MATCHING REAL AND SYNTHETIC PANORAMIC IMAGES USING A VARIANT OF GEOMETRIC HASHING

This work demonstrates an approach to automatically initialize a visual model-based tracker, and recover from lost tracking, without prior camera pose information. These approaches are commonly referred to as tracking-by-detection. Previous tracking-by-detection techniques used either fiducials (i.e. landmarks or markers) or the object’s texture. The main contribution of this work is the development of a tracking-by-detection algorithm that is based solely on natural geometric features. A variant of geometric hashing, a model-to-image registration algorithm, is proposed that searches for a matching panoramic image from a database of synthetic panoramic images captured in a 3D virtual environment. The approach identifies corresponding features between the matched panoramic images. The corresponding features are to be used in a photogrammetric space resection to estimate the camera pose. The experiments apply this algorithm to initialize a model-based tracker in an indoor environment using the 3D CAD model of the building

A comparative analysis of scanned maps and imagery for mapping applications

Abstract In mapping organizations, the implementation of more automation coupled with the availability of heterogeneous data requires the investigation, adaptation and evaluation of new approaches and techniques. The demand for rapid mapping operations such as database generation and updating is continuously increasing. Due to the rising use of raster data, image analysis techniques have been investigated and tested in this study to introduce automation in the assessment of scanned topographic monochrome maps and Landsat 7 ETM+ imagery for feature separation and extraction in northern Canada. The work focuses on the detection and extraction of lakes -predominant features in the North -as well as on to their spatiotemporal comparison. Various approaches using digital image processing techniques were implemented and evaluated. Thresholding and texture measures were used to evaluate the potential of rapid extraction of certain topographic elements from scanned monochrome maps of northern Canada. A raster to vector approach (R ! V) followed for the vectorization of these extracted features. The extraction of features from Landsat 7 ETM+ imagery involved image and theme enhancement by applying various image fusion and spectral transformations (e.g., Brovey, PCI-IMGFUSE, intensity -hue -saturation (IHS), principal component analysis (PCA), Tasseled Cap, Normalized Difference Vegetation Index (NDVI)), followed by image classification and thresholding. Tests showed that the approaches were more or less feature-dependent, while, at the same time, they can augment and significantly enhance the conventional topographic mapping methods. Following the analysis of the map and image data, change detection between two lake datasets was performed both interactively and in an automated mode based on the non-intersection of old and new features. The various approaches and methodology developed and implemented within a GIS environment along with examples, results and limitations are presented and discussed. Crow

Vehicle localization by lidar point correlation improved by change detection

LiDAR sensors are proven sensors for accurate vehicle localization. Instead of detecting and matching features in the LiDAR data, we want to use the entire information provided by the scanners. As dynamic objects, like cars, pedestrians or even construction sites could lead to wrong localization results, we use a change detection algorithm to detect these objects in the reference data. If an object occurs in a certain number of measurements at the same position, we mark it and every containing point as static. In the next step, we merge the data of the single measurement epochs to one reference dataset, whereby we only use static points. Further, we also use a classification algorithm to detect trees. For the online localization of the vehicle, we use simulated data of a vertical aligned automotive LiDAR sensor. As we only want to use static objects in this case as well, we use a random forest classifier to detect dynamic scan points online. Since the automotive data is derived from the LiDAR Mobile Mapping System, we are able to use the labelled objects from the reference data generation step to create the training data and further to detect dynamic objects online. The localization then can be done by a point to image correlation method using only static objects. We achieved a localization standard deviation of about 5 cm (position) and 0.06° (heading), and were able to successfully localize the vehicle in about 93 % of the cases along a trajectory of 13 km in Hannover, Germany

Pléiades project: Assessment of georeferencing accuracy, image quality, pansharpening performence and DSM/DTM quality

Pléiades 1A and 1B are twin optical satellites of Optical and Radar Federated Earth Observation (ORFEO) program jointly running by France and Italy. They are the first satellites of Europe with sub-meter resolution. Airbus DS (formerly Astrium Geo) runs a MyGIC (formerly Pléiades Users Group) program to validate Pléiades images worldwide for various application purposes. The authors conduct three projects, one is within this program, the second is supported by BEU Scientific Research Project Program, and the third is supported by TÜBİTAK. Assessment of georeferencing accuracy, image quality, pansharpening performance and Digital Surface Model/Digital Terrain Model (DSM/DTM) quality subjects are investigated in these projects. For these purposes, triplet panchromatic (50 cm Ground Sampling Distance (GSD)) and VNIR (2 m GSD) Pléiades 1A images were investigated over Zonguldak test site (Turkey) which is urbanised, mountainous and covered by dense forest. The georeferencing accuracy was estimated with a standard deviation in X and Y (SX, SY) in the range of 0.45m by bias corrected Rational Polynomial Coefficient (RPC) orientation, using ~170 Ground Control Points (GCPs). 3D standard deviation of ±0.44m in X, ±0.51m in Y, and ±1.82m in Z directions have been reached in spite of the very narrow angle of convergence by bias corrected RPC orientation. The image quality was also investigated with respect to effective resolution, Signal to Noise Ratio (SNR) and blur coefficient. The effective resolution was estimated with factor slightly below 1.0, meaning that the image quality corresponds to the nominal resolution of 50cm. The blur coefficients were achieved between 0.39-0.46 for triplet panchromatic images, indicating a satisfying image quality. SNR is in the range of other comparable space borne images which may be caused by de-noising of Pléiades images. The pansharpened images were generated by various methods, and are validated by most common statistical metrics and also visual interpretation. The generated DSM and DTM were achieved with ±1.6m standard deviation in Z (SZ) in relation to a reference DTM.Airbus Defence and SpaceBEU/2014-47912266-01TÜBİTAK/114Y38

Orientation of oblique airborne image sets - Experiences from the ISPRS/Eurosdr benchmark on multi-platform photogrammetry

During the last decade the use of airborne multi camera systems increased significantly. The development in digital camera technology allows mounting several mid- or small-format cameras efficiently onto one platform and thus enables image capture under different angles. Those oblique images turn out to be interesting for a number of applications since lateral parts of elevated objects, like buildings or trees, are visible. However, occlusion or illumination differences might challenge image processing. From an image orientation point of view those multi-camera systems bring the advantage of a better ray intersection geometry compared to nadir-only image blocks. On the other hand, varying scale, occlusion and atmospheric influences which are difficult to model impose problems to the image matching and bundle adjustment tasks. In order to understand current limitations of image orientation approaches and the influence of different parameters such as image overlap or GCP distribution, a commonly available dataset was released. The originally captured data comprises of a state-of-the-art image block with very high overlap, but in the first stage of the so-called ISPRS/EUROSDR benchmark on multi-platform photogrammetry only a reduced set of images was released. In this paper some first results obtained with this dataset are presented. They refer to different aspects like tie point matching across the viewing directions, influence of the oblique images onto the bundle adjustment, the role of image overlap and GCP distribution. As far as the tie point matching is concerned we observed that matching of overlapping images pointing to the same cardinal direction, or between nadir and oblique views in general is quite successful. Due to the quite different perspective between images of different viewing directions the standard tie point matching, for instance based on interest points does not work well. How to address occlusion and ambiguities due to different views onto objects is clearly a non-solved research problem so far. In our experiments we also confirm that the obtainable height accuracy is better when all images are used in bundle block adjustment. This was also shown in other research before and is confirmed here. Not surprisingly, the large overlap of 80/80% provides much better object space accuracy – random errors seem to be about 2-3fold smaller compared to the 60/60% overlap. A comparison of different software approaches shows that newly emerged commercial packages, initially intended to work with small frame image blocks, do perform very well

The relationship between baseline Organizational Readiness to Change Assessment subscale scores and implementation of hepatitis prevention services in substance use disorders treatment clinics: a case study

<p>Abstract</p> <p>Background</p> <p>The Organizational Readiness to Change Assessment (ORCA) is a measure of organizational readiness for implementing practice change in healthcare settings that is organized based on the core elements and sub-elements of the Promoting Action on Research Implementation in Health Services (PARIHS) framework. General support for the reliability and factor structure of the ORCA has been reported. However, no published study has examined the utility of the ORCA in a clinical setting. The purpose of the current study was to examine the relationship between baseline ORCA scores and implementation of hepatitis prevention services in substance use disorders (SUD) clinics.</p> <p>Methods</p> <p>Nine clinic teams from Veterans Health Administration SUD clinics across the United States participated in a six-month training program to promote evidence-based practices for hepatitis prevention. A representative from each team completed the ORCA evidence and context subscales at baseline.</p> <p>Results</p> <p>Eight of nine clinics reported implementation of at least one new hepatitis prevention practice after completing the six-month training program. Clinic teams were categorized by level of implementation-high (n = 4) versus low (n = 5)-based on how many hepatitis prevention practices were integrated into their clinics after completing the training program. High implementation teams had significantly higher scores on the patient experience and leadership culture subscales of the ORCA compared to low implementation teams. While not reaching significance in this small sample, high implementation clinics also had higher scores on the research, clinical experience, staff culture, leadership behavior, and measurement subscales as compared to low implementation clinics.</p> <p>Conclusions</p> <p>The results of this study suggest that the ORCA was able to measure differences in organizational factors at baseline between clinics that reported high and low implementation of practice recommendations at follow-up. This supports the use of the ORCA to describe factors related to implementing practice recommendations in clinical settings. Future research utilizing larger sample sizes will be essential to support these preliminary findings.</p