Multiple View Geometry For Video Analysis And Post-production

Multiple view geometry is the foundation of an important class of computer vision techniques for simultaneous recovery of camera motion and scene structure from a set of images. There are numerous important applications in this area. Examples include video post-production, scene reconstruction, registration, surveillance, tracking, and segmentation. In video post-production, which is the topic being addressed in this dissertation, computer analysis of the motion of the camera can replace the currently used manual methods for correctly aligning an artificially inserted object in a scene. However, existing single view methods typically require multiple vanishing points, and therefore would fail when only one vanishing point is available. In addition, current multiple view techniques, making use of either epipolar geometry or trifocal tensor, do not exploit fully the properties of constant or known camera motion. Finally, there does not exist a general solution to the problem of synchronization of N video sequences of distinct general scenes captured by cameras undergoing similar ego-motions, which is the necessary step for video post-production among different input videos. This dissertation proposes several advancements that overcome these limitations. These advancements are used to develop an efficient framework for video analysis and post-production in multiple cameras. In the first part of the dissertation, the novel inter-image constraints are introduced that are particularly useful for scenes where minimal information is available. This result extends the current state-of-the-art in single view geometry techniques to situations where only one vanishing point is available. The property of constant or known camera motion is also described in this dissertation for applications such as calibration of a network of cameras in video surveillance systems, and Euclidean reconstruction from turn-table image sequences in the presence of zoom and focus. We then propose a new framework for the estimation and alignment of camera motions, including both simple (panning, tracking and zooming) and complex (e.g. hand-held) camera motions. Accuracy of these results is demonstrated by applying our approach to video post-production applications such as video cut-and-paste and shadow synthesis. As realistic image-based rendering problems, these applications require extreme accuracy in the estimation of camera geometry, the position and the orientation of the light source, and the photometric properties of the resulting cast shadows. In each case, the theoretical results are fully supported and illustrated by both numerical simulations and thorough experimentation on real data

External multi-modal imaging sensor calibration for sensor fusion: A review

Multi-modal data fusion has gained popularity due to its diverse applications, leading to an increased demand for external sensor calibration. Despite several proven calibration solutions, they fail to fully satisfy all the evaluation criteria, including accuracy, automation, and robustness. Thus, this review aims to contribute to this growing field by examining recent research on multi-modal imaging sensor calibration and proposing future research directions. The literature review comprehensively explains the various characteristics and conditions of different multi-modal external calibration methods, including traditional motion-based calibration and feature-based calibration. Target-based calibration and targetless calibration are two types of feature-based calibration, which are discussed in detail. Furthermore, the paper highlights systematic calibration as an emerging research direction. Finally, this review concludes crucial factors for evaluating calibration methods and provides a comprehensive discussion on their applications, with the aim of providing valuable insights to guide future research directions. Future research should focus primarily on the capability of online targetless calibration and systematic multi-modal sensor calibration.Ministerio de Ciencia, Innovación y Universidades | Ref. PID2019-108816RB-I0

Data reduction methods for single-mode optical interferometry - Application to the VLTI two-telescopes beam combiner VINCI

The interferometric data processing methods that we describe in this paper use a number of innovative techniques. In particular, the implementation of the wavelet transform allows us to obtain a good immunity of the fringe processing to false detections and large amplitude perturbations by the atmospheric piston effect, through a careful, automated selection of the interferograms. To demonstrate the data reduction procedure, we describe the processing and calibration of a sample of stellar data from the VINCI beam combiner. Starting from the raw data, we derive the angular diameter of the dwarf star Alpha Cen A. Although these methods have been developed specifically for VINCI, they are easily applicable to other single-mode beam combiners, and to spectrally dispersed fringes.Comment: Accepted for publication in Astronomy & Astrophysics, 17 pages, 19 figure

A Full Scale Camera Calibration Technique with Automatic Model Selection – Extension and Validation

This thesis presents work on the testing and development of a complete camera calibration approach which can be applied to a wide range of cameras equipped with normal, wide-angle, fish-eye, or telephoto lenses. The full scale calibration approach estimates all of the intrinsic and extrinsic parameters. The calibration procedure is simple and does not require prior knowledge of any parameters. The method uses a simple planar calibration pattern. Closed-form estimates for the intrinsic and extrinsic parameters are computed followed by nonlinear optimization. Polynomial functions are used to describe the lens projection instead of the commonly used radial model. Statistical information criteria are used to automatically determine the complexity of the lens distortion model. In the first stage experiments were performed to verify and compare the performance of the calibration method. Experiments were performed on a wide range of lenses. Synthetic data was used to simulate real data and validate the performance. Synthetic data was also used to validate the performance of the distortion model selection which uses Information Theoretic Criterion (AIC) to automatically select the complexity of the distortion model. In the second stage work was done to develop an improved calibration procedure which addresses shortcomings of previously developed method. Experiments on the previous method revealed that the estimation of the principal point during calibration was erroneous for lenses with a large focal length. To address this issue the calibration method was modified to include additional methods to accurately estimate the principal point in the initial stages of the calibration procedure. The modified procedure can now be used to calibrate a wide spectrum of imaging systems including telephoto and verifocal lenses. Survey of current work revealed a vast amount of research concentrating on calibrating only the distortion of the camera. In these methods researchers propose methods to calibrate only the distortion parameters and suggest using other popular methods to find the remaining camera parameters. Using this proposed methodology we apply distortion calibration to our methods to separate the estimation of distortion parameters. We show and compare the results with the original method on a wide range of imaging systems

Optical techniques for 3D surface reconstruction in computer-assisted laparoscopic surgery

One of the main challenges for computer-assisted surgery (CAS) is to determine the intra-opera- tive morphology and motion of soft-tissues. This information is prerequisite to the registration of multi-modal patient-specific data for enhancing the surgeon’s navigation capabilites by observ- ing beyond exposed tissue surfaces and for providing intelligent control of robotic-assisted in- struments. In minimally invasive surgery (MIS), optical techniques are an increasingly attractive approach for in vivo 3D reconstruction of the soft-tissue surface geometry. This paper reviews the state-of-the-art methods for optical intra-operative 3D reconstruction in laparoscopic surgery and discusses the technical challenges and future perspectives towards clinical translation. With the recent paradigm shift of surgical practice towards MIS and new developments in 3D opti- cal imaging, this is a timely discussion about technologies that could facilitate complex CAS procedures in dynamic and deformable anatomical regions

Deep Depth From Focus

Depth from focus (DFF) is one of the classical ill-posed inverse problems in computer vision. Most approaches recover the depth at each pixel based on the focal setting which exhibits maximal sharpness. Yet, it is not obvious how to reliably estimate the sharpness level, particularly in low-textured areas. In this paper, we propose `Deep Depth From Focus (DDFF)' as the first end-to-end learning approach to this problem. One of the main challenges we face is the hunger for data of deep neural networks. In order to obtain a significant amount of focal stacks with corresponding groundtruth depth, we propose to leverage a light-field camera with a co-calibrated RGB-D sensor. This allows us to digitally create focal stacks of varying sizes. Compared to existing benchmarks our dataset is 25 times larger, enabling the use of machine learning for this inverse problem. We compare our results with state-of-the-art DFF methods and we also analyze the effect of several key deep architectural components. These experiments show that our proposed method `DDFFNet' achieves state-of-the-art performance in all scenes, reducing depth error by more than 75% compared to the classical DFF methods.Comment: accepted to Asian Conference on Computer Vision (ACCV) 201