Download - Computer Vision Research @ UNR
Computer Vision Research @ UNR
Dr. George Bebis
http://www.cse.unr.edu/CVL
Computer Vision Laboratory (CVL)
• CVL was founded in 1998 to conduct basic and applied research in computer vision.
• Members
- 2 faculty- 7 PhD students- 2 MS students- 6 undergraduate students
Total funding:
$4.2M
Sponsors:
External Collaborators:
LLNL
LANL
Main CVL Research Areas
Biometrics Segmentation
Object detection/tracking
3D object recognition
3D reconstruction
Human action recognition
Applications
Hand-based Authentication/Identification
Hand-based Authentication/Identification (cont’d)
Extensions: use hand geometry forgender, ethnicity, and age classification
G. Amayeh, G. Bebis, A. Erol, and M. Nicolescu, "Hand-Based Verification and Identification Using Palm-Finger Segmentation and Fusion", Computer Vision and Image Understanding, vol 113, pp. 477-501, 2009.
Fingerprint Identification
minutiae small overlapping area
matching
input
ID
Fingerprint Identification (cont’d)
Super-Template Synthesis
matching
ID
super-template
T. Uz, G. Bebis, A. Erol, and S. Prabhakar, "Minutiae-Based Template Synthesis and Matching for Fingerprint Authentication", Computer Vision and Image Understanding, vol 113, pp. 979-992, 2009.
Face Recognition
http://www.face-rec.org/
appearance changes
Face Recognition (cont’d)
• Visible spectrum– High resolution, less sensitive to the presence of
eyeglasses.– Sensitive to changes in illumination direction and facial
expression.
• Thermal IR spectrum– Not sensitive to illumination changes.– Low resolution, sensitive to air currents, face heat
patterns, aging, and the presence of eyeglasses (i.e., glass is opaque to thermal IR).
LWIR
Face Recognition (cont’d)
Feature Extraction
Fusion UsingGenetic Algorithms
Reconstruct Image
FusedImage
G. Bebis, A. Gyaourova, S. Singh, and I. Pavlidis, "Face Recognition by Fusing Thermal Infrared and Visible Imagery", Image and Vision Computing, vol. 24, no. 7, pp. 727-742, 2006.
Face Recognition (cont’d)
Vehicle Detection and Tracking
Ford’s low light camera Ford’s Concept Car
Vehicle Detection and Tracking (cont’d)• Our system can process 10 fps on average.• Classification error is close to 6% (FP + FN)
(a) (b)FN
FP
Z. Sun, G. Bebis, and R. Miller, "Monocular Pre-crash Vehicle Detection: Features and Classifiers", IEEE Transactions on Image Processing , vol. 15, no. 7, pp. 2019-2034, July 2006.
Segmentation
Segmentation (cont’d)
L. Loss, G. Bebis, M. Nicolescu, and A. Skurikhin, "An Iterative Multi-Scale Tensor Voting Scheme for Perceptual Grouping of Natural Shapes in Cluttered Backgrounds", Computer Vision and Image Understanding (CVIU) vol. 113, no. 1, pp. 126-149, January 2009.
More information on Computer Vision
• Computer Vision Home Page http://www.cs.cmu.edu/afs/cs/project/cil/ftp/html/vision.html
• Home Page http://www.cs.unr.edu/CRCD
• UNR Computer Vision Laboratory http://www.cs.unr.edu/CVL