Apresentação interativa de uma Unidade de Conservação utilizando recursos de imersão 360° do Google Street View

A Unidade de Conservação (UC) denominada Parque Ecológico dos Jequitibás, localizada no Distrito Federal, é voltada para visitação. Visando a utilização de novas tecnologias, este trabalho teve como objetivo criar uma apresentação interativa desta UC, utilizando imagens panorâmicas 360° exibidas por meio de recursos de imersão 360° do Street View do Google Maps. Assim, foram abordados o histórico, recursos, captura, processamento, peculiaridades e inserção das imagens panorâmicas 360° na Ficha Local da UC no Google Maps. Obteve-se como principal resultado a oferta de conteúdo interativo que pode ser considerado um recurso de acessibilidade, fonte de informação, recurso didático em ensino, geração de renda por meio da replicação do método e fonte de registro temporal de informações

Creating virtual environment by 3D computer vision techniques.

Lao Tze Kin Jackie.Thesis (M.Phil.)--Chinese University of Hong Kong, 2000.Includes bibliographical references (leaves 83-87).Abstracts in English and Chinese.Chapter 1 --- Introduction --- p.1Chapter 1.1 --- 3D Modeling using Active Contour --- p.3Chapter 1.2 --- Rectangular Virtual Environment Construction --- p.5Chapter 1.3 --- Thesis Contribution --- p.7Chapter 1.4 --- Thesis Outline --- p.7Chapter 2 --- Background --- p.9Chapter 2.1 --- Panoramic Representation --- p.9Chapter 2.1.1 --- Static Mosaic --- p.10Chapter 2.1.2 --- Advanced Mosaic Representation --- p.15Chapter 2.1.3 --- Panoramic Walkthrough --- p.17Chapter 2.2 --- Active Contour Model --- p.24Chapter 2.2.1 --- Parametric Active Contour Model --- p.28Chapter 2.3 --- 3D Shape Estimation --- p.29Chapter 2.3.1 --- Model Formation with both intrinsic and extrinsic parameters --- p.29Chapter 2.3.2 --- Model Formation with only Intrinsic Parameter and Epipo- lar Geometry --- p.32Chapter 3 --- 3D Object Modeling using Active Contour --- p.39Chapter 3.1 --- Point Acquisition Through Active Contour --- p.40Chapter 3.2 --- Object Segmentation and Panorama Generation --- p.43Chapter 3.2.1 --- Object Segmentation --- p.44Chapter 3.2.2 --- Panorama Construction --- p.44Chapter 3.3 --- 3D modeling and Texture Mapping --- p.45Chapter 3.3.1 --- Texture Mapping From Parameterization --- p.46Chapter 3.4 --- Experimental Results --- p.48Chapter 3.4.1 --- Experimental Error --- p.49Chapter 3.4.2 --- Comparison between Virtual 3D Model with Actual Model --- p.54Chapter 3.4.3 --- Comparison with Existing Techniques --- p.55Chapter 3.5 --- Discussion --- p.55Chapter 4 --- Rectangular Virtual Environment Construction --- p.57Chapter 4.1 --- Rectangular Environment Construction using Traditional (Hori- zontal) Panoramic Scenes --- p.58Chapter 4.1.1 --- Image Manipulation --- p.59Chapter 4.1.2 --- Panoramic Mosaic Creation --- p.59Chapter 4.1.3 --- Measurement of Panning Angles --- p.61Chapter 4.1.4 --- Estimate Side Ratio --- p.62Chapter 4.1.5 --- Wireframe Modeling and Cylindrical Projection --- p.63Chapter 4.1.6 --- Experimental Results --- p.66Chapter 4.2 --- Rectangular Environment Construction using Vertical Panoramic Scenes --- p.67Chapter 4.3 --- Building virtual environments for complex scenes --- p.73Chapter 4.4 --- Comparison with Existing Techniques --- p.75Chapter 4.5 --- Discussion and Future Directions --- p.77Chapter 5 --- System Integration --- p.79Chapter 6 --- Conclusion --- p.81Bibliography --- p.8

Algorithms, Protocols & Systems for Remote Observation Using Networked Robotic Cameras

Emerging advances in robotic cameras, long-range wireless networking, and distributed sensors make feasible a new class of hybrid teleoperated/autonomous robotic remote "observatories" that can allow groups of peoples, via the Internet, to observe, record, and index detailed activity occurred in remote site. Equipped with robotic pan-tilt actuation mechanisms and a high-zoom lens, the camera can cover a large region with very high spatial resolution and allows for observation at a distance. High resolution motion panorama is the most nature data representation. We develop algorithms and protocols for high resolution motion panorama. We discover and prove the projection invariance and achieve real time image alignment. We propose a minimum variance based incremental frame alignment algorithm to minimize the accumulation of alignment error in incremental image alignment and ensure the quality of the panorama video over the long run. We propose a Frame Graph based panorama documentation algorithm to manage the large scale data involved in the online panorama video documentation. We propose a on-demand high resolution panorama video-streaming system that allows on-demand sharing of a high-resolution motion panorama and efficiently deals with multiple concurrent spatial-temporal user requests. In conclusion, our research work on high resolution motion panorama have significantly improve the efficiency and accuracy of image alignment, panorama video quality, data organization, and data storage and retrieving in remote observation using networked robotic cameras

Applying image processing techniques to pose estimation and view synthesis.

Fung Yiu-fai Phineas.Thesis (M.Phil.)--Chinese University of Hong Kong, 1999.Includes bibliographical references (leaves 142-148).Abstracts in English and Chinese.Chapter 1 --- Introduction --- p.1Chapter 1.1 --- Model-based Pose Estimation --- p.3Chapter 1.1.1 --- Application - 3D Motion Tracking --- p.4Chapter 1.2 --- Image-based View Synthesis --- p.4Chapter 1.3 --- Thesis Contribution --- p.7Chapter 1.4 --- Thesis Outline --- p.8Chapter 2 --- General Background --- p.9Chapter 2.1 --- Notations --- p.9Chapter 2.2 --- Camera Models --- p.10Chapter 2.2.1 --- Generic Camera Model --- p.10Chapter 2.2.2 --- Full-perspective Camera Model --- p.11Chapter 2.2.3 --- Affine Camera Model --- p.12Chapter 2.2.4 --- Weak-perspective Camera Model --- p.13Chapter 2.2.5 --- Paraperspective Camera Model --- p.14Chapter 2.3 --- Model-based Motion Analysis --- p.15Chapter 2.3.1 --- Point Correspondences --- p.16Chapter 2.3.2 --- Line Correspondences --- p.18Chapter 2.3.3 --- Angle Correspondences --- p.19Chapter 2.4 --- Panoramic Representation --- p.20Chapter 2.4.1 --- Static Mosaic --- p.21Chapter 2.4.2 --- Dynamic Mosaic --- p.22Chapter 2.4.3 --- Temporal Pyramid --- p.23Chapter 2.4.4 --- Spatial Pyramid --- p.23Chapter 2.5 --- Image Pre-processing --- p.24Chapter 2.5.1 --- Feature Extraction --- p.24Chapter 2.5.2 --- Spatial Filtering --- p.27Chapter 2.5.3 --- Local Enhancement --- p.31Chapter 2.5.4 --- Dynamic Range Stretching or Compression --- p.32Chapter 2.5.5 --- YIQ Color Model --- p.33Chapter 3 --- Model-based Pose Estimation --- p.35Chapter 3.1 --- Previous Work --- p.35Chapter 3.1.1 --- Estimation from Established Correspondences --- p.36Chapter 3.1.2 --- Direct Estimation from Image Intensities --- p.49Chapter 3.1.3 --- Perspective-3-Point Problem --- p.51Chapter 3.2 --- Our Iterative P3P Algorithm --- p.58Chapter 3.2.1 --- Gauss-Newton Method --- p.60Chapter 3.2.2 --- Dealing with Ambiguity --- p.61Chapter 3.2.3 --- 3D-to-3D Motion Estimation --- p.66Chapter 3.3 --- Experimental Results --- p.68Chapter 3.3.1 --- Synthetic Data --- p.68Chapter 3.3.2 --- Real Images --- p.72Chapter 3.4 --- Discussions --- p.73Chapter 4 --- Panoramic View Analysis --- p.76Chapter 4.1 --- Advanced Mosaic Representation --- p.76Chapter 4.1.1 --- Frame Alignment Policy --- p.77Chapter 4.1.2 --- Multi-resolution Representation --- p.77Chapter 4.1.3 --- Parallax-based Representation --- p.78Chapter 4.1.4 --- Multiple Moving Objects --- p.79Chapter 4.1.5 --- Layers and Tiles --- p.79Chapter 4.2 --- Panorama Construction --- p.79Chapter 4.2.1 --- Image Acquisition --- p.80Chapter 4.2.2 --- Image Alignment --- p.82Chapter 4.2.3 --- Image Integration --- p.88Chapter 4.2.4 --- Significant Residual Estimation --- p.89Chapter 4.3 --- Advanced Alignment Algorithms --- p.90Chapter 4.3.1 --- Patch-based Alignment --- p.91Chapter 4.3.2 --- Global Alignment (Block Adjustment) --- p.92Chapter 4.3.3 --- Local Alignment (Deghosting) --- p.93Chapter 4.4 --- Mosaic Application --- p.94Chapter 4.4.1 --- Visualization Tool --- p.94Chapter 4.4.2 --- Video Manipulation --- p.95Chapter 4.5 --- Experimental Results --- p.96Chapter 5 --- Panoramic Walkthrough --- p.99Chapter 5.1 --- Problem Statement and Notations --- p.100Chapter 5.2 --- Previous Work --- p.101Chapter 5.2.1 --- 3D Modeling and Rendering --- p.102Chapter 5.2.2 --- Branching Movies --- p.103Chapter 5.2.3 --- Texture Window Scaling --- p.104Chapter 5.2.4 --- Problems with Simple Texture Window Scaling --- p.105Chapter 5.3 --- Our Walkthrough Approach --- p.106Chapter 5.3.1 --- Cylindrical Projection onto Image Plane --- p.106Chapter 5.3.2 --- Generating Intermediate Frames --- p.108Chapter 5.3.3 --- Occlusion Handling --- p.114Chapter 5.4 --- Experimental Results --- p.116Chapter 5.5 --- Discussions --- p.116Chapter 6 --- Conclusion --- p.121Chapter A --- Formulation of Fischler and Bolles' Method for P3P Problems --- p.123Chapter B --- Derivation of z1 and z3 in terms of z2 --- p.127Chapter C --- Derivation of e1 and e2 --- p.129Chapter D --- Derivation of the Update Rule for Gauss-Newton Method --- p.130Chapter E --- Proof of (λ1λ2-λ 4）>〉0 --- p.132Chapter F --- Derivation of φ and hi --- p.133Chapter G --- Derivation of w1j to w4j --- p.134Chapter H --- More Experimental Results on Panoramic Stitching Algorithms --- p.138Bibliography --- p.14

Three-Dimensional Reconstruction of Braided River Morphology and Morphodynamics with Structure-from-Motion Photogrammetry

PhDThe recent emergence of Structure-from-Motion Photogrammetry (SfM) has created a cost-effective alternative to conventional laser scanning for the production of high-resolution topographic datasets. There has been an explosion of applications of SfM within the geomorphological community in recent years, however, the focus of these has largely been small-scale (102 – 103 m2), building on innovations in low altitude Unmanned Aircraft Systems (UAS). This thesis examines the potential to extend the scope of SfM photogrammetry in order to quantify of landscape scale processes. This is examined through repeat surveys of a ~35 km2 reach of the Dart River, New Zealand. An initial SfM survey of this reach was conducted in April 2014, following a large landslide at the Slipstream debris fan. Validation of the resulting digital elevation models using Independent Control Point's (ICPs) suggested encouraging results, however benchmarking the survey against a long-range laser scanned surface indicated the presence of significant systematic errors associated with inaccurate estimation of the SfM bundle adjustment. Using a combination of scaled laboratory field experiments, this research aimed to develop and test photogrammetric data collection and modelling strategies to enhance modelling of 3D scene structure using limited constraints. A repeat survey in 2015 provided an opportunity to evaluate a new survey strategy, incorporating a convergent camera network and a priori measurement of camera pose. This resulted in halving of mean checkpoint residuals and a reduction in systematic error. The models produced for both 2014 and 2015 were compared using a DEM differencing (DoD) methodology to assess the applicability of wide-area SfM models for the analysis of geomorphic change detection. The systematic errors within the 2014 model confound reliable change detection, although strategies to correlate the two surveys and measure the residual change show promise. The future use of SfM over broad landscape scales has significant potential, however, this will require robust data collection and modelling strategies and improved error modelling to increase user confidence.This work has been supported by a Natural Environmental Research Council studentship (Grant number NE/L501797/

Characterization of errors in compositing panoramic images

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