Direct global adjustment methods for endoscopic mosaicking

ABSTRACT Endoscopy is an invaluable tool for several surgical and diagnostic applications. It permits minimally invasive visualization of internal structures thus involving little or no injury to internal structures. This method of visualization however restricts the size of the imaging device and therefore compromises on the field of view captured in a single image. The problem of a narrow field of view can be solved by capturing video sequences and stitching them to generate a mosaic of the scene under consideration. Registration of images in the sequence is therefore a crucial step. Existing methods compute frame-to-frame registration estimates and use these to resample images in order to generate a mosaic. The complexity of the appearance of internal structures and accumulation of registration error in frame to frame estimates however can be large enough to cause a cumulative drift that can misrepresent the scene. These errors can be reduced by application of global adjustment schemes. In this paper, we present a set of techniques for overcoming this problem of drift for pixel based registration in order to achieve global consistency of mosaics. The algorithm uses the frame-to-frame estimate as an initialization and subsequently corrects these estimates by setting up a large scale optimization problem which simultaneously solves for all corrections of estimates. In addition we set up a graph and introduce loop closure constraints in order to ensure consistency of registration. We present our method and results in semi global and fully global graph based adjustment methods as well as validation of our results

Improving the Geotagging Accuracy of Street-level Images

Integrating images taken at street-level with satellite imagery is becoming increasingly valuable in the decision-making processes not only for individuals, but also in business and governmental sectors. To perform this integration, images taken at street-level need to be accurately georeferenced. This georeference information can be derived from a global positioning system (GPS). However, GPS data is prone to errors up to 15 meters, and needs to be corrected for the purpose of geo-referencing. In this thesis, an automatic method is proposed for correcting the georeference information obtained from the GPS data, based on image registration techniques. The proposed method uses an optimization technique to find local optimal solutions by matching high-level features and their relative locations. A global optimization method is then employed over all of the local solutions by applying a geometric constraint. The main contribution of this thesis is introducing a new direction for correcting the GPS data which is more economical and more consistent compared to existing manual method. Other than high cost (labor and management), the main concern with manual correction is the low degree of consistency between different human operators. Our proposed automatic software-based method is a solution for these drawbacks. Other contributions can be listed as (1) modified Chamfer matching (CM) cost function which improves the accuracy of standard CM for images with various misleading/disturbing edges; (2) Monte-Carlo-inspired statistical analysis which made it possible to quantify the overall performance of the proposed algorithm; (3) Novel similarity measure for applying normalized cross correlation (NCC) technique on multi-level thresholded images, which is used to compare multi-modal images more accurately as compared to standard application of NCC on raw images. (4) Casting the problem of selecting an optimal global solution among set of local minima into a problem of finding an optimal path in a graph using Dijkstra\u27s algorithm. We used our algorithm for correcting the georeference information of 20 chains containing more than 7000 fisheye images and our experimental results show that the proposed algorithm can achieve an average error of 2 meters, which is acceptable for most of applications

Fine-To-Coarse Global Registration of RGB-D Scans

RGB-D scanning of indoor environments is important for many applications, including real estate, interior design, and virtual reality. However, it is still challenging to register RGB-D images from a hand-held camera over a long video sequence into a globally consistent 3D model. Current methods often can lose tracking or drift and thus fail to reconstruct salient structures in large environments (e.g., parallel walls in different rooms). To address this problem, we propose a "fine-to-coarse" global registration algorithm that leverages robust registrations at finer scales to seed detection and enforcement of new correspondence and structural constraints at coarser scales. To test global registration algorithms, we provide a benchmark with 10,401 manually-clicked point correspondences in 25 scenes from the SUN3D dataset. During experiments with this benchmark, we find that our fine-to-coarse algorithm registers long RGB-D sequences better than previous methods

Robust Rotation Synchronization via Low-rank and Sparse Matrix Decomposition

This paper deals with the rotation synchronization problem, which arises in global registration of 3D point-sets and in structure from motion. The problem is formulated in an unprecedented way as a "low-rank and sparse" matrix decomposition that handles both outliers and missing data. A minimization strategy, dubbed R-GoDec, is also proposed and evaluated experimentally against state-of-the-art algorithms on simulated and real data. The results show that R-GoDec is the fastest among the robust algorithms.Comment: The material contained in this paper is part of a manuscript submitted to CVI