Consistent joint photometric and geometric image registration

In this paper, we derive a novel robust image alignment technique that performs joint geometric and photometric registration in the total least square sense. The main idea is to use the total least square metrics instead of the ordinary least square metrics, which is commonly used in the literature. While the OLS model indicates that the target image may contain noise and the reference image should be noise-free, this puts a severe limitation on practical registration problems. By introducing the TLS model, which allows perturbations in both images, we can obtain mutually consistent parameters. Experimental results show that our method is indeed much more consistent and accurate in presence of noise compared to existing registration algorithms

Super resolution and dynamic range enhancement of image sequences

Camera producers try to increase the spatial resolution of a camera by reducing size of sites on sensor array. However, shot noise causes the signal to noise ratio drop as sensor sites get smaller. This fact motivates resolution enhancement to be performed through software. Super resolution (SR) image reconstruction aims to combine degraded images of a scene in order to form an image which has higher resolution than all observations. There is a demand for high resolution images in biomedical imaging, surveillance, aerial/satellite imaging and high-definition TV (HDTV) technology. Although extensive research has been conducted in SR, attention has not been given to increase the resolution of images under illumination changes. In this study, a unique framework is proposed to increase the spatial resolution and dynamic range of a video sequence using Bayesian and Projection onto Convex Sets (POCS) methods. Incorporating camera response function estimation into image reconstruction allows dynamic range enhancement along with spatial resolution improvement. Photometrically varying input images complicate process of projecting observations onto common grid by violating brightness constancy. A contrast invariant feature transform is proposed in this thesis to register input images with high illumination variation. Proposed algorithm increases the repeatability rate of detected features among frames of a video. Repeatability rate is increased by computing the autocorrelation matrix using the gradients of contrast stretched input images. Presented contrast invariant feature detection improves repeatability rate of Harris corner detector around %25 on average. Joint multi-frame demosaicking and resolution enhancement is also investigated in this thesis. Color constancy constraint set is devised and incorporated into POCS framework for increasing resolution of color-filter array sampled images. Proposed method provides fewer demosaicking artifacts compared to existing POCS method and a higher visual quality in final image

Dynamical Analysis of Nearby ClustErs. Automated astrometry from the ground: precision proper motions over wide field

The kinematic properties of the different classes of objects in a given association hold important clues about its member's history, and offer a unique opportunity to test the predictions of the various models of stellar formation and evolution. DANCe (standing for Dynamical Analysis of Nearby ClustErs) is a survey program aimed at deriving a comprehensive and homogeneous census of the stellar and substellar content of a number of nearby (<1kpc) young (<500Myr) associations. Whenever possible, members will be identified based on their kinematics properties, ensuring little contamination from background and foreground sources. Otherwise, the dynamics of previously confirmed members will be studied using the proper motion measurements. We present here the method used to derive precise proper motion measurements, using the Pleiades cluster as a test bench. Combining deep wide field multi-epoch panchromatic images obtained at various obervatories over up to 14 years, we derive accurate proper motions for the sources present in the field of the survey. The datasets cover ~80 square degrees, centered around the Seven Sisters. Using new tools, we have computed a catalog of 6116907 unique sources, including proper motion measurements for 3577478 of them. The catalogue covers the magnitude range between i=12~24mag, achieving a proper motion accuracy <1mas/yr for sources as faint as i=22.5mag. We estimate that our final accuracy reaches 0.3mas/yr in the best cases, depending on magnitude, observing history, and the presence of reference extragalactic sources for the anchoring onto the ICRS.Comment: Accepted for publication in A&

Depth-Assisted Semantic Segmentation, Image Enhancement and Parametric Modeling

This dissertation addresses the problem of employing 3D depth information on solving a number of traditional challenging computer vision/graphics problems. Humans have the abilities of perceiving the depth information in 3D world, which enable humans to reconstruct layouts, recognize objects and understand the geometric space and semantic meanings of the visual world. Therefore it is significant to explore how the 3D depth information can be utilized by computer vision systems to mimic such abilities of humans. This dissertation aims at employing 3D depth information to solve vision/graphics problems in the following aspects: scene understanding, image enhancements and 3D reconstruction and modeling. In addressing scene understanding problem, we present a framework for semantic segmentation and object recognition on urban video sequence only using dense depth maps recovered from the video. Five view-independent 3D features that vary with object class are extracted from dense depth maps and used for segmenting and recognizing different object classes in street scene images. We demonstrate a scene parsing algorithm that uses only dense 3D depth information to outperform using sparse 3D or 2D appearance features. In addressing image enhancement problem, we present a framework to overcome the imperfections of personal photographs of tourist sites using the rich information provided by large-scale internet photo collections (IPCs). By augmenting personal 2D images with 3D information reconstructed from IPCs, we address a number of traditionally challenging image enhancement techniques and achieve high-quality results using simple and robust algorithms. In addressing 3D reconstruction and modeling problem, we focus on parametric modeling of flower petals, the most distinctive part of a plant. The complex structure, severe occlusions and wide variations make the reconstruction of their 3D models a challenging task. We overcome these challenges by combining data driven modeling techniques with domain knowledge from botany. Taking a 3D point cloud of an input flower scanned from a single view, each segmented petal is fitted with a scale-invariant morphable petal shape model, which is constructed from individually scanned 3D exemplar petals. Novel constraints based on botany studies are incorporated into the fitting process for realistically reconstructing occluded regions and maintaining correct 3D spatial relations. The main contribution of the dissertation is in the intelligent usage of 3D depth information on solving traditional challenging vision/graphics problems. By developing some advanced algorithms either automatically or with minimum user interaction, the goal of this dissertation is to demonstrate that computed 3D depth behind the multiple images contains rich information of the visual world and therefore can be intelligently utilized to recognize/ understand semantic meanings of scenes, efficiently enhance and augment single 2D images, and reconstruct high-quality 3D models

Extrinsic Calibration and Ego-Motion Estimation for Mobile Multi-Sensor Systems

Autonomous robots and vehicles are often equipped with multiple sensors to perform vital tasks such as localization or mapping. The joint system of various sensors with different sensing modalities can often provide better localization or mapping results than individual sensor alone in terms of accuracy or completeness. However, to enable improved performance, two important challenges have to be addressed when dealing with multi-sensor systems. Firstly, how to accurately determine the spatial relationship between individual sensor on the robot? This is a vital task known as extrinsic calibration. Without this calibration information, measurements from different sensors cannot be fused. Secondly, how to combine data from multiple sensors to correct for the deficiencies of each sensor, and thus, provides better estimations? This is another important task known as data fusion. The core of this thesis is to provide answers to these two questions. We cover, in the first part of the thesis, aspects related to improving the extrinsic calibration accuracy, and present, in the second part, novel data fusion algorithms designed to address the ego-motion estimation problem using data from a laser scanner and a monocular camera. In the extrinsic calibration part, we contribute by revealing and quantifying the relative calibration accuracies of three common types of calibration methods, so as to offer an insight into choosing the best calibration method when multiple options are available. Following that, we propose an optimization approach for solving common motion-based calibration problems. By exploiting the Gauss-Helmert model, our approach is more accurate and robust than classical least squares model. In the data fusion part, we focus on camera-laser data fusion and contribute with two new ego-motion estimation algorithms that combine complementary information from a laser scanner and a monocular camera. The first algorithm utilizes camera image information to guide the laser scan-matching. It can provide accurate motion estimates and yet can work in general conditions without requiring a field-of-view overlap between the camera and laser scanner, nor an initial guess of the motion parameters. The second algorithm combines the camera and the laser scanner information in a direct way, assuming the field-of-view overlap between the sensors is substantial. By maximizing the information usage of both the sparse laser point cloud and the dense image, the second algorithm is able to achieve state-of-the-art estimation accuracy. Experimental results confirm that both algorithms offer excellent alternatives to state-of-the-art camera-laser ego-motion estimation algorithms

Geometric and photometric affine invariant image registration

This thesis aims to present a solution to the correspondence problem for the registration of wide-baseline images taken from uncalibrated cameras. We propose an affine invariant descriptor that combines the geometry and photometry of the scene to find correspondences between both views. The geometric affine invariant component of the descriptor is based on the affine arc-length metric, whereas the photometry is analysed by invariant colour moments. A graph structure represents the spatial distribution of the primitive features; i.e. nodes correspond to detected high-curvature points, whereas arcs represent connectivities by extracted contours. After matching, we refine the search for correspondences by using a maximum likelihood robust algorithm. We have evaluated the system over synthetic and real data. The method is endemic to propagation of errors introduced by approximations in the system.BAE SystemsSelex Sensors and Airborne System

ElasticFusion: real-time dense SLAM and light source estimation

We present a novel approach to real-time dense visual SLAM. Our system is capable of capturing comprehensive dense globally consistent surfel-based maps of room scale environments and beyond explored using an RGB-D camera in an incremental online fashion, without pose graph optimisation or any post-processing steps. This is accomplished by using dense frame-tomodel camera tracking and windowed surfel-based fusion coupled with frequent model refinement through non-rigid surface deformations. Our approach applies local model-to-model surface loop closure optimisations as often as possible to stay close to the mode of the map distribution, while utilising global loop closure to recover from arbitrary drift and maintain global consistency. In the spirit of improving map quality as well as tracking accuracy and robustness, we furthermore explore a novel approach to real-time discrete light source detection. This technique is capable of detecting numerous light sources in indoor environments in real-time as a user handheld camera explores the scene. Absolutely no prior information about the scene or number of light sources is required. By making a small set of simple assumptions about the appearance properties of the scene our method can incrementally estimate both the quantity and location of multiple light sources in the environment in an online fashion. Our results demonstrate that our technique functions well in many different environments and lighting configurations. We show that this enables (a) more realistic augmented reality (AR) rendering; (b) a richer understanding of the scene beyond pure geometry and; (c) more accurate and robust photometric trackin

Exploiting Structural Regularities and Beyond: Vision-based Localization and Mapping in Man-Made Environments

Image-based estimation of camera motion, known as visual odometry (VO), plays a very important role in many robotic applications such as control and navigation of unmanned mobile robots, especially when no external navigation reference signal is available. The core problem of VO is the estimation of the camera’s ego-motion (i.e. tracking) either between successive frames, namely relative pose estimation, or with respect to a global map, namely absolute pose estimation. This thesis aims to develop efficient, accurate and robust VO solutions by taking advantage of structural regularities in man-made environments, such as piece-wise planar structures, Manhattan World and more generally, contours and edges. Furthermore, to handle challenging scenarios that are beyond the limits of classical sensor based VO solutions, we investigate a recently emerging sensor — the event camera and study on event-based mapping — one of the key problems in the event-based VO/SLAM. The main achievements are summarized as follows. First, we revisit an old topic on relative pose estimation: accurately and robustly estimating the fundamental matrix given a collection of independently estimated homograhies. Three classical methods are reviewed and then we show a simple but nontrivial two-step normalization within the direct linear method that achieves similar performance to the less attractive and more computationally intensive hallucinated points based method. Second, an efficient 3D rotation estimation algorithm for depth cameras in piece-wise planar environments is presented. It shows that by using surface normal vectors as an input, planar modes in the corresponding density distribution function can be discovered and continuously tracked using efficient non-parametric estimation techniques. The relative rotation can be estimated by registering entire bundles of planar modes by using robust L1-norm minimization. Third, an efficient alternative to the iterative closest point algorithm for real-time tracking of modern depth cameras in ManhattanWorlds is developed. We exploit the common orthogonal structure of man-made environments in order to decouple the estimation of the rotation and the three degrees of freedom of the translation. The derived camera orientation is absolute and thus free of long-term drift, which in turn benefits the accuracy of the translation estimation as well. Fourth, we look into a more general structural regularity—edges. A real-time VO system that uses Canny edges is proposed for RGB-D cameras. Two novel alternatives to classical distance transforms are developed with great properties that significantly improve the classical Euclidean distance field based methods in terms of efficiency, accuracy and robustness. Finally, to deal with challenging scenarios that go beyond what standard RGB/RGB-D cameras can handle, we investigate the recently emerging event camera and focus on the problem of 3D reconstruction from data captured by a stereo event-camera rig moving in a static scene, such as in the context of stereo Simultaneous Localization and Mapping