Klaus Spitzer
RWTH Aachen University
55 Papers
296 Citations
Klaus Spitzer is an academic researcher from RWTH Aachen University. The author has contributed to research in topics: Image segmentation & Image retrieval. The author has an hindex of 14, co-authored 55 publications.
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Papers
Content-based image retrieval in medical applications
Thomas Martin Lehmann,Mark Oliver Güld,Christian Thies,Benedikt Fischer,Klaus Spitzer,Daniel Keysers,Hermann Ney,Michael Kohnen,Henning Schubert,Berthold B. Wein +9 more
TL;DR: The proposed architecture is suitable for content-based image retrieval in medical applications and improves current picture archiving and communication systems that still rely on alphanumerical descriptions, which are insufficient for image retrieval of high recall and precision.
Blended learning positively affects students’ satisfaction and the role of the tutor in the problem-based learning process: results of a mixed-method evaluation
TL;DR: To enhance students’ motivation and satisfaction and to overcome the problems with the changing quality of tutors, online learning and face-to-face classes were systematically combined resulting in a blended learning scenario (blended problem-based learning—bPBL).
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Gabor Filtering of Complex Hue/Saturation Images for Color Texture Classification
Christoph Palm,Daniel Keysers,Thomas Martin Lehmann,Klaus Spitzer +3 more
- 01 Jan 2000
TL;DR: The consideration of the color information enhances the classification of color texture and the introduced features suggest the use of the HSV-colorspace with less features than RGB.
Extended query refinement for medical image retrieval.
Thomas M. Deserno,Mark Oliver Güld,Bartosz Plodowski,Klaus Spitzer,Berthold B. Wein,Henning Schubert,Hermann Ney,Thomas Seidl +7 more
TL;DR: This paper presents a powerful user interface for CBIR that provides all three mechanisms for extended query refinement and has a significant impact for medical CBIR applications.
Colour texture analysis for quantitative laryngoscopy.
Justus Ilgner,Christoph Palm,Andreas G Schütz,Klaus Spitzer,Martin Westhofen,Thomas Martin Lehmann +5 more
TL;DR: The results document a first step towards an objective, machine-based classification of laryngeal images, which could provide the basis for further development of an expert system for use in indirect laryngoscopy.
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