Active haptic exploration for 3D shape reconstruction.

by Fung Wai Keung.Thesis (M.Phil.)--Chinese University of Hong Kong, 1996.Includes bibliographical references (leaves 146-151).Acknowledgements --- p.viiiAbstract --- p.1Chapter 1 --- Overview --- p.3Chapter 1.1 --- Tactile Sensing in Human and Robot --- p.4Chapter 1.1.1 --- Human Hands and Robotic Hands --- p.4Chapter 1.1.2 --- Mechanoreceptors in skin and Tactile Sensor Arrays --- p.7Chapter 1.2 --- Motivation --- p.12Chapter 1.3 --- Objectives --- p.13Chapter 1.4 --- Related Work --- p.14Chapter 1.4.1 --- Using Vision Alone --- p.15Chapter 1.4.2 --- Integration of Vision and Touch --- p.15Chapter 1.4.3 --- Using Touch Sensing Alone --- p.17Chapter 1.4.3.1 --- Ronald S. Fearing's Work --- p.18Chapter 1.4.3.2 --- Peter K. Allen's Work --- p.22Chapter 1.5 --- Outline --- p.26Chapter 2 --- Geometric Models --- p.27Chapter 2.1 --- Introduction --- p.27Chapter 2.2 --- Superquadrics --- p.27Chapter 2.2.1 --- 2D Superquadrics --- p.27Chapter 2.2.2 --- 3D Superquadrics --- p.29Chapter 2.3 --- Model Recovery of Superquadric Models --- p.31Chapter 2.3.1 --- Problem Formulation --- p.31Chapter 2.3.2 --- Least Squares Optimization --- p.33Chapter 2.4 --- Free-Form Deformations --- p.34Chapter 2.4.1 --- Bernstein Basis --- p.36Chapter 2.4.2 --- B-Spline Basis --- p.38Chapter 2.5 --- Other Geometric Models --- p.41Chapter 2.5.1 --- Generalized Cylinders --- p.41Chapter 2.5.2 --- Hyperquadrics --- p.42Chapter 2.5.3 --- Polyhedral Models --- p.44Chapter 2.5.4 --- Function Representation --- p.45Chapter 3 --- Sensing Strategy --- p.54Chapter 3.1 --- Introduction --- p.54Chapter 3.2 --- Sensing Algorithm --- p.55Chapter 3.2.1 --- Assumption of objects --- p.55Chapter 3.2.2 --- Haptic Exploration Procedures --- p.56Chapter 3.3 --- Contour Tracing --- p.58Chapter 3.4 --- Tactile Sensor Data Preprocessing --- p.59Chapter 3.4.1 --- Data Transformation and Sensor Calibration --- p.60Chapter 3.4.2 --- Noise Filtering --- p.61Chapter 3.5 --- Curvature Determination --- p.64Chapter 3.6 --- Step Size Determination --- p.73Chapter 4 --- 3D Shape Reconstruction --- p.80Chapter 4.1 --- Introduction --- p.80Chapter 4.2 --- Correspondence Problem --- p.81Chapter 4.2.1 --- Affine Invariance Property of B-splines --- p.84Chapter 4.2.2 --- Point Inversion Problem --- p.87Chapter 4.3 --- Parameter Triple Interpolation --- p.91Chapter 4.4 --- 3D Object Shape Reconstruction --- p.94Chapter 4.4.1 --- Heuristic Approach --- p.94Chapter 4.4.2 --- Closed Contour Recovery --- p.97Chapter 4.4.3 --- Control Lattice Recovery --- p.102Chapter 5 --- Implementation --- p.105Chapter 5.1 --- Introduction --- p.105Chapter 5.2 --- Implementation Tool - MATLAB --- p.105Chapter 5.2.1 --- Optimization Toolbox --- p.107Chapter 5.2.2 --- Splines Toolbox --- p.108Chapter 5.3 --- Geometric Model Implementation --- p.109Chapter 5.3.1 --- FFD Examples --- p.111Chapter 5.4 --- Shape Reconstruction Implementation --- p.112Chapter 5.5 --- 3D Model Reconstruction Examples --- p.120Chapter 5.5.1 --- Example 1 --- p.120Chapter 5.5.2 --- Example 2 --- p.121Chapter 6 --- Conclusion --- p.128Chapter 6.1 --- Future Work --- p.129Appendix --- p.133Bibliography --- p.14

A New Approach to Model Parameter Determination of Self-Potential Data using Memory-based Hybrid Dragonfly Algorithm

A new approach based on global optimization technique is applied to invert Self-Potential (SP) data which is a highly nonlinear inversion problem. This technique is called Memory-based Hybrid Dragonfly Algorithm (MHDA). This algorithm is proposed to balance out the high exploration behavior of Dragonfly Algorithm (DA), which causes a low convergence rate and often leads to the local optimum solution. MHDA was developed by adding internal memory and iterative level hybridization into DA which successfully balanced the exploration and exploitation behaviors of DA. In order to assess the performance of MHDA, it is firstly implemented to invert the single and multiple noises contaminated in synthetic SP data, which were caused by several simple geometries of buried anomalies: sphere and inclined sheet. MHDA is subsequently implemented to invert the field SP data for several cases: buried metallic drum, landslide, and Lumpur Sidoarjo (LUSI) embankment anomalies. As a stochastic method, MHDA is able to provide Posterior Distribution Model (PDM), which contains possible solutions of the SP data inversion. PDM is obtained from the exploration behavior of MHDA. All accepted models as PDM have a lower misfit value than the specified tolerance value of the objective function in the inversion process. In this research, solutions of the synthetic and field SP data inversions are estimated by the median value of PDM. Furthermore, the uncertainty value of obtained solutions can be estimated by the standard deviation value of PDM. The inversion results of synthetic and field SP data show that MHDA is able to estimate the solutions and the uncertainty values of solutions well. It indicates that MHDA is a good and an innovative technique to be implemented in solving the SP data inversion problem

On the Geometry of the Moduli Space of Certain Lattice Polarized K3 Surfaces and Their Picard-Fuchs Operators

K3 surfaces have a long and rich study in mathematics, and more recently in physics via string theory. Often, K3 surfaces come in multiparameter families - the parameters describing these surfaces fit together to form their own geometric space, a so-called moduli space. In particular, the moduli spaces of K3 surfaces equipped with a lattice polarization can sometimes be constructed explicitly, which subsequently reveals important information about the original K3 surface. In this work, we construct such families explicitly from certain rational elliptic surfaces via the so-called mixed-twist construction of Doran & Malmendier, which in turn produces the moduli space. After identifying the lattice polarization by computing Jacobian elliptic fibrations, we find a rich differential geometric content imparted to the moduli space - an integrable holomorphic conformal structure - via quadratic relations satisfied by the period integrals of the K3 surface. This geometry allows one to compute crucial data about the K3 surface family, the Picard-Fuchs operators, by applying a general programme on uniformizing differential equations discovered by Sasaki & Yoshida. In physics, this differential geometric data is known as a flat special geometry, and has implications for a type of supersymmetric quantum field theory associated with the K3 surface. Via the mixed-twist construction, this is related to Nf= 4 Seiberg-Witten curves from N = 2 SU(2) super Yang-Mills theory with various mass configurations. We show as well how one can restrict the moduli, leading to subvarieties of the moduli space on which the lattice polarization extends. This can allow one to construct interesting families of Calabi-Yau manifolds, which are of crucial importance in string theory as well. Moreover, we study how other data that governs the complex structure of the elliptic fibres of certain generic fibrations determines global information about a Jacobian elliptic K3 surface in terms of string theoretic and index theoretic terms via holomorphic anomalies

Neural Deformable Models for 3D Bi-Ventricular Heart Shape Reconstruction and Modeling from 2D Sparse Cardiac Magnetic Resonance Imaging

We propose a novel neural deformable model (NDM) targeting at the reconstruction and modeling of 3D bi-ventricular shape of the heart from 2D sparse cardiac magnetic resonance (CMR) imaging data. We model the bi-ventricular shape using blended deformable superquadrics, which are parameterized by a set of geometric parameter functions and are capable of deforming globally and locally. While global geometric parameter functions and deformations capture gross shape features from visual data, local deformations, parameterized as neural diffeomorphic point flows, can be learned to recover the detailed heart shape.Different from iterative optimization methods used in conventional deformable model formulations, NDMs can be trained to learn such geometric parameter functions, global and local deformations from a shape distribution manifold. Our NDM can learn to densify a sparse cardiac point cloud with arbitrary scales and generate high-quality triangular meshes automatically. It also enables the implicit learning of dense correspondences among different heart shape instances for accurate cardiac shape registration. Furthermore, the parameters of NDM are intuitive, and can be used by a physician without sophisticated post-processing. Experimental results on a large CMR dataset demonstrate the improved performance of NDM over conventional methods.Comment: Accepted by ICCV 202

Clifford wavelets for fetal ECG extraction

Analysis of the fetal heart rate during pregnancy is essential for monitoring the proper development of the fetus. Current fetal heart monitoring techniques lack the accuracy in fetal heart rate monitoring and features acquisition, resulting in diagnostic medical issues. The challenge lies in the extraction of the fetal ECG from the mother's ECG during pregnancy. This approach has the advantage of being a reliable and non-invasive technique. For this aim, we propose in this paper a wavelet/multi-wavelet method allowing to extract perfectly the feta ECG parameters from the abdominal mother ECG. The method is essentially due to the exploitation of Clifford wavelets as recent variants in the field. We prove that these wavelets are more efficient and performing against classical ones. The experimental results are therefore due to two basic classes of wavelets and multi-wavelets. A first-class is the classical Haar Schauder, and a second one is due to Clifford valued wavelets and multi-wavelets. These results showed that wavelets/multiwavelets are already good bases for the FECG processing, provided that Clifford ones are the best.Comment: 21 pages, 8 figures, 1 tabl

Particle Swarm Optimization and Uncertainty Assessment in Inverse Problems

Most inverse problems in the industry (and particularly in geophysical exploration) are highly underdetermined because the number of model parameters too high to achieve accurate data predictions and because the sampling of the data space is scarce and incomplete; it is always affected by different kinds of noise. Additionally, the physics of the forward problem is a simplification of the reality. All these facts result in that the inverse problem solution is not unique; that is, there are different inverse solutions (called equivalent), compatible with the prior information that fits the observed data within similar error bounds. In the case of nonlinear inverse problems, these equivalent models are located in disconnected flat curvilinear valleys of the cost-function topography. The uncertainty analysis consists of obtaining a representation of this complex topography via different sampling methodologies. In this paper, we focus on the use of a particle swarm optimization (PSO) algorithm to sample the region of equivalence in nonlinear inverse problems. Although this methodology has a general purpose, we show its application for the uncertainty assessment of the solution of a geophysical problem concerning gravity inversion in sedimentary basins, showing that it is possible to efficiently perform this task in a sampling-while-optimizing mode. Particularly, we explain how to use and analyze the geophysical models sampled by exploratory PSO family members to infer different descriptors of nonlinear uncertainty

Análise de Movimento Não Rígido em Visão por Computador

Neste artigo são apresentadas várias metodologias actualmente existentes, no domínio da Visão por Computador, para a análise de movimento não rígido e são indicados diversos exemplos de aplicações. Assim o movimento não rígido é classificado e, para cada classe resultante, são indicadas as restrições e as condições inerentes e verificados alguns trabalhos realizados no seu âmbito. Como as questões de análise de movimento e modelização da forma se tornam inseparáveis quando se considera o movimento do tipo não rígido, a modelização sugere uma classificação possível da forma não rígida e do movimento. Assim são também apresentados modelos de forma para objectos deformáveis e indicados vários exemplos de aplicações. Com este estudo, de certo modo aprofundado, das várias metodologias, e suas aplicações, existentes no domínio da análise de movimento não rígido, espera-se contribuir para o seu desenvolvimento, dada a actual carência de boas revisões do estado da arte neste domínio.In this article several methodologies actually existent, in the Computer Vision domain, for non-rigid movement analysis are presented and several examples of applications are indicated. Thus the non-rigid movement is classified and, for each resulting class, the restrictions and the inherent conditions are presented and some works accomplished in its ambit are verified. As the questions of movement and shape analysis becomes non-separable when its considered the movement of the non-rigid type, the shape models also suggests a possible classification of the non-rigid shape and of the movement. Thus shape models for deformable objects will be presented and some examples of applications indicated. With this study, in certain way deep, of several methodologies, and its applications, existent in the domain of the non-rigid movement analysis, the authors hope to contribute for its development, given the actual lack of good state of the art revisions in this domain

Superquadric representation of scenes from multi-view range data

Object representation denotes representing three-dimensional (3D) real-world objects with known graphic or mathematic primitives recognizable to computers. This research has numerous applications for object-related tasks in areas including computer vision, computer graphics, reverse engineering, etc. Superquadrics, as volumetric and parametric models, have been selected to be the representation primitives throughout this research. Superquadrics are able to represent a large family of solid shapes by a single equation with only a few parameters. This dissertation addresses superquadric representation of multi-part objects and multiobject scenes. Two issues motivate this research. First, superquadric representation of multipart objects or multi-object scenes has been an unsolved problem due to the complex geometry of objects. Second, superquadrics recovered from single-view range data tend to have low confidence and accuracy due to partially scanned object surfaces caused by inherent occlusions. To address these two problems, this dissertation proposes a multi-view superquadric representation algorithm. By incorporating both part decomposition and multi-view range data, the proposed algorithm is able to not only represent multi-part objects or multi-object scenes, but also achieve high confidence and accuracy of recovered superquadrics. The multi-view superquadric representation algorithm consists of (i) initial superquadric model recovery from single-view range data, (ii) pairwise view registration based on recovered superquadric models, (iii) view integration, (iv) part decomposition, and (v) final superquadric fitting for each decomposed part. Within the multi-view superquadric representation framework, this dissertation proposes a 3D part decomposition algorithm to automatically decompose multi-part objects or multiobject scenes into their constituent single parts consistent with human visual perception. Superquadrics can then be recovered for each decomposed single-part object. The proposed part decomposition algorithm is based on curvature analysis, and includes (i) Gaussian curvature estimation, (ii) boundary labeling, (iii) part growing and labeling, and (iv) post-processing. In addition, this dissertation proposes an extended view registration algorithm based on superquadrics. The proposed view registration algorithm is able to handle deformable superquadrics as well as 3D unstructured data sets. For superquadric fitting, two objective functions primarily used in the literature have been comprehensively investigated with respect to noise, viewpoints, sample resolutions, etc. The objective function proved to have better performance has been used throughout this dissertation. In summary, the three algorithms (contributions) proposed in this dissertation are generic and flexible in the sense of handling triangle meshes, which are standard surface primitives in computer vision and graphics. For each proposed algorithm, the dissertation presents both theory and experimental results. The results demonstrate the efficiency of the algorithms using both synthetic and real range data of a large variety of objects and scenes. In addition, the experimental results include comparisons with previous methods from the literature. Finally, the dissertation concludes with a summary of the contributions to the state of the art in superquadric representation, and presents possible future extensions to this research