Projection of Stabilized Aerial Imagery Onto Digital Elevation Maps for Geo-Rectified and Jitter-Free Viewing

As imagery is collected from an airborne platform, an individual viewing the images wants to know from where on the Earth the images were collected. To do this, some information about the camera needs to be known, such as its position and orientation relative to the Earth. This can be provided by common inertial navigation systems (INS). Once the location of the camera is known, it is useful to project an image onto some representation of the Earth. Due to the non-smooth terrain of the Earth (mountains, valleys, etc.), this projection is highly non-linear. Thus, to ensure accurate projection, one needs to project onto a digital elevation map (DEM). This allows one to view the images overlaid onto a representation of the Earth. A code has been developed that takes an image, a model of the camera used to acquire that image, the pose of the camera during acquisition (as provided by an INS), and a DEM, and outputs an image that has been geo-rectified. The world coordinate of the bounds of the image are provided for viewing purposes. The code finds a mapping from points on the ground (DEM) to pixels in the image. By performing this process for all points on the ground, one can "paint" the ground with the image, effectively performing a projection of the image onto the ground. In order to make this process efficient, a method was developed for finding a region of interest (ROI) on the ground to where the image will project. This code is useful in any scenario involving an aerial imaging platform that moves and rotates over time. Many other applications are possible in processing aerial and satellite imagery

Constructing a Database from Multiple 2D Images for Camera Pose Estimation and Robot Localization

The LMDB (Landmark Database) Builder software identifies persistent image features (landmarks) in a scene viewed multiple times and precisely estimates the landmarks 3D world positions. The software receives as input multiple 2D images of approximately the same scene, along with an initial guess of the camera poses for each image, and a table of features matched pair-wise in each frame. LMDB Builder aggregates landmarks across an arbitrarily large collection of frames with matched features. Range data from stereo vision processing can also be passed to improve the initial guess of the 3D point estimates. The LMDB Builder aggregates feature lists across all frames, manages the process to promote selected features to landmarks, and iteratively calculates the 3D landmark positions using the current camera pose estimations (via an optimal ray projection method), and then improves the camera pose estimates using the 3D landmark positions. Finally, it extracts image patches for each landmark from auto-selected key frames and constructs the landmark database. The landmark database can then be used to estimate future camera poses (and therefore localize a robotic vehicle that may be carrying the cameras) by matching current imagery to landmark database image patches and using the known 3D landmark positions to estimate the current pose

Real-Time Feature Tracking Using Homography

This software finds feature point correspondences in sequences of images. It is designed for feature matching in aerial imagery. Feature matching is a fundamental step in a number of important image processing operations: calibrating the cameras in a camera array, stabilizing images in aerial movies, geo-registration of images, and generating high-fidelity surface maps from aerial movies. The method uses a Shi-Tomasi corner detector and normalized cross-correlation. This process is likely to result in the production of some mismatches. The feature set is cleaned up using the assumption that there is a large planar patch visible in both images. At high altitude, this assumption is often reasonable. A mathematical transformation, called an homography, is developed that allows us to predict the position in image 2 of any point on the plane in image 1. Any feature pair that is inconsistent with the homography is thrown out. The output of the process is a set of feature pairs, and the homography. The algorithms in this innovation are well known, but the new implementation improves the process in several ways. It runs in real-time at 2 Hz on 64-megapixel imagery. The new Shi-Tomasi corner detector tries to produce the requested number of features by automatically adjusting the minimum distance between found features. The homography-finding code now uses an implementation of the RANSAC algorithm that adjusts the number of iterations automatically to achieve a pre-set probability of missing a set of inliers. The new interface allows the caller to pass in a set of predetermined points in one of the images. This allows the ability to track the same set of points through multiple frames

Automatic Calibration of an Airborne Imaging System to an Inertial Navigation Unit

This software automatically calibrates a camera or an imaging array to an inertial navigation system (INS) that is rigidly mounted to the array or imager. In effect, it recovers the coordinate frame transformation between the reference frame of the imager and the reference frame of the INS. This innovation can automatically derive the camera-to-INS alignment using image data only. The assumption is that the camera fixates on an area while the aircraft flies on orbit. The system then, fully automatically, solves for the camera orientation in the INS frame. No manual intervention or ground tie point data is required

Building a 2.5D Digital Elevation Model from 2D Imagery

When projecting imagery into a georeferenced coordinate frame, one needs to have some model of the geographical region that is being projected to. This model can sometimes be a simple geometrical curve, such as an ellipse or even a plane. However, to obtain accurate projections, one needs to have a more sophisticated model that encodes the undulations in the terrain including things like mountains, valleys, and even manmade structures. The product that is often used for this purpose is a Digital Elevation Model (DEM). The technology presented here generates a high-quality DEM from a collection of 2D images taken from multiple viewpoints, plus pose data for each of the images and a camera model for the sensor. The technology assumes that the images are all of the same region of the environment. The pose data for each image is used as an initial estimate of the geometric relationship between the images, but the pose data is often noisy and not of sufficient quality to build a high-quality DEM. Therefore, the source imagery is passed through a feature-tracking algorithm and multi-plane-homography algorithm, which refine the geometric transforms between images. The images and their refined poses are then passed to a stereo algorithm, which generates dense 3D data for each image in the sequence. The 3D data from each image is then placed into a consistent coordinate frame and passed to a routine that divides the coordinate frame into a number of cells. The 3D points that fall into each cell are collected, and basic statistics are applied to determine the elevation of that cell. The result of this step is a DEM that is in an arbitrary coordinate frame. This DEM is then filtered and smoothed in order to remove small artifacts. The final step in the algorithm is to take the initial DEM and rotate and translate it to be in the world coordinate frame [such as UTM (Universal Transverse Mercator), MGRS (Military Grid Reference System), or geodetic] such that it can be saved in a standard DEM format and used for projection

Lidar for Guidance of a Spacecraft or Exploratory Robot

A report describes the Laser Mapper (LAMP) -- a lightweight, compact, low-power lidar system under development for guidance of a spacecraft or exploratory robotic vehicle (rover) at Mars or another planet. The LAMP is intended especially for use during rendezvous of two spacecraft in orbit, for mapping terrain during descent and landing of a spacecraft, for capturing a sample that has been launched into orbit, or navigation and avoidance of obstacles by a rover traversing terrain. The LAMP includes a laser that emits high-power, short light pulses. The laser beam is aimed in azimuth and elevation by use of a mirror on a two-axis gimbal, which scans the beam across a field of regard. Light reflected by a target is collected by a telescope, and the distance to the target is determined by measuring the round-trip travel time for reflected light pulses. The distance information is combined with directional information to construct a three-dimensional map of targets in the field of regard

Effect of Deutetrabenazine on Chorea Among Patients With Huntington Disease A Randomized Clinical Trial

Importance Deutetrabenazine is a novel molecule containing deuterium, which attenuates CYP2D6 metabolism and increases active metabolite half-lives and may therefore lead to stable systemic exposure while preserving key pharmacological activity. Objective To evaluate efficacy and safety of deutetrabenazine treatment to control chorea associated with Huntington disease. Design, Setting, and Participants Ninety ambulatory adults diagnosed with manifest Huntington disease and a baseline total maximal chorea score of 8 or higher (range, 0-28; lower score indicates less chorea) were enrolled from August 2013 to August 2014 and randomized to receive deutetrabenazine (n = 45) or placebo (n = 45) in a double-blind fashion at 34 Huntington Study Group sites. Interventions Deutetrabenazine or placebo was titrated to optimal dose level over 8 weeks and maintained for 4 weeks, followed by a 1-week washout. Main Outcomes and Measures Primary end point was the total maximal chorea score change from baseline (the average of values from the screening and day-0 visits) to maintenance therapy (the average of values from the week 9 and 12 visits) obtained by in-person visits. This study was designed to detect a 2.7-unit treatment difference in scores. The secondary end points, assessed hierarchically, were the proportion of patients who achieved treatment success on the Patient Global Impression of Change (PGIC) and on the Clinical Global Impression of Change (CGIC), the change in 36-Item Short Form– physical functioning subscale score (SF-36), and the change in the Berg Balance Test. Results Ninety patients with Huntington disease (mean age, 53.7 years; 40 women [44.4%]) were enrolled. In the deutetrabenazine group, the mean total maximal chorea scores improved from 12.1 (95% CI, 11.2-12.9) to 7.7 (95% CI, 6.5-8.9), whereas in the placebo group, scores improved from 13.2 (95% CI, 12.2-14.3) to 11.3 (95% CI, 10.0-12.5); the mean between-group difference was –2.5 units (95% CI, –3.7 to –1.3) (P < .001). Treatment success, as measured by the PGIC, occurred in 23 patients (51%) in the deutetrabenazine group vs 9 (20%) in the placebo group (P = .002). As measured by the CGIC, treatment success occurred in 19 patients (42%) in the deutetrabenazine group vs 6 (13%) in the placebo group (P = .002). In the deutetrabenazine group, the mean SF-36 physical functioning subscale scores decreased from 47.5 (95% CI, 44.3-50.8) to 47.4 (44.3-50.5), whereas in the placebo group, scores decreased from 43.2 (95% CI, 40.2-46.3) to 39.9 (95% CI, 36.2-43.6), for a treatment benefit of 4.3 (95% CI, 0.4 to 8.3) (P = .03). There was no difference between groups (mean difference of 1.0 unit; 95% CI, –0.3 to 2.3; P = .14), for improvement in the Berg Balance Test, which improved by 2.2 units (95% CI, 1.3-3.1) in the deutetrabenazine group and by 1.3 units (95% CI, 0.4-2.2) in the placebo group. Adverse event rates were similar for deutetrabenazine and placebo, including depression, anxiety, and akathisia. Conclusions and Relevance Among patients with chorea associated with Huntington disease, the use of deutetrabenazine compared with placebo resulted in improved motor signs at 12 weeks. Further research is needed to assess the clinical importance of the effect size and to determine longer-term efficacy and safety

Discrete Multi-Dimensional Scaling

In recent years, a number of models of lexical access based on attractor networks have appeared. These models reproduce a number of effects seen in psycholinguistic experiments, but all suffer from unrealistic representations of lexical semantics. In an effort to improve this situation we are looking at techniques developed in the information retrieval literature that use the statistics found in large corpora to automatically produce vector representations for large numbers of words. This paper concentrates on the problem of transforming the real-valued cooccurrence vectors produced by these statistical techniques into the binary- or bipolar-valued vectors required by attractor network models, while maintaining the important inter-vector distance relationships. We describe an algorithm we call discrete multidimensional scaling which accomplishes this, and present the results of a set of experiments using this algorithm. Introduction Our goal is to develop a connectionist model of lex..