BLOB DETECTION TECHNIQUE USING IMAGE PROCESSING FOR IDENTIFICATION OF MACHINE...
Presentation_final
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O
P T I C
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L MU S
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C CO I I ORE GN T N
A Seminar By Neha Javalagi(302015)
Under the guidance of:
Prof. Mrs Vidya B Patil
Department of Computer Engineering,MIT Pune 1
2. Application of optical character recognition
technique.
It interprets sheet music or printed scores into
editable or playable form. e.g. MIDI (for
playback) and MusicXML (for page layout).
Optical Music Recognition introduces a method
to generate music using pictures.
Offline and Online OMR.
Department of Computer Engineering,MIT Pune
Introduction to Optical Music
Recognition
3. Department of Computer Engineering,MIT Pune
Comparison between OMR and
OCR
Optical character recognition (OCR) is the mechanical
or electronic conversion of images of typewritten or
printed text into machine-encoded text .
These Group of musical symbols are analogous to the
rows of characters in optical character recognition.
4. Department of Computer Engineering,MIT Pune
1. Musical symbols are connected by staff lines.
2. Musical symbols on the same score can have
great variation in relative sizes.
WHY do ordinary OCR
techniques not perform well for
music scores?
5. Department of Computer Engineering,MIT Pune
Need of OMR
1. Addresses the problem of musical data
acquisition .
2. New functionalities and capabilities with
interactive multimedia technologies.
3. Playback ,musical analysis, reprinting,
editing, and digital archiving.
4. Archiving music, Preservation of cultural
heritage.
5. Help the composer compose efficiently and
creatively.
7. Department of Computer Engineering,MIT Pune
Steps in OMR
Play on any device
Create a MIDI equivalent of the original musical
score
Recognize Semantics of Music Notation
Symbol Identification
Musical Object Location
Staff Line Identification
Scanning with Optical Scanner
8. Department of Computer Engineering,MIT Pune
Step 1: Detection of staff lines
The staff lines embed some other information that is very important for the
optical music recognition. Following information is important for various reasons:
1. The thickness of the staff lines
2. Staff spacing.
3. The inclination of the staff lines.
9. Department of Computer Engineering,MIT Pune
• The staff lines graphically connect most musical
symbols, thus interfering with the recognition of the
symbols.
• Staff lines disturb the contour of the musical
symbols.
• So, the staff line presents, to some extent, noise
to the recognition of musical symbols.
• While staff lines make the recognition of music
symbols difficult, the musical symbols also make
Difficulties in Detection of Staff Lines
and Symbols
10. Department of Computer Engineering,MIT Pune
Methods for Detection of Staff
Lines
1.Hough Transform
The three plots intersect in one single
point (0.925.9.6) These coordinates are
the parameters ( θ , r) or line in which the
three points lay.
If for a given point,we plot the family of
lines that goes through it, we get a
sinusoid. For instance, for x0=8 and
y0=6and we get the following plot where
r>0 and θ < 0 and > 2π.
11. Department of Computer Engineering,MIT Pune
Other Methods
2.Horizontal Projection
3. Frequency Count(20msec)
4. Fourier Transform(140msec)
5. Co-relation(26612msec)
6.Template Matching
12. Department of Computer Engineering,MIT Pune
Morphological
Closing
1. extract chunks of staff lines; 2. regularize their shapes;
3. extend the chunks horizontally; 4. correct some defects;
5. select staff lines 6. reconstruct an image
without staff lines
16. Department of Computer Engineering,MIT Pune
• MIDI(Musical Instrument Digital Interface) is a
technical standard that specifies certain values for
electronic musical instruments and other related
devices.
• Advantages of MIDI include:
• compactness (an entire song can be coded in a
few hundred lines, i.e. in a few kilobytes),
• ease of modification and manipulation
• choice of instruments.
Step 3: MIDI Transform
17. Department of Computer Engineering,MIT Pune
Issues and Challenges
• Unavailability of a master music dataset with different
deformations to test different OMR systems.
• Lack of a framework with appropriate metrics to measure the
accuracy of different OMR systems.
• Ambiguous typesetting.
• The distorted staff lines are a common problem in both
printed & handwritten scores.
• The staff lines are often not straight or horizontal (due to
wrinkles or poor digitization), and in some cases hardly
parallel to each other.
• Old works in which the quality of the paper and ink has
decreased severely.
• Broken and overlapping symbols, differences in sizes and
shapes and zones of high density of symbols.
18. Department of Computer Engineering,MIT Pune
Existing technologies
• Existing softwares :
~Capella-scan Info
~ForteScan Light
~PDFtoMUSIC
~PhotoScore
~Audiveris (open source)
• Existing humanoids:
~Toyota Motor Company has developed Music-playing
humanoids.
19. Department of Computer Engineering,MIT Pune
Case-Study
Audiveris:
Main features:
1.Printed music as input (no handwritten music)
2Standard music notation (no tablatures yet)
3.Input formats: PDF, JPG, PNG, TIFF, BMP, ...
4.Output format: Music XML
5.Any number of pages per score, of parts per system, of staves per part, of voices per
measure
6.Internal neural network trainable by end user
7.Available on Windows and Linux
8.GNU GPL V2 license
20. Department of Computer Engineering,MIT Pune
Case-Study(2)
Robot Vision System
1.System Architecture –
• Computer:Intel® Pentium® 4 CPU 2.4GHz 1GBRAM
• Operating system:Microsoft Windows XP SP3
• PTZ camera:Canon Product VC-C4
• Developing tool:Borland C++ Builder 6.0
2.Result Analysis:
• On considering results from the printed piano scores of 10 songs, This
module only costs 200~300 milliseconds.
• System can recognize and play the printed piano scores in real time and
the recognition rate is over 87% on an average.
21. Department of Computer Engineering,MIT Pune
Future Trends
• Advanced systems that can create both background an
main music from one input picture.
• Research the relationship between feelings and
instruments.
• Systems that create harmonious music like an orchestr
ensemble.
• System that only generates worthy and new music.
bots.
• Robot ensemble orchestra.
• Cloud computing to provide "OMR as a service”.
