30,465 research outputs found
Details of Deformable Part Models for Automatically Georeferencing Historical Map Images
Libraries are digitizing their collections of maps from all eras, generating increasingly large online collections of historical cartographic resources. Aligning such maps to a modern geographic coordinate system greatly increases their utility. This work presents a method for such automatic georeferencing, matching raster image content to GIS vector coordinate data. Given an approximate initial alignment that has already been projected from a spherical geographic coordinate system to a Cartesian map coordinate system, a probabilistic shape-matching scheme determines an optimized match between the GIS contours and ink in the binarized map image. Us- ing an evaluation set of 20 historical maps from states and regions of the U.S., the method reduces average alignment RMSE by 12%
Deformable Part Models for Automatically Georeferencing Historical Map Images
Libraries are digitizing their collections of maps from all eras, generating increasingly large online collections of historical cartographic resources. Aligning such maps to a modern geographic coordinate system greatly increases their utility. This work presents a method for such automatic georeferencing, matching raster image content to GIS vector coordinate data. Given an approximate initial alignment that has already been projected from a spherical geographic coordinate system to a Cartesian map coordinate system, a probabilistic shape-matching scheme determines an optimized match between the GIS contours and ink in the binarized map image. Using an evaluation set of 20 historical maps from states and regions of the U.S., the method reduces average alignment RMSE by 12%
Data-Driven Shape Analysis and Processing
Data-driven methods play an increasingly important role in discovering
geometric, structural, and semantic relationships between 3D shapes in
collections, and applying this analysis to support intelligent modeling,
editing, and visualization of geometric data. In contrast to traditional
approaches, a key feature of data-driven approaches is that they aggregate
information from a collection of shapes to improve the analysis and processing
of individual shapes. In addition, they are able to learn models that reason
about properties and relationships of shapes without relying on hard-coded
rules or explicitly programmed instructions. We provide an overview of the main
concepts and components of these techniques, and discuss their application to
shape classification, segmentation, matching, reconstruction, modeling and
exploration, as well as scene analysis and synthesis, through reviewing the
literature and relating the existing works with both qualitative and numerical
comparisons. We conclude our report with ideas that can inspire future research
in data-driven shape analysis and processing.Comment: 10 pages, 19 figure
Joint Material and Illumination Estimation from Photo Sets in the Wild
Faithful manipulation of shape, material, and illumination in 2D Internet
images would greatly benefit from a reliable factorization of appearance into
material (i.e., diffuse and specular) and illumination (i.e., environment
maps). On the one hand, current methods that produce very high fidelity
results, typically require controlled settings, expensive devices, or
significant manual effort. To the other hand, methods that are automatic and
work on 'in the wild' Internet images, often extract only low-frequency
lighting or diffuse materials. In this work, we propose to make use of a set of
photographs in order to jointly estimate the non-diffuse materials and sharp
lighting in an uncontrolled setting. Our key observation is that seeing
multiple instances of the same material under different illumination (i.e.,
environment), and different materials under the same illumination provide
valuable constraints that can be exploited to yield a high-quality solution
(i.e., specular materials and environment illumination) for all the observed
materials and environments. Similar constraints also arise when observing
multiple materials in a single environment, or a single material across
multiple environments. The core of this approach is an optimization procedure
that uses two neural networks that are trained on synthetic images to predict
good gradients in parametric space given observation of reflected light. We
evaluate our method on a range of synthetic and real examples to generate
high-quality estimates, qualitatively compare our results against
state-of-the-art alternatives via a user study, and demonstrate
photo-consistent image manipulation that is otherwise very challenging to
achieve
Aligning archive maps and extracting footprints for analysis of historic urban environments.
Archive cartography and archaeologist's sketches are invaluable resources when analysing a historic town or city. A virtual reconstruction of a city provides the user with the ability to navigate and explore an environment which no longer exists to obtain better insight into its design and purpose. However, the process of reconstructing the city from maps depicting features such as building footprints and roads can be labour intensive. In this paper we present techniques to aid in the semi-automatic extraction of building footprints from digital images of archive maps and sketches. Archive maps often exhibit problems in the form of inaccuracies and inconsistencies in scale which can lead to incorrect reconstructions. By aligning archive maps to accurate modern vector data one may reduce these problems. Furthermore, the efficiency of the footprint extraction methods may be improved by aligning either modern vector data or previously extracted footprints, since common elements can be identified between maps of differing time periods and only the difference between the two needs to be extracted. An evaluation of two alignment approaches is presented: using a linear affine transformation and a set of piecewise linear affine transformations
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