Mr. Thomas Vogel Profile

Thomas Vogel

at Otto-von-Guericke-Univ Magdeburg

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Proceedings Article | 17 January 2006 Paper

BlobContours: adapting Blobworld for supervised color- and texture-based image segmentation

Thomas Vogel, Dinh Quyen Nguyen, Jana Dittmann

Proceedings Volume 6073, 60730I (2006) https://doi.org/10.1117/12.650742

KEYWORDS: Image segmentation, Image processing algorithms and systems, Image processing, Image quality, Image retrieval, MATLAB, Visualization, C++, Expectation maximization algorithms, Feature extraction

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Extracting features is the first and one of the most crucial steps in recent image retrieval process. While the color features and the texture features of digital images can be extracted rather easily, the shape features and the layout features depend on reliable image segmentation. Unsupervised image segmentation, often used in image analysis, works on merely syntactical basis. That is, what an unsupervised segmentation algorithm can segment is only regions, but not objects. To obtain high-level objects, which is desirable in image retrieval, human assistance is needed. Supervised image segmentations schemes can improve the reliability of segmentation and segmentation refinement. In this paper we propose a novel interactive image segmentation technique that combines the reliability of a human expert with the precision of automated image segmentation. The iterative procedure can be considered a variation on the Blobworld algorithm introduced by Carson et al. from EECS Department, University of California, Berkeley. Starting with an initial segmentation as provided by the Blobworld framework, our algorithm, namely BlobContours, gradually updates it by recalculating every blob, based on the original features and the updated number of Gaussians. Since the original algorithm has hardly been designed for interactive processing we had to consider additional requirements for realizing a supervised segmentation scheme on the basis of Blobworld. Increasing transparency of the algorithm by applying usercontrolled iterative segmentation, providing different types of visualization for displaying the segmented image and decreasing computational time of segmentation are three major requirements which are discussed in detail.

Proceedings Article | 21 March 2005 Paper

Illustration watermarking: an object-based approach for digital images

Thomas Vogel, Jana Dittmann

Proceedings Volume 5681, (2005) https://doi.org/10.1117/12.586977

KEYWORDS: Digital watermarking, Image segmentation, Image processing, Visualization, Visual process modeling, Databases, Image quality, Computer security, Computer programming, Expectation maximization algorithms

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Digital watermarking techniques are used for a variety of applications, e.g. to protect copyrights of users, to guarantee the integrity of content or to provide additional information embedded in the media. Irrespective of the application, in common watermarking algorithms embedded information is either spread over the entire medium or concentrated at dedicated positions given by a visual or psychoacoustical model. In most cases syntactical and not semantical aspects determine embedding. In our paper we introduce an object-based DWT annotation watermarking scheme respecting the semantical characteristics of digital images for a so-called illustration watermarking. Here the goal is to embed annotation directly into the image objects by illustrating them with further information. Beside the semantic analysis for the illustrations we evaluate the competing properties capacity, non-perceptibility and robustness. Security issues are less important, since an annotation watermark does generally not need to be difficult to remove. If an attacker wants to destroy the embedded information there is often no need to keep him from that. Annotated data would lose value and therefore there is in most cases no attack motivation. After discussing the requirements for object-based watermarking we introduce the model of watermarking of data for illustrating the content and describe generic watermark embedding and extraction, as well as all necessary preprocessing like segmentation, feature extraction and the visual model.

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