2D/3D Image and Video processing
Members:
Asst. Prof. Yücel Yemez (
Prof. Lale Akarun (PRL)
Berk Gokberk (PRL)
Ceyhun Burak Akgül
Ender Konukoğlu
Erdem Yörük
Erhan Sütlü
Biometry and
Man-Machine Interfaces
Biometrics
addresses the problem of exploiting human body characteristics, such as face,
voice, gait, fingerprint, hand geometry etc. to recognize people. We are also
involved in the following projects:
Hand biometry
Researchers: Erdem Yörük, Bülent Sankur,
Ender Konukoglu, Jerome Darbon
Description: To develop a hand-based
biometric system using hand geometry and palm appearance data. A database of
the hand images of about 500 people is made available on the web.
Face detection and recognition
Researchers: Cem Demirkir, H. Kemal Ekenel, Bulent Sankur
Description: This work addresses the judicious combination of
principal-component and independent-component features for person recognition.
A wavelet-based scheme is investigated to combat the adverse effects of
illumination and face expression. We also investigate algorithms for
pose-independent face detection in cluttered scenes based on the idea of
Viola-Jones and extend it to video tracking of faces.
Behavior understanding
Researchers: Hatice Cinar, Bulent Sankur
Description: We consider office environment
where cameras track people and their behavior is interpreted.
Publications:
E. Yorük,
E. Konukoglu, J. Darbon, B. Sankur, Shape-Based
Hand Recognition, IEEE Image Processing, (under review), 2004.
H.K. Ekenel, B. Sankur, Feature selection in the independent component
subspace for face recognition, Pattern Recognition Letters, 25, 13771388, 2004.
H.K. Ekenel, B. Sankur,
Multiresolution Face Recognition, Image and Vision
Computing, (under review), 2004.
V. Starovoitov,
D. Samal, B. Sankur , Matching
Of Faces in Camera Images and Document Photographs, ICASSP'2000, Int.
Conference on Signal, Speech and Signal Processing, 5-9 June 2000, Istanbul.
M. Reijnders,
P. van Beek, B. Sankur,
J.C.A. van der Lubbe,
Facial Feature
Localization and Adaptation of a Generic Face Model for Model Based Coding,
Signal Processing: Image Communication, vol. 7, 57-75, March 1995.
B. Esme, B. Sankur,
3D Image Processing
3D Archeological/Cultural Object Indexing
Researchers: Ceyhun Burak Akgul, Helin Dutagaci, Bulent Sankur
Description: 3D datasets are being prepared and novel modeling, registration, feature extraction, data fusion, and classification methods are considered. We address specifically the determination of the class of museum art objects. This project is cooperating with the Cost project entitled Sculpteur.
Info: http://www.sculpteurweb.org/html/
Video Coding
Video object tracking
Description: We have developed a
scalable object-tracking framework, which is capable of tracking the contour of
nonrigid objects in the presence of occlusion. The
framework consists of open-loop boundary prediction and closed-loop boundary
correction parts. The closed-loop boundary correction block employs a suitably
weighted combination of low-level features such as color edge, color
segmentation, motion models, and motion segmentation for each subcontour.
Image and Video Quality
Description: We determine measures to evaluate
quantitatively the performance of video object segmentation and tracking
methods without ground-truth (GT) segmentation maps, based on spatial
differences of color and motion along the boundary and temporal differences between
the color histograms. Canonical correlation analysis has indicated that ground-truthless measures perform as well as ground-truthed measures. We assess statistically several objective
quality measures for still images with applications in steganalysis
and coding.
Publications:
C. Erdem Eroglu, B. Sankur, M. Tekalp, Performance
Measures for Video Object Segmentation and Tracking, IEEE Image
Processing, 13(7), 937-950, July 2004.
C. Erdem Eroglu, B. Sankur, M. Tekalp, Video
Object Tracking with Feedbackof
Performance Measures, IEEE
Circuit Systems and Video Processing, 13(4), 310-324, April 2003.
C.E. Eroglu, B. Sankur, Performance Measures for Video Object
Segmentation and Tracking, SPIE Conf. Visual Communications and
Image Processing 2003, Lugano, Switzerland, July 8-11
2003.
Avcibas, B. Sankur,
K. Sayood, Statistical
Evaluation of Image Quality Measures, Journal of Electronic Imaging,
11(2), 206-23, April 2002. (Availability software: QUALITY for
the calculation of several (26) image quality measures)
E. Anarım, Ç. Eroğlu
Erdem, G. Karabulut, E. Yanmaz, , "Motion Estimation
in the Frequency Domain Using Fuzzy C-Planes Clustering, IEEE
Trans. on Image Processing, October 2001.
Y. Yemez,
B. Sankur,
Image Compression and
Segmentation
We
have been involved in a comprehensive assessment effort for thresholding
methods, especially in the nondestructive testing tasks. We have considered
also color image segmentation schemes and the use of M-band wavelets.
Thresholding
Researchers: Mehmet Sezgin, Bulent Sankur
Description: We compare several (44) thresholding
algorithms and develop an
evaluation platform. We also consider the use of thresholding in non-destructive testing environments. Multithresholding schemes are currently being assessed.
Color-image Segmentation
Researchers: Fatih
Kurugollu, Bulent Sankur:
Description: We consider segmentation schemes for color
images such as with multi-channel thresholding or
constraint-satisfaction neural networks.
Compression
Researchers: Ismail
Avcibas, Bulent Sankur
Description: We
present a compression technique that provides progressive transmission as well
as lossless and near-lossless compression in a single framework.
M-band wavelets
Researchers: Yucel Yemez, Emin Anarim,
Bulent Sankur
Description:
We develop M-band wavelet schemes for the extraction of directional edges
and for compression purposes.
Publications:
B. Sankur, M. Sezgin, A
Survey Over Image Thresholding Techniques And Quantitative Performance Evaluation, Journal of
Electronic Imaging, 13(1), 146-165, January, 2004. (Available software: OTIMEC with graphic
user interface for the execution of several (42) image thresholding
algorithms)
I. Avcibas, B. Sankur,
M. Sezgin, B. Sankur, Image Multithresholding Based on Sample Moment Function,
ICIP2003: International Conference on Image Processing, Barcelona 2003.
F. Kurugollü, B. Sankur, E. Harmanci, Image
Segmentation by Relaxation Using Constraint Satisfaction Neural Network, Journal
of Image and Vision Computing, 20, 483-497, 2002.
I. Avcibas, N. Memon, B. Sankur, K. Sayood, Lossless
and Near-Lossless Image Compression with Successive Refinement, IEEE
Signal Processing Letters, 9(10), 312-314, October 2002.
V. Letournelle, B. Sankur, F. Pradeille, H. Maitre, Feature
Extraction for Quality Asessment of Aerial Image
Segmentation, ISPRS: International Soc. for Photogrammetry
and Remote Sensing, PCV'02 Photogrammetric Computer
Vision, 2002.
F. Kurugollü, B. Sankur, E. Harmanci Multiband Image Segmentation Using Histogram Multithresholding and Fusion, Journal of
Image and Vision Computing,
9(13), 915-928, 2001.
H. Caglar, S. Güntürk, B. Sankur, E. Anarim, VQ-Adaptive
Block Transform Coding of Images, IEEE
Trans. Image Processing, Vol. 7, pp. 110-116, January 1998.
Y. Yemez, T. Aydin, B. Sankur, E. Anarim, Multidirectional
and Multiscale Edge Detection via M-Band Wavelet
Transform, IEEE Trans. Image
Processing, Vol. 5, pp. 1370-1377, 1996.
Texture Analysis and
Pattern Recognition
Researchers: R. Meylani,
A. Latif-Amet, O.G. Sezer,
A. Serdaroğlu A. Ercil, A. Ertüzün
Description: To detect the defects encountered in textile
images. Many different algorithms and methods are investigated for this purpose
such as co-occurrence matrices, wavelets and subband
domain analysis, 2-D lattice filters and subspace methods such as independent
component and independent subspace analysis methods.
Publications:
Serdaroğlu, A. Ertüzün and A. Ercil, Defect
Detection in Textile Fabric Images Using Wavelet Transforms And Independent
Component Analysis accepted, Pattern
Recognition and Image Understanding : New Technologies, PRIA-7-2004, Oct. 18-23, 2004,
O.G. Sezer, A. Ertüzün
and A. Ercil, Textile Fabric Defect Detection using
Independent Component Analysis, Submittd to Image and Vision Computing, 2003.
O. G. Sezer, A. Ertuzun and A. Ercil "Independent Component Analysis for Texture
Defect Detection", Pattern Recognition and Image Analysis, vol. 14,
no. 2, 2004, pp.
303-307.
Latif Amet, A. Ertüzün and A. Erçil, An Efficient Method for Texture Defect Detection: Subband Domain Co-Occurrence Matrices, Image and Vision Computing, May 2000,
pp.543-553.
S. Özdemir, A. Baykut, R. Meylani, A. Erçil and A. Ertüzün, "Comparative Evaluation of Texture Analysis
Algorithms for Defect Inspection of Textile Products", in Proc. Int. Conf. on Computer Vision and
Pattern Recognition (ICVP98), Aug. 14-17, 1998, Brisbane, Australia,
pp.1738-1740.
Latif Amet, A. Ertüzün and A. Erçil, Texture Defect Detection Using Subband
Domain Co-occurrence Matrices, in Proc.
IEEE Southeast Symp. for Image Analysis and
Interpretation (SSIAI 98), April 6-7 1998,
R. Meylani, A. Ertüzün and A. Erçil, "A
Comparative Study on the Adaptive Lattice Filters for Defect Detection",
in Proc. 3rd IEEE Int. Conf. on
Electronics, Circuits and Systems (ICECS96), Oct. 13-16, 1996, Rhodes,
Greece, pp. 976-979.
R. Meylani, A. Ertüzün
and A. Erçil, "Texture Defect Detection Using
the Adaptive Two-Dimensional Lattice Filter", in Proc. IEEE Int. Conf. on Image Processing, Sept. 16-19, 1996, Laussanne, Switzerland, pp. 165-168.