Hybrid Focal Stereo Networks for Pattern Analysis in Homogeneous Scenes

In this paper we address the problem of multiple camera calibration in the presence of a homogeneous scene, and without the possibility of employing calibration object based methods. The proposed solution exploits salient features present in a larger field of view, but instead of employing active vision we replace the cameras with stereo rigs featuring a long focal analysis camera, as well as a short focal registration camera. Thus, we are able to propose an accurate solution which does not require intrinsic variation models as in the case of zooming cameras. Moreover, the availability of the two views simultaneously in each rig allows for pose re-estimation between rigs as often as necessary. The algorithm has been successfully validated in an indoor setting, as well as on a difficult scene featuring a highly dense pilgrim crowd in Makkah.Comment: 13 pages, 6 figures, submitted to Machine Vision and Application

Estimating Epipolar Geometry With The Use of a Camera Mounted Orientation Sensor

Context: Image processing and computer vision are rapidly becoming more and more commonplace, and the amount of information about a scene, such as 3D geometry, that can be obtained from an image, or multiple images of the scene is steadily increasing due to increasing resolutions and availability of imaging sensors, and an active research community. In parallel, advances in hardware design and manufacturing are allowing for devices such as gyroscopes, accelerometers and magnetometers and GPS receivers to be included alongside imaging devices at a consumer level. Aims: This work aims to investigate the use of orientation sensors in the field of computer vision as sources of data to aid with image processing and the determination of a scene’s geometry, in particular, the epipolar geometry of a pair of images - and devises a hybrid methodology from two sets of previous works in order to exploit the information available from orientation sensors alongside data gathered from image processing techniques. Method: A readily available consumer-level orientation sensor was used alongside a digital camera to capture images of a set of scenes and record the orientation of the camera. The fundamental matrix of these pairs of images was calculated using a variety of techniques - both incorporating data from the orientation sensor and excluding its use Results: Some methodologies could not produce an acceptable result for the Fundamental Matrix on certain image pairs, however, a method described in the literature that used an orientation sensor always produced a result - however in cases where the hybrid or purely computer vision methods also produced a result - this was found to be the least accurate. Conclusion: Results from this work show that the use of an orientation sensor to capture information alongside an imaging device can be used to improve both the accuracy and reliability of calculations of the scene’s geometry - however noise from the orientation sensor can limit this accuracy and further research would be needed to determine the magnitude of this problem and methods of mitigation

Variable Resolution & Dimensional Mapping For 3d Model Optimization

Three-dimensional computer models, especially geospatial architectural data sets, can be visualized in the same way humans experience the world, providing a realistic, interactive experience. Scene familiarization, architectural analysis, scientific visualization, and many other applications would benefit from finely detailed, high resolution, 3D models. Automated methods to construct these 3D models traditionally has produced data sets that are often low fidelity or inaccurate; otherwise, they are initially highly detailed, but are very labor and time intensive to construct. Such data sets are often not practical for common real-time usage and are not easily updated. This thesis proposes Variable Resolution & Dimensional Mapping (VRDM), a methodology that has been developed to address some of the limitations of existing approaches to model construction from images. Key components of VRDM are texture palettes, which enable variable and ultra-high resolution images to be easily composited; texture features, which allow image features to integrated as image or geometry, and have the ability to modify the geometric model structure to add detail. These components support a primary VRDM objective of facilitating model refinement with additional data. This can be done until the desired fidelity is achieved as practical limits of infinite detail are approached. Texture Levels, the third component, enable real-time interaction with a very detailed model, along with the flexibility of having alternate pixel data for a given area of the model and this is achieved through extra dimensions. Together these techniques have been used to construct models that can contain GBs of imagery data

Contribution towards a fast stereo dense matching.

Stereo matching is important in the area of computer vision as it is the basis of the reconstruction process. Many applications require 3D reconstruction such as view synthesis, robotics... The main task of matching uncalibrated images is to determine the corresponding pixels and other features where the motion between these images and the camera parameters is unknown. Although some methods have been carried out over the past two decades on the matching problem, most of these methods are not practical and difficult to implement. Our approach considers a reliable image edge features in order to develop a fast and practical method. Therefore, we propose a fast stereo matching algorithm combining two different approaches for matching as the image is segmented into two sets of regions: edge regions and non-edge regions. We have used an algebraic method that preserves disparity continuity at the object continuous surfaces. Our results demonstrate that we gain a speed dense matching while the implementation is kept simple and straightforward.Dept. of Computer Science. Paper copy at Leddy Library: Theses & Major Papers - Basement, West Bldg. / Call Number: Thesis2005 .Z42. Source: Masters Abstracts International, Volume: 44-03, page: 1420. Thesis (M.Sc.)--University of Windsor (Canada), 2005

Magnetic-Visual Sensor Fusion-based Dense 3D Reconstruction and Localization for Endoscopic Capsule Robots

Reliable and real-time 3D reconstruction and localization functionality is a crucial prerequisite for the navigation of actively controlled capsule endoscopic robots as an emerging, minimally invasive diagnostic and therapeutic technology for use in the gastrointestinal (GI) tract. In this study, we propose a fully dense, non-rigidly deformable, strictly real-time, intraoperative map fusion approach for actively controlled endoscopic capsule robot applications which combines magnetic and vision-based localization, with non-rigid deformations based frame-to-model map fusion. The performance of the proposed method is demonstrated using four different ex-vivo porcine stomach models. Across different trajectories of varying speed and complexity, and four different endoscopic cameras, the root mean square surface reconstruction errors 1.58 to 2.17 cm.Comment: submitted to IROS 201

Disparity map generation based on trapezoidal camera architecture for multiview video

Visual content acquisition is a strategic functional block of any visual system. Despite its wide possibilities, the arrangement of cameras for the acquisition of good quality visual content for use in multi-view video remains a huge challenge. This paper presents the mathematical description of trapezoidal camera architecture and relationships which facilitate the determination of camera position for visual content acquisition in multi-view video, and depth map generation. The strong point of Trapezoidal Camera Architecture is that it allows for adaptive camera topology by which points within the scene, especially the occluded ones can be optically and geometrically viewed from several different viewpoints either on the edge of the trapezoid or inside it. The concept of maximum independent set, trapezoid characteristics, and the fact that the positions of cameras (with the exception of few) differ in their vertical coordinate description could very well be used to address the issue of occlusion which continues to be a major problem in computer vision with regards to the generation of depth map