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Erin Plasse Advisors: Professor Hanson Professor Rudko. Image Processing Algorithm for Speech Acoustics. Introduction. Experiment done in 1960’s by Kenneth Stevens and Dr. Sven Öhman in Sweden Used a cineradiograph x-ray to take lateral images of the vocal tract - PowerPoint PPT Presentation
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Erin PlasseAdvisors: Professor Hanson
Professor Rudko
Introduction Experiment done in 1960’s by Kenneth
Stevens and Dr. Sven Öhman in Sweden Used a cineradiograph x-ray to take lateral
images of the vocal tract 31 utterances and 2 sentences were made Analyzed how articulators displace over
time 45 frames/second
Movie Clip
Image Processing
Perkell (1969)- Used manual methods to make tracings of the images
Typical Tracing
Perkell (1969) used manual
Typical Analysis
Perkell (1969) used manual measures
Goals & Parameters Design an algorithm in MATLAB to automate the
tracings using edge detection methods Trace certain articulators, such as, lips, velum,
epiglottis, hyoid bone, etc. Results should be similar to the original tracings Only 13 utterances were analyzed Obtain tracings for the 20 utterances not
analyzed by Perkell (1969) Manual extraction is time consuming Smooth and continuous curves
Design Alternatives
Snakes: Active Contour Models Matlab script written by Eric Debreuve
Chan-Vese Region Based Segmentation Algorithm Matlab script written by Shawn Lankton
EdgeTrak System for Ultrasound images VIMS Lab, University of Delaware
Customize one of above to create own design for the data
Snakes: Active Contour ModelsMichael Kass
Snake: Energy minimizing spline guided by external forces
Image forces pull it toward lines and edges
MATLAB code written by Eric Debreuve Only worked with binary images
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Chan-Vese Algorithm Region based segmentation Use homogeneity of intensity in a
region as the constraint Only applicable to closed contours Uses an initial mask region MATLAB script written by Shawn
Lankton
Pharynx using Chan-Vese
EdgeTrak System Li, Kambhamettu, Stone Uses gradient image forces and intensity
information in local regions Energy definition for snakes: ETotal = α Eint + β Eext
Energy band gap External energy is redefined for EdgeTrak
as: E′ext(vi) = Eband(vi) •Eext(vi) Not effective for closed contours Good for tracking tongue in noisy images
with high-contrast unrelated edges
Energy Minimization Band
Main contribution of EdgeTrak method, finds the intensity of the regions.Energy band regions are found around each snake elementFind mean intensity difference between regionsFind new external energy using band energy Minimize total energy using dynamic programming
EdgeTrak Program
The Final Design
Used methods from both the EdgeTrak System, Chan-Vese, and snake methods.
Implemented using MATLAB Used only the image gradient to find
edges Tongue is the articulator that is
focused on
MATLAB Code
User picks 5 points 33 snake elements found using spline
interpolation Computes internal and external energy of
initial snake elements Computes internal and external energies
of points surrounding each initial point Finds the surrounding point with the
lowest energy, this becomes new point New contour is graphed
MATLAB Code Demo
%Edge_trak_demo
%Coded by Erin Plasse
Final Results
Results-o Energy of original snake = -96.9553o Energy of new snake = 1.2244o Percent change Snake energy = 101.2629
Alpha = .2, Beta = .8, Delta = 5
E_snake_orig = -21.8775
E_snake_new = -0.5480
Percent_change_Snake_energy = 97.495
Initial Points
Final Points
Application to other articulators
Cont.
Future Work Apply the contour model to a
sequence of consecutive frames Find more articulators Use the intensity method for external
energy as described in the Edge Trak program
References Perkell, Joseph S.. Physiology of Speech Production:
Results and Implications of a Quantitative Cineradiographic Study. Cambridge, MA: The MIT Press, 1969.
Stevens, Kenneth and Öhman, Dr. Sven. (1963). “Cineradiographic Studies of speech:procedures and objectives.” J. Acoust. Soc. Am., 35, 1889.
M. Kass, A. Witkin, and D. Terzopoulos, “Snakes: Active Contour Models,” Int. J. Comput. Vis., vol. 1, pp. 321-331, 1988.
T.F. Chan, L.A. Vese. Active Contours Without Edges. IEEE Trans. On Img. Processing., vol. 10 , pp.266-277, 2001.
M. Li, C. Kambhametti, M. Stone. Automatic Contour Tracking in Ultrasound Images. 2004.
Questions?