Hasso-Plattner-Institut
  
Hasso-Plattner-Institut
Prof. Dr. Jürgen Döllner
  
 

Flow-based Image and Video Abstraction

This project investigates automatic non-photorealistic image processing techniques for the creation of simplified stylistic illustrations from color images, videos and 3D renderings based on the bilateral and difference of Gaussians filters.

Our method extends the approach of [Winnemöller et al. 2006] to use iterated bilateral filtering for abstraction and difference-of-Gaussians (DoG) for edge extraction by adapting it to the local orientation of the input. To represent local orientation we construct a smooth tensor field. From the eigenvectors of this tensor field we derive a vector field that has similar characteristics as the edge tangent flow (ETF) of [Kang et al. 2007], but its computation is much less expensive. Besides gradient calculation, only smoothing with a box or Gaussian filter is necessary. In contrast to that, ETF construction requires several iterations of a nonlinear filter with large filter kernel.

The xy-separated bilateral filter [Pham and van Vliet 2005] used by Winnemöller et al. suffers from horizontal and vertical artifacts. These artifacts appear in particular when the filter is applied iteratively. Our approach works by first filtering in direction of the gradient and then filtering the intermediate result in perpendicular direction. When applied iteratively our approach does not suffer from horizontal or vertical artifacts and creates smooth output at curved boundaries.

DoG edges often look frayed and don’t reassemble straight line and curve segments very well. To work around this limitation, [Kang et al. 2007] recently introduced the concept of flow-based difference-of-Gaussians which, compared to DoG edges, create more coherent lines. They replaced the DoG filter by a flow-guided anisotropic kernel whose shape is defined by the ETF. Comparable high-quality results can be achieved by a separated implementation with corresponding reduced computational complexity. We first apply a one-dimensional difference-of- Gaussian filter in direction of the gradient and then apply smoothing along the vector field that we derive from the smoothed structure tensor.

Publications

Automated Image-Based Abstraction of Aerial Images

Semmo, Amir and Kyprianidis, Jan Eric and Döllner, Jürgen
In Painho, Marco and Santos, Maribel Yasmina and Pundt, Hardy, ed., Geospatial Thinking, of Lecture Notes in Geoinformation and Cartography, pages 359-378. Springer, 5 2010

DOI: 10.1007/978-3-642-12326-9_19

Abstract:



Aerial images represent a fundamental type of geodata with a broad range of applications in GIS and geovisualization. The perception and cognitive processing of aerial images by the human, however, still is faced with the specific limitations of photorealistic depictions such as low contrast areas, unsharp object borders as well as visual noise. In this paper we present a novel technique to automatically abstract aerial images that enhances visual clarity and generalizes the contents of aerial images to improve their perception and recognition. The technique applies non-photorealistic image processing by smoothing local image regions with low contrast and emphasizing edges in image regions with high contrast. To handle the abstraction of large images, we introduce an image tiling procedure that is optimized for post-processing images on GPUs and avoids visible artifacts across junctions. This is technically achieved by filtering additional connection tiles that overlap the main tiles of the input image. The technique also allows the generation of different levels of abstraction for aerial images by computing a mipmap pyramid, where each of the mipmap levels is filtered with adapted abstraction parameters. These mipmaps can then be used to perform level-of-detail rendering of abstracted aerial images. Finally, the paper contributes a study to aerial image abstraction by analyzing the results of the abstraction process on distinctive visible elements in common aerial image types. In particular, we have identified a high abstraction straction potential in landscape images and a higher benefit from edge enhancement in urban environments.

BibTeX file

@incollection{SKD10,
author = { Semmo, Amir and Kyprianidis, Jan Eric and D{\"o}llner, J{\"u}rgen },
title = { Automated Image-Based Abstraction of Aerial Images },
year = { 2010 },
pages = { 359-378 },
month = { 5 },
abstract = {

Aerial images represent a fundamental type of geodata with a broad range of applications in GIS and geovisualization. The perception and cognitive processing of aerial images by the human, however, still is faced with the specific limitations of photorealistic depictions such as low contrast areas, unsharp object borders as well as visual noise. In this paper we present a novel technique to automatically abstract aerial images that enhances visual clarity and generalizes the contents of aerial images to improve their perception and recognition. The technique applies non-photorealistic image processing by smoothing local image regions with low contrast and emphasizing edges in image regions with high contrast. To handle the abstraction of large images, we introduce an image tiling procedure that is optimized for post-processing images on GPUs and avoids visible artifacts across junctions. This is technically achieved by filtering additional connection tiles that overlap the main tiles of the input image. The technique also allows the generation of different levels of abstraction for aerial images by computing a mipmap pyramid, where each of the mipmap levels is filtered with adapted abstraction parameters. These mipmaps can then be used to perform level-of-detail rendering of abstracted aerial images. Finally, the paper contributes a study to aerial image abstraction by analyzing the results of the abstraction process on distinctive visible elements in common aerial image types. In particular, we have identified a high abstraction straction potential in landscape images and a higher benefit from edge enhancement in urban environments.
},
editor = { Painho, Marco and Santos, Maribel Yasmina and Pundt, Hardy },
publisher = { Springer },
series = { Lecture Notes in Geoinformation and Cartography },
booktitle = { Geospatial Thinking },
priority = { 0 }
}

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last change: Sun, 08 Sep 2013 19:03:41 +0200

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