Enhancement of Underwater Video Mosaics for Post-Processing

Mosaics of seafloor created from still images or video acquired underwater have proved to be useful for construction of maps of forensic and archeological sites, species\u27 abundance estimates, habitat characterization, etc. Images taken by a camera mounted on a stable platform are registered (at first pair-wise and then globally) and assembled in a high resolution visual map of the surveyed area. While this map is usually sufficient for a human orientation and even quantitative measurements, it often contains artifacts that complicate an automatic post-processing (for example, extraction of shapes for organism counting, or segmentation for habitat characterization). The most prominent artifacts are inter-frame seams caused by inhomogeneous artificial illumination, and local feature misalignments due to parallax effects - result of an attempt to represent a 3D world on a 2D map. In this paper we propose two image processing techniques for mosaic quality enhancement - median mosaic-based illumination correction suppressing appearance of inter-frame seams, and micro warping decreasing influence of parallax effects

As-rigid-as-possible mosaicking and serial section registration of large ssTEM datasets

Motivation: Tiled serial section Transmission Electron Microscopy (ssTEM) is increasingly used to describe high-resolution anatomy of large biological specimens. In particular in neurobiology, TEM is indispensable for analysis of synaptic connectivity in the brain. Registration of ssTEM image mosaics has to recover the 3D continuity and geometrical properties of the specimen in presence of various distortions that are applied to the tissue during sectioning, staining and imaging. These include staining artifacts, mechanical deformation, missing sections and the fact that structures may appear dissimilar in consecutive sections. Results: We developed a fully automatic, non-rigid but as-rigid-as-possible registration method for large tiled serial section microscopy stacks. We use the Scale Invariant Feature Transform (SIFT) to identify corresponding landmarks within and across sections and globally optimize the pose of all tiles in terms of least square displacement of these landmark correspondences. We evaluate the precision of the approach using an artificially generated dataset designed to mimic the properties of TEM data. We demonstrate the performance of our method by registering an ssTEM dataset of the first instar larval brain of Drosophila melanogaster consisting of 6885 images. Availability: This method is implemented as part of the open source software TrakEM2 (http://www.ini.uzh.ch/∼acardona/trakem2.html) and distributed through the Fiji project (http://pacific.mpi-cbg.de). Contact: [email protected]

3D modeling of indoor environments by a mobile platform with a laser scanner and panoramic camera

One major challenge of 3DTV is content acquisition. Here, we present a method to acquire a realistic, visually convincing D model of indoor environments based on a mobile platform that is equipped with a laser range scanner and a panoramic camera. The data of the 2D laser scans are used to solve the simultaneous lo- calization and mapping problem and to extract walls. Textures for walls and floor are built from the images of a calibrated panoramic camera. Multiresolution blending is used to hide seams in the gen- erated textures. The scene is further enriched by 3D-geometry cal- culated from a graph cut stereo technique. We present experimental results from a moderately large real environment.

As-rigid-as-possible mosaicking and serial section registration of large ssTEM datasets

Motivation: Tiled serial section Transmission Electron Microscopy (ssTEM) is increasingly used to describe high-resolution anatomy of large biological specimens. In particular in neurobiology, TEM is indispensable for analysis of synaptic connectivity in the brain. Registration of ssTEM image mosaics has to recover the 3D continuity and geometrical properties of the specimen in presence of various distortions that are applied to the tissue during sectioning, staining and imaging. These include staining artifacts, mechanical deformation, missing sections and the fact that structures may appear dissimilar in consecutive sections

As-rigid-as-possible mosaicking and serial section registration of large ssTEM datasets

Motivation: Tiled serial section Transmission Electron Microscopy (ssTEM) is increasingly used to describe high-resolution anatomy of large biological specimens. In particular in neurobiology, TEM is indispensable for analysis of synaptic connectivity in the brain. Registration of ssTEM image mosaics has to recover the 3D continuity and geometrical properties of the specimen in presence of various distortions that are applied to the tissue during sectioning, staining and imaging. These include staining artifacts, mechanical deformation, missing sections and the fact that structures may appear dissimilar in consecutive sections

Globally Optimal Fetoscopic Mosaicking Based on Pose Graph Optimisation With Affine Constraints

Fetoscopic laser ablation surgery could be guided using a high-quality panorama of the operating site, representing a map of the placental vasculature. This can be achieved during the initial inspection phase of the procedure using image mosaicking techniques. Due to the lack of camera calibration in the operating room, it has been mostly modelled as an affine registration problem. While previous work mostly focuses on image feature extraction for visual odometry, the challenges related to large-scale reconstruction (re-localisation, loop closure, drift correction) remain largely unaddressed in this context. This letter proposes using pose graph optimisation to produce globally optimal image mosaics of placental vessels. Our approach follows the SLAM framework with a front-end for visual odometry and a back-end for long-term refinement. Our front-end uses a recent state-of-the-art odometry approach based on vessel segmentation, which is then managed by a key-frame structure and the bag-of-words (BoW) scheme to retrieve loop closures. The back-end, which is our key contribution, models odometry and loop closure constraints as a pose graph with affine warpings between states. This problem in the special Euclidean space cannot be solved by existing pose graph algorithms and available libraries such as G2O. We model states on affine Lie group with local linearisation in its Lie algebra. The cost function is established using Mahalanobis distance with the vectorisation of the Lie algebra. Finally, an iterative optimisation algorithm is adopted to minimise the cost function. The proposed pose graph optimisation is first validated on simulation data with a synthetic trajectory that has different levels of noise and different numbers of loop closures. Then the whole system is validated using real fetoscopic data that has three sequences with different numbers of frames and loop closures. Experimental results validate the advantage of the proposed method compared with baselines

Globally optimal stitching of tiled 3D microscopic image acquisitions

Motivation: Modern anatomical and developmental studies often require high-resolution imaging of large specimens in three dimensions (3D). Confocal microscopy produces high-resolution 3D images, but is limited by a relatively small field of view compared with the size of large biological specimens. Therefore, motorized stages that move the sample are used to create a tiled scan of the whole specimen. The physical coordinates provided by the microscope stage are not precise enough to allow direct reconstruction (Stitching) of the whole image from individual image stacks