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Computer Vision Research @ UNR. Dr. George Bebis http://www.cse.unr.edu/CVL. External Collaborators:. LANL. LLNL. Computer Vision Laboratory (CVL). Sponsors:. CVL was founded in 1998 to conduct basic and applied research in computer vision. Members 2 faculty 7 PhD students

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computer vision research @ unr

Computer Vision Research @ UNR

Dr. George Bebis

http://www.cse.unr.edu/CVL

computer vision laboratory cvl

External Collaborators:

LANL

LLNL

Computer Vision Laboratory (CVL)

Sponsors:

  • 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

main cvl research areas
Main CVL Research Areas

Object detection/tracking

Biometrics

Segmentation

3D reconstruction

3D object recognition

Human action recognition

Applications

hand based authentication identification cont d
Hand-based Authentication/Identification (cont’d)

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.

Extensions: use hand geometry for

gender, ethnicity, and age classification

fingerprint identification
Fingerprint Identification

small overlapping area

minutiae

input

matching

ID

fingerprint identification cont d
Fingerprint Identification (cont’d)

Super-Template Synthesis

super-template

matching

ID

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
Face Recognition

appearance changes

http://www.face-rec.org/

face recognition cont d
Face Recognition (cont’d)
  • 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).
  • Visible spectrum
    • High resolution, less sensitive to the presence of eyeglasses.
    • Sensitive to changes in illumination direction and facial expression.

LWIR

face recognition cont d1

Feature

Extraction

Reconstruct

Image

Fusion Using

Genetic Algorithms

Fused

Image

Face Recognition (cont’d)

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.

vehicle detection and tracking
Vehicle Detection and Tracking

Ford’s low light camera

Ford’s Concept Car

vehicle detection and tracking cont d

(a)

(b)

Vehicle Detection and Tracking (cont’d)
  • Our system can process 10 fps on average.
  • Classification error is close to 6% (FP + FN)

FP

FN

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 cont d
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
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
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