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Computer Vision Research @ UNR

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

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  1. Computer Vision Research @ UNR Dr. George Bebis http://www.cse.unr.edu/CVL

  2. 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

  3. Main CVL Research Areas Object detection/tracking Biometrics Segmentation 3D reconstruction 3D object recognition Human action recognition Applications

  4. Hand-based Authentication/Identification

  5. 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

  6. Fingerprint Identification small overlapping area minutiae input matching ID

  7. 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.

  8. Face Recognition appearance changes http://www.face-rec.org/

  9. 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

  10. 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.

  11. Face Recognition (cont’d)

  12. Vehicle Detection and Tracking Ford’s low light camera Ford’s Concept Car

  13. (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.

  14. Segmentation

  15. 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.

  16. 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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