Low-cost interactive active monocular range finder

This paper describes a low-cost interactive active monocular range finder and illustrates the effect of introducing interactivity to the range acquisition process. The range finder consists of only one camera and a laser pointer, to which three LEDs are attached. When a user scans the laser along surfaces of objects, the camera captures the image of spots (one from the laser, and the others from LEDs), and triangulation is carried out using the camera\u27s viewing direction and the optical axis of the laser. The user interaction allows the range finder to acquire range data in which the sampling rate varies across the object depending on the underlying surface structures. Moreover, the processes of separating objects from the background and/or finding parts in the object can be achieved using the operator\u27s knowledge of the objects

Three-Dimensional Biplanar Reconstruction of the Scoliotic Spine for Standard Clinical Setup

Tese de Doutoramento. Engenharia Informática. Faculdade de Engenharia. Universidade do Porto. 201

TECHNIKI ROZPOZNAWANIA TWARZY

The problem of face recognition is discussed. The main methods of recognition are considered. The calibrated stereo pair for the face and calculating the depth map by the correlation algorithm are used. As a result, a 3D mask of the face is obtained. Using three anthropomorphic points, then constructed a coordinate system that ensures a possibility of superposition of the tested mask.Omawiany jest problem rozpoznawania twarzy. Rozważane są główne metody rozpoznawania. Użyta zostaje skalibrowana para stereo dla twarzy oraz obliczanie mapy głębokości poprzez algorytm korelacji. W wyniku takiego, uzyskiwana jest maska twarzy w wymiarze 3D. Użycie trzech antropomorficznych punktów, a następnie skonstruowany systemu współrzędnych zapewnia możliwość nakładania się przetestowanej maski

Measurement of the branching fraction for the decay neutral kaon(long) going to pion-antipion-positron-electron in the high M(pi-pi) invariant mass region

A measurement of the branching fraction for the rare decay K0L → pi+pi-e +e- was performed using data collected from experiment E871 which ran at the AGS of Brookhaven National Laboratory. Analysis of the data revealed 27.7 +/- 7.4 signal events in the signal region, 0.4905 GeV \u3c Mpipiee \u3c 0.505 GeV together with 13.3 +/- 3.7 background events. The branching fractions of ( 8.5+/-2.3stat +/-1.0sys ) x 10-6 using the phenomenological model acceptance and ( 2.3+/-0.6stat +/-0.3sys ) x 10-6 using the chiral perturbation model represent the first measurements for K0L → pi+pi-e +e- in the dipion invariant mass region 0.475 GeV \u3c Mpipi \u3c 0.497 GeV. These results support the prediction of chiral perturbation theory rather than that of the phenomenological model

A measurement of the decay rate for the process kaon(L) going to positive muon negative muon

A sample of 87 events of the GIM suppressed decay K\sb{\rm L} \to \mu\sp+\mu\sp- were observed in an experiment performed in 1988 at the Brookhaven National Laboratory. Concurrently, 8,887 examples of the CP-violating decay K\sb{\rm L} \to \pi\sp+\pi\sp- were also seen. The apparatus consisted of a double-magnet spectrometer as well as electromagnetic and muon detector systems. From the previously measured branching ratio for K\sb{\rm L} \to \pi\sp+\pi\sp- and the different instrumental acceptances of the detector for the two decays, the data sample was normalized to the effective number of K\sb{\rm L} decays observed. A value for the ratio (K\sb{\rm L} \to \mu\sp+\mu\sp-)/(K\sb{\rm L} \to anything) of (5.7 $\pm$ 0.6(stat.) $\pm$ 0.3(syst.)) $\times$ 10\sp{-9} was obtained

3D indoor topological modelling based on homotopy continuation

Indoor navigation is important for various applications such as disaster management, building modelling and safety analysis. In the last decade, the indoor environment has been a focus of extensive research that includes the development of indoor data acquisition techniques, three-dimensional (3D) data modelling and indoor navigation. 3D indoor navigation modelling requires a valid 3D geometrical model that can be represented as a cell complex: a model without any gap or intersection such that the two cells, a room and corridor, should perfectly touch each other. This research is to develop a method for 3D topological modelling of an indoor navigation network using a geometrical model of an indoor building environment. To reduce the time and cost of the surveying process, a low-cost non-contact range-based surveying technique was used to acquire indoor building data. This technique is rapid as it requires a shorter time than others, but the results show inconsistencies in the horizontal angles for short distances in indoor environments. The rangefinder was calibrated using the least squares adjustment and a polynomial kernel. A method of combined interval analysis and homotopy continuation was developed to model the uncertainty level and minimize error of the non-contact range-based surveying techniques used in an indoor building environment. Finally, a method of 3D indoor topological building modelling was developed as a base for building models which include 3D geometry, topology and semantic information. The developed methods in this research can locate a low-cost, efficient and affordable procedure for developing a disaster management system in the near-future

Acquisition and modeling of 3D irregular objects.

by Sai-bun Wong.Thesis (M.Phil.)--Chinese University of Hong Kong, 1994.Includes bibliographical references (leaves 127-131).Abstract --- p.vAcknowledgment --- p.viiChapter 1 --- Introduction --- p.1-8Chapter 1.1 --- Overview --- p.2Chapter 1.2 --- Survey --- p.4Chapter 1.3 --- Objectives --- p.6Chapter 1.4 --- Thesis Organization --- p.7Chapter 2 --- Range Sensing --- p.9-30Chapter 2.1 --- Alternative Approaches to Range Sensing --- p.9Chapter 2.1.1 --- Size Constancy --- p.9Chapter 2.1.2 --- Defocusing --- p.11Chapter 2.1.3 --- Deconvolution --- p.14Chapter 2.1.4 --- Binolcular Vision --- p.18Chapter 2.1.5 --- Active Triangulation --- p.20Chapter 2.1.6 --- Time-of-Flight --- p.22Chapter 2.2 --- Transmitter and Detector in Active Sensing --- p.26Chapter 2.2.1 --- Acoustics --- p.26Chapter 2.2.2 --- Optics --- p.28Chapter 2.2.3 --- Microwave --- p.29Chapter 2.3 --- Conclusion --- p.29Chapter 3 --- Scanning Mirror --- p.31-47Chapter 3.1 --- Scanning Mechanisms --- p.31Chapter 3.2 --- Advantages of Scanning Mirror --- p.32Chapter 3.3 --- Feedback of Scanning Mirror --- p.33Chapter 3.4 --- Scanning Mirror Controller --- p.35Chapter 3.5 --- Point-to-Point Scanning --- p.39Chapter 3.6 --- Line Scanning --- p.39Chapter 3.7 --- Specifications and Measurements --- p.41Chapter 4 --- The Rangefinder with Reflectance Sensing --- p.48-58Chapter 4.1 --- Ambient Noises --- p.49Chapter 4.2 --- Occlusion/Shadow --- p.49Chapter 4.3 --- Accuracy and Precision --- p.50Chapter 4.4 --- Optics --- p.53Chapter 4.5 --- Range/Reflectance Crosstalk --- p.56Chapter 4.6 --- Summary --- p.58Chapter 5 --- Computer Generation of Range Map --- p.59-75Chapter 5.1 --- Homogenous Transformation --- p.61Chapter 5.2 --- From Global to Viewer Coordinate --- p.63Chapter 5.3 --- Z-buffering --- p.55Chapter 5.4 --- Generation of Range Map --- p.66Chapter 5.5 --- Experimental Results --- p.68Chapter 6 --- Characterization of Range Map --- p.76-90Chapter 6.1 --- Mean and Gaussian Curvature --- p.76Chapter 6.2 --- Methods of Curvature Generation --- p.78Chapter 6.2.1 --- Convolution --- p.78Chapter 6.2.2 --- Local Surface Patching --- p.81Chapter 6.3 --- Feature Extraction --- p.84Chapter 6.4 --- Conclusion --- p.85Chapter 7 --- Merging Multiple Characteristic Views --- p.91-119Chapter 7.1 --- Rigid Body Model --- p.91Chapter 7.2 --- Sub-rigid Body Model --- p.94Chapter 7.3 --- Probabilistic Relaxation Matching --- p.95Chapter 7.4 --- Merging the Sub-rigid Body Model --- p.99Chapter 7.5 --- Illustration --- p.101Chapter 7.6 --- Merging Multiple Characteristic Views --- p.104Chapter 7.7 --- Mislocation of Feature Extraction --- p.105Chapter 7.7.1 --- The Transform Matrix for Perfect Matching --- p.106Chapter 7.7.2 --- Introducing The Errors in Feature Set --- p.108Chapter 7.8 --- Summary --- p.113Chapter 8 --- Conclusion --- p.120-126References --- p.127-131Appendix A - Projection of Object --- p.A1-A2Appendix B - Performance Analysis on Rangefinder System --- p.B1-B16Appendix C - Matching of Two Characteristic views --- p.C1-C