Computer Vision UNR Dr. George Bebis

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 for gender, 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 , 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 , 2009.

Face Recognition 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 Using Genetic Algorithms Reconstruct Image Fused Image 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 , 2006.

Face Recognition (cont’d)

Vehicle Detection and Tracking Ford’s low light cameraFord’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 , 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 , January 2009.

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