution is to use skeletal flow, without explicit point matching, to provide a sparse flow. This sparse flow is upgraded to a dense flow. In this paper we describe our algorithm in greater detail, and provide experimental evidence to support our claims.Comment: ACCV201

Evaluation of a Coherent Point Drift Algorithm for Breast Image Registration via Surface Markers

Breast Magnetic Resonance Imaging (MRI) is a reliable imagingtool for localization and evaluation of lesions prior to breast conservingsurgery (BCS). MR images typically will be used to determinethe size and location of the tumours before making the incisionin order to minimize the amount of tissue excised.The arm position and configuration of the breast during andprior to surgery are different and one question is whether it wouldbe possible to match the two configurations. This matching processcan potentially be used in development of tools to guide surgeonsin the incision process.Recently, a Thin-Plate-Spline (TPS) algorithm has been proposedto assess the feasibility of breast tissue matching using fiducialsurface markers in two different arm positions. The registrationalgorithm uses the surface markers only and does not employ theimage intensities.In this manuscript, we apply and evaluate a coherent point drift(CPD) algorithm for registration of three-dimensional breast MR imagesof six patient volunteers. In particular, we evaluate the resultsof the previous TPS registration technique to the proposed rigidCPD, affine CPD, and deformable CPD registration algorithms onthe same patient datasets.The preliminary results suggest that the CPD deformable registrationalgorithm is superior in correcting the motion of the breastcompared to CPD rigid, affine and TPS registration algorithms