funded by the german research foundation dfg n.
Download
Skip this Video
Loading SlideShow in 5 Seconds..
Funded by the German Research Foundation (DFG) PowerPoint Presentation
Download Presentation
Funded by the German Research Foundation (DFG)

Loading in 2 Seconds...

play fullscreen
1 / 18

Funded by the German Research Foundation (DFG)

4 Vues Download Presentation
Télécharger la présentation

Funded by the German Research Foundation (DFG)

- - - - - - - - - - - - - - - - - - - - - - - - - - - E N D - - - - - - - - - - - - - - - - - - - - - - - - - - -
Presentation Transcript

  1. Funded by the German Research Foundation (DFG)

  2. PROJECT PARTNERS Database Research Group Department of Computer Science UNIVERSITY OF ROSTOCK Musicologists Institute for Musicology UNIVERSITY OF ROSTOCK Image Processing Fraunhofer Institute for Computer Graphics Rostock

  3. PROBLEMS IN MUSICOLOGY How to preserve historical material, and associate and manage corresponding knowledge? There exist multiple collections of historical manuscripts all over the world. One of them is the 18th century music scores collection at the University of Rostock.

  4. PROBLEMS IN MUSICOLOGY Who was the writer of a music score? ... What relation did he have to the composer? What is the paper made of? Who was the owner, user and collector of the copy? „Each music writer has a typical and unique handwriting signature, which can be defined on a set of a „few“ handwriting characteristics.

  5. COMPUTER SCIENCE SOLUTIONS A digital archive for storing handwritten historical music scores and their corresponding metadata Browsing and retrieval interfaces for musicologists, librarians and other library users Integrating experts knowledge for semi-automatic analysis of handwritings Image processing tools for automatic analysis of handwriting in music scores

  6. MODELING AND STORAGE

  7. BROWSING AND SEARCH

  8. WRITER IDENTIFICATION Music Score Feature Base Handwriting Classification Feature -Extraction Result from the Classification Handwriting Clusters Feature Vectors Handwriting Clustering empirical optimization

  9. HANDWRITING FEATURES • Clef • Slant • Note Stems • Note Flags • Note Beams • Accidentals • Rastrum • Note Head Form • Time Signatures • Bar Lines • Writing Habits • Note Beams Offset • Rests

  10. SEMI-AUTOMATIC HANDWRITING ANALYSIS ...1.2.2 closed loop ...1.2.2.1 ...1.2.2.3 acsending or descending ...1.2.2.1.7 ...1.2.2.1.3 ...1.2.2.1.5 left or right of G-point or loop clusters of scribes similarity matrix ... 1.2.2.1.7 ... 1.2.2.1 0.6 ... 1.2.2.3 0.2 ... 1.2.2.1.3 0.8 ... 1.2.2.1.5 0.4

  11. SEMI-AUTOMATIC HANDWRITING ANALYSIS

  12. AUTOMATIC HANDWRITING ANALYSIS Query Image Smoothing, histogram equalization, morphological operations Consistency Check Image Preprocessing Score Images Writer Identification (unknown writer) Consistency Check Segmentation Image Analysis Feature Extraction and indexing for known writers Recognize Music Symbols of interest Object Recognition Feature Vectors Pattern Matching, Classification Writer Identification Query Result

  13. AUTOMATIC HANDWRITING ANALYSIS

  14. WRITER CLASSIFICATION

  15. WE WILL BE HAPPY TO ANSWER YOUR QUESTIONS ...