Geometric uncertainty models for correspondence problems in digital image processing

Many recent advances in technology rely heavily on the correct interpretation of an enormous amount of visual information. All available sources of visual data (e.g. cameras in surveillance networks, smartphones, game consoles) must be adequately processed to retrieve the most interesting user information. Therefore, computer vision and image processing techniques gain significant interest at the moment, and will do so in the near future. Most commonly applied image processing algorithms require a reliable solution for correspondence problems. The solution involves, first, the localization of corresponding points -visualizing the same 3D point in the observed scene- in the different images of distinct sources, and second, the computation of consistent geometric transformations relating correspondences on scene objects. This PhD presents a theoretical framework for solving correspondence problems with geometric features (such as points and straight lines) representing rigid objects in image sequences of complex scenes with static and dynamic cameras. The research focuses on localization uncertainty due to errors in feature detection and measurement, and its effect on each step in the solution of a correspondence problem. Whereas most other recent methods apply statistical-based models for spatial localization uncertainty, this work considers a novel geometric approach. Localization uncertainty is modeled as a convex polygonal region in the image space. This model can be efficiently propagated throughout the correspondence finding procedure. It allows for an easy extension toward transformation uncertainty models, and to infer confidence measures to verify the reliability of the outcome in the correspondence framework. Our procedure aims at finding reliable consistent transformations in sets of few and ill-localized features, possibly containing a large fraction of false candidate correspondences. The evaluation of the proposed procedure in practical correspondence problems shows that correct consistent correspondence sets are returned in over 95% of the experiments for small sets of 10-40 features contaminated with up to 400% of false positives and 40% of false negatives. The presented techniques prove to be beneficial in typical image processing applications, such as image registration and rigid object tracking

Large Scale 3D Mapping of Indoor Environments Using a Handheld RGBD Camera

The goal of this research is to investigate the problem of reconstructing a 3D representation of an environment, of arbitrary size, using a handheld color and depth (RGBD) sensor. The focus of this dissertation is to examine four of the underlying subproblems to this system: camera tracking, loop closure, data storage, and integration. First, a system for 3D reconstruction of large indoor planar environments with data captured from an RGBD sensor mounted on a mobile robotic platform is presented. An algorithm for constructing nearly drift-free 3D occupancy grids of large indoor environments in an online manner is also presented. This approach combines data from an odometry sensor with output from a visual registration algorithm, and it enforces a Manhattan world constraint by utilizing factor graphs to produce an accurate online estimate of the trajectory of the mobile robotic platform. Through several experiments in environments with varying sizes and construction it is shown that this method reduces rotational and translational drift significantly without performing any loop closing techniques. In addition the advantages and limitations of an octree data structure representation of a 3D environment is examined. Second, the problem of sensor tracking, specifically the use of the KinectFusion algorithm to align two subsequent point clouds generated by an RGBD sensor, is studied. A method to overcome a significant limitation of the Iterative Closest Point (ICP) algorithm used in KinectFusion is proposed, namely, its sole reliance upon geometric information. The proposed method uses both geometric and color information in a direct manner that uses all the data in order to accurately estimate camera pose. Data association is performed by computing a warp between the two color images associated with two RGBD point clouds using the Lucas-Kanade algorithm. A subsequent step then estimates the transformation between the point clouds using either a point-to-point or point-to-plane error metric. Scenarios in which each of these metrics fails are described, and a normal covariance test for automatically selecting between them is proposed. Together, Lucas-Kanade data association (LKDA) along with covariance testing enables robust camera tracking through areas of low geometrical features, while at the same time retaining accuracy in environments in which the existing ICP technique succeeds. Experimental results on several publicly available datasets demonstrate the improved performance both qualitatively and quantitatively. Third, the choice of state space in the context of performing loop closure is revisited. Although a relative state space has been discounted by previous authors, it is shown that such a state space is actually extremely powerful, able to achieve recognizable results after just one iteration. The power behind the technique is that changing the orientation of one node is able to affect other nodes. At the same time, the approach --- which is referred to as Pose Optimization using a Relative State Space (POReSS) --- is fast because, like the more popular incremental state space, the Jacobian never needs to be explicitly computed. Furthermore, it is shown that while POReSS is able to quickly compute a solution near the global optimum, it is not precise enough to perform the fine adjustments necessary to achieve acceptable results. As a result, a method to augment POReSS with a fast variant of Gauss-Seidel --- which is referred to as Graph-Seidel --- on a global state space to allow the solution to settle closer to the global minimum is proposed. Through a set of experiments, it is shown that this combination of POReSS and Graph-Seidel is not only faster but achieves a lower residual than other non-linear algebra techniques. Moreover, unlike the linear algebra-based techniques, it is shown that this approach scales to very large graphs. In addition to revisiting the idea of using a relative state space, the benefits of only optimizing the rotational components of a trajectory in order to perform loop closing is examined (rPOReSS). Finally, an incremental implementation of the rotational optimization is proposed (irPOReSS)

Single View Modeling and View Synthesis

This thesis develops new algorithms to produce 3D content from a single camera. Today, amateurs can use hand-held camcorders to capture and display the 3D world in 2D, using mature technologies. However, there is always a strong desire to record and re-explore the 3D world in 3D. To achieve this goal, current approaches usually make use of a camera array, which suffers from tedious setup and calibration processes, as well as lack of portability, limiting its application to lab experiments. In this thesis, I try to produce the 3D contents using a single camera, making it as simple as shooting pictures. It requires a new front end capturing device rather than a regular camcorder, as well as more sophisticated algorithms. First, in order to capture the highly detailed object surfaces, I designed and developed a depth camera based on a novel technique called light fall-off stereo (LFS). The LFS depth camera outputs color+depth image sequences and achieves 30 fps, which is necessary for capturing dynamic scenes. Based on the output color+depth images, I developed a new approach that builds 3D models of dynamic and deformable objects. While the camera can only capture part of a whole object at any instance, partial surfaces are assembled together to form a complete 3D model by a novel warping algorithm. Inspired by the success of single view 3D modeling, I extended my exploration into 2D-3D video conversion that does not utilize a depth camera. I developed a semi-automatic system that converts monocular videos into stereoscopic videos, via view synthesis. It combines motion analysis with user interaction, aiming to transfer as much depth inferring work from the user to the computer. I developed two new methods that analyze the optical flow in order to provide additional qualitative depth constraints. The automatically extracted depth information is presented in the user interface to assist with user labeling work. In this thesis, I developed new algorithms to produce 3D contents from a single camera. Depending on the input data, my algorithm can build high fidelity 3D models for dynamic and deformable objects if depth maps are provided. Otherwise, it can turn the video clips into stereoscopic video

Sparse Bayesian information filters for localization and mapping

Submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy at the Massachusetts Institute of Technology and the Woods Hole Oceanographic Institution February 2008This thesis formulates an estimation framework for Simultaneous Localization and Mapping (SLAM) that addresses the problem of scalability in large environments. We describe an estimation-theoretic algorithm that achieves significant gains in computational efficiency while maintaining consistent estimates for the vehicle pose and the map of the environment. We specifically address the feature-based SLAM problem in which the robot represents the environment as a collection of landmarks. The thesis takes a Bayesian approach whereby we maintain a joint posterior over the vehicle pose and feature states, conditioned upon measurement data. We model the distribution as Gaussian and parametrize the posterior in the canonical form, in terms of the information (inverse covariance) matrix. When sparse, this representation is amenable to computationally efficient Bayesian SLAM filtering. However, while a large majority of the elements within the normalized information matrix are very small in magnitude, it is fully populated nonetheless. Recent feature-based SLAM filters achieve the scalability benefits of a sparse parametrization by explicitly pruning these weak links in an effort to enforce sparsity. We analyze one such algorithm, the Sparse Extended Information Filter (SEIF), which has laid much of the groundwork concerning the computational benefits of the sparse canonical form. The thesis performs a detailed analysis of the process by which the SEIF approximates the sparsity of the information matrix and reveals key insights into the consequences of different sparsification strategies. We demonstrate that the SEIF yields a sparse approximation to the posterior that is inconsistent, suffering from exaggerated confidence estimates. This overconfidence has detrimental effects on important aspects of the SLAM process and affects the higher level goal of producing accurate maps for subsequent localization and path planning. This thesis proposes an alternative scalable filter that maintains sparsity while preserving the consistency of the distribution. We leverage insights into the natural structure of the feature-based canonical parametrization and derive a method that actively maintains an exactly sparse posterior. Our algorithm exploits the structure of the parametrization to achieve gains in efficiency, with a computational cost that scales linearly with the size of the map. Unlike similar techniques that sacrifice consistency for improved scalability, our algorithm performs inference over a posterior that is conservative relative to the nominal Gaussian distribution. Consequently, we preserve the consistency of the pose and map estimates and avoid the effects of an overconfident posterior. We demonstrate our filter alongside the SEIF and the standard EKF both in simulation as well as on two real-world datasets. While we maintain the computational advantages of an exactly sparse representation, the results show convincingly that our method yields conservative estimates for the robot pose and map that are nearly identical to those of the original Gaussian distribution as produced by the EKF, but at much less computational expense. The thesis concludes with an extension of our SLAM filter to a complex underwater environment. We describe a systems-level framework for localization and mapping relative to a ship hull with an Autonomous Underwater Vehicle (AUV) equipped with a forward-looking sonar. The approach utilizes our filter to fuse measurements of vehicle attitude and motion from onboard sensors with data from sonar images of the hull. We employ the system to perform three-dimensional, 6-DOF SLAM on a ship hull

A Study on Automatic Latent Fingerprint Identification System

Latent fingerprints are the unintentional impressions found at the crime scenes and are considered crucial evidence in criminal identification. Law enforcement and forensic agencies have been using latent fingerprints as testimony in courts. However, since the latent fingerprints are accidentally leftover on different surfaces, the lifted prints look inferior. Therefore, a tremendous amount of research is being carried out in automatic latent fingerprint identification to improve the overall fingerprint recognition performance. As a result, there is an ever-growing demand to develop reliable and robust systems. In this regard, we present a comprehensive literature review of the existing methods utilized in latent fingerprint acquisition, segmentation, quality assessment, enhancement, feature extraction, and matching steps. Later, we provide insight into different benchmark latent datasets available to perform research in this area. Our study highlights various research challenges and gaps by performing detailed analysis on the existing state-of-the-art segmentation, enhancement, extraction, and matching approaches to strengthen the research