Survey of image-based representations and compression techniques

In this paper, we survey the techniques for image-based rendering (IBR) and for compressing image-based representations. Unlike traditional three-dimensional (3-D) computer graphics, in which 3-D geometry of the scene is known, IBR techniques render novel views directly from input images. IBR techniques can be classified into three categories according to how much geometric information is used: rendering without geometry, rendering with implicit geometry (i.e., correspondence), and rendering with explicit geometry (either with approximate or accurate geometry). We discuss the characteristics of these categories and their representative techniques. IBR techniques demonstrate a surprising diverse range in their extent of use of images and geometry in representing 3-D scenes. We explore the issues in trading off the use of images and geometry by revisiting plenoptic-sampling analysis and the notions of view dependency and geometric proxies. Finally, we highlight compression techniques specifically designed for image-based representations. Such compression techniques are important in making IBR techniques practical.published_or_final_versio

Advanced visual rendering, gesture-based interaction and distributed delivery for immersive and interactive media services

The media industry is currently being pulled in the often-opposing directions of increased realism (high resolution, stereoscopic, large screen) and personalisation (selection and control of content, availability on many devices). A capture, production, delivery and rendering system capable of supporting both these trends is being developed by a consortium of European organisations including partners from the broadcast, film, telecoms and academic sectors, in the EU-funded FascinatE project. This paper reports on the latest project developments in the delivery network and end-user device domains, including advanced audiovisual rendering, computer analysis and scripting, content-aware distributed delivery and gesture-based interaction. The paper includes an overview of existing immersive media services and concludes with initial service concept descriptions and their market potential.Peer ReviewedPreprin

Tele-immersive display with live-streamed video.

Tang Wai-Kwan.Thesis (M.Phil.)--Chinese University of Hong Kong, 2001.Includes bibliographical references (leaves 88-95).Abstracts in English and Chinese.Abstract --- p.iAcknowledgement --- p.iiiChapter 1 --- Introduction --- p.1Chapter 1.1 --- Applications --- p.3Chapter 1.2 --- Motivation and Goal --- p.6Chapter 1.3 --- Thesis Outline --- p.7Chapter 2 --- Background and Related Work --- p.8Chapter 2.1 --- Panoramic Image Navigation --- p.8Chapter 2.2 --- Image Mosaicing --- p.9Chapter 2.2.1 --- Image Registration --- p.10Chapter 2.2.2 --- Image Composition --- p.12Chapter 2.3 --- Immersive Display --- p.13Chapter 2.4 --- Video Streaming --- p.14Chapter 2.4.1 --- Video Coding --- p.15Chapter 2.4.2 --- Transport Protocol --- p.18Chapter 3 --- System Design --- p.19Chapter 3.1 --- System Architecture --- p.19Chapter 3.1.1 --- Video Capture Module --- p.19Chapter 3.1.2 --- Video Streaming Module --- p.23Chapter 3.1.3 --- Stitching and Rendering Module --- p.24Chapter 3.1.4 --- Display Module --- p.24Chapter 3.2 --- Design Issues --- p.25Chapter 3.2.1 --- Modular Design --- p.25Chapter 3.2.2 --- Scalability --- p.26Chapter 3.2.3 --- Workload distribution --- p.26Chapter 4 --- Panoramic Video Mosaic --- p.28Chapter 4.1 --- Video Mosaic to Image Mosaic --- p.28Chapter 4.1.1 --- Assumptions --- p.29Chapter 4.1.2 --- Processing Pipeline --- p.30Chapter 4.2 --- Camera Calibration --- p.33Chapter 4.2.1 --- Perspective Projection --- p.33Chapter 4.2.2 --- Distortion --- p.36Chapter 4.2.3 --- Calibration Procedure --- p.37Chapter 4.3 --- Panorama Generation --- p.39Chapter 4.3.1 --- Cylindrical and Spherical Panoramas --- p.39Chapter 4.3.2 --- Homography --- p.41Chapter 4.3.3 --- Homography Computation --- p.42Chapter 4.3.4 --- Error Minimization --- p.44Chapter 4.3.5 --- Stitching Multiple Images --- p.46Chapter 4.3.6 --- Seamless Composition --- p.47Chapter 4.4 --- Image Mosaic to Video Mosaic --- p.49Chapter 4.4.1 --- Varying Intensity --- p.49Chapter 4.4.2 --- Video Frame Management --- p.50Chapter 5 --- Immersive Display --- p.52Chapter 5.1 --- Human Perception System --- p.52Chapter 5.2 --- Creating Virtual Scene --- p.53Chapter 5.3 --- VisionStation --- p.54Chapter 5.3.1 --- F-Theta Lens --- p.55Chapter 5.3.2 --- VisionStation Geometry --- p.56Chapter 5.3.3 --- Sweet Spot Relocation and Projection --- p.57Chapter 5.3.4 --- Sweet Spot Relocation in Vector Representation --- p.61Chapter 6 --- Video Streaming --- p.65Chapter 6.1 --- Video Compression --- p.66Chapter 6.2 --- Transport Protocol --- p.66Chapter 6.3 --- Latency and Jitter Control --- p.67Chapter 6.4 --- Synchronization --- p.70Chapter 7 --- Implementation and Results --- p.71Chapter 7.1 --- Video Capture --- p.71Chapter 7.2 --- Video Streaming --- p.73Chapter 7.2.1 --- Video Encoding --- p.73Chapter 7.2.2 --- Streaming Protocol --- p.75Chapter 7.3 --- Implementation Results --- p.76Chapter 7.3.1 --- Indoor Scene --- p.76Chapter 7.3.2 --- Outdoor Scene --- p.78Chapter 7.4 --- Evaluation --- p.78Chapter 8 --- Conclusion --- p.83Chapter 8.1 --- Summary --- p.83Chapter 8.2 --- Future Directions --- p.84Chapter A --- Parallax --- p.8

Omnidirectional Vision Based Topological Navigation

Goedemé T., Van Gool L., ''Omnidirectional vision based topological navigation'', Mobile robots navigation, pp. 172-196, Barrera Alejandra, ed., March 2010, InTech.status: publishe

Multiperspective mosaics and layered representation for scene visualization

This thesis documents the efforts made to implement multiperspective mosaicking for the purpose of mosaicking undervehicle and roadside sequences. For the undervehicle sequences, it is desired to create a large, high-resolution mosaic that may used to quickly inspect the entire scene shot by a camera making a single pass underneath the vehicle. Several constraints are placed on the video data, in order to facilitate the assumption that the entire scene in the sequence exists on a single plane. Therefore, a single mosaic is used to represent a single video sequence. Phase correlation is used to perform motion analysis in this case. For roadside video sequences, it is assumed that the scene is composed of several planar layers, as opposed to a single plane. Layer extraction techniques are implemented in order to perform this decomposition. Instead of using phase correlation to perform motion analysis, the Lucas-Kanade motion tracking algorithm is used in order to create dense motion maps. Using these motion maps, spatial support for each layer is determined based on a pre-initialized layer model. By separating the pixels in the scene into motion-specific layers, it is possible to sample each element in the scene correctly while performing multiperspective mosaicking. It is also possible to fill in many gaps in the mosaics caused by occlusions, hence creating more complete representations of the objects of interest. The results are several mosaics with each mosaic representing a single planar layer of the scene

Map-Based Localization for Unmanned Aerial Vehicle Navigation

Unmanned Aerial Vehicles (UAVs) require precise pose estimation when navigating in indoor and GNSS-denied / GNSS-degraded outdoor environments. The possibility of crashing in these environments is high, as spaces are confined, with many moving obstacles. There are many solutions for localization in GNSS-denied environments, and many different technologies are used. Common solutions involve setting up or using existing infrastructure, such as beacons, Wi-Fi, or surveyed targets. These solutions were avoided because the cost should be proportional to the number of users, not the coverage area. Heavy and expensive sensors, for example a high-end IMU, were also avoided. Given these requirements, a camera-based localization solution was selected for the sensor pose estimation. Several camera-based localization approaches were investigated. Map-based localization methods were shown to be the most efficient because they close loops using a pre-existing map, thus the amount of data and the amount of time spent collecting data are reduced as there is no need to re-observe the same areas multiple times. This dissertation proposes a solution to address the task of fully localizing a monocular camera onboard a UAV with respect to a known environment (i.e., it is assumed that a 3D model of the environment is available) for the purpose of navigation for UAVs in structured environments. Incremental map-based localization involves tracking a map through an image sequence. When the map is a 3D model, this task is referred to as model-based tracking. A by-product of the tracker is the relative 3D pose (position and orientation) between the camera and the object being tracked. State-of-the-art solutions advocate that tracking geometry is more robust than tracking image texture because edges are more invariant to changes in object appearance and lighting. However, model-based trackers have been limited to tracking small simple objects in small environments. An assessment was performed in tracking larger, more complex building models, in larger environments. A state-of-the art model-based tracker called ViSP (Visual Servoing Platform) was applied in tracking outdoor and indoor buildings using a UAVs low-cost camera. The assessment revealed weaknesses at large scales. Specifically, ViSP failed when tracking was lost, and needed to be manually re-initialized. Failure occurred when there was a lack of model features in the cameras field of view, and because of rapid camera motion. Experiments revealed that ViSP achieved positional accuracies similar to single point positioning solutions obtained from single-frequency (L1) GPS observations standard deviations around 10 metres. These errors were considered to be large, considering the geometric accuracy of the 3D model used in the experiments was 10 to 40 cm. The first contribution of this dissertation proposes to increase the performance of the localization system by combining ViSP with map-building incremental localization, also referred to as simultaneous localization and mapping (SLAM). Experimental results in both indoor and outdoor environments show sub-metre positional accuracies were achieved, while reducing the number of tracking losses throughout the image sequence. It is shown that by integrating model-based tracking with SLAM, not only does SLAM improve model tracking performance, but the model-based tracker alleviates the computational expense of SLAMs loop closing procedure to improve runtime performance. Experiments also revealed that ViSP was unable to handle occlusions when a complete 3D building model was used, resulting in large errors in its pose estimates. The second contribution of this dissertation is a novel map-based incremental localization algorithm that improves tracking performance, and increases pose estimation accuracies from ViSP. The novelty of this algorithm is the implementation of an efficient matching process that identifies corresponding linear features from the UAVs RGB image data and a large, complex, and untextured 3D model. The proposed model-based tracker improved positional accuracies from 10 m (obtained with ViSP) to 46 cm in outdoor environments, and improved from an unattainable result using VISP to 2 cm positional accuracies in large indoor environments. The main disadvantage of any incremental algorithm is that it requires the camera pose of the first frame. Initialization is often a manual process. The third contribution of this dissertation is a map-based absolute localization algorithm that automatically estimates the camera pose when no prior pose information is available. The method benefits from vertical line matching to accomplish a registration procedure of the reference model views with a set of initial input images via geometric hashing. Results demonstrate that sub-metre positional accuracies were achieved and a proposed enhancement of conventional geometric hashing produced more correct matches - 75% of the correct matches were identified, compared to 11%. Further the number of incorrect matches was reduced by 80%

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