Computer Vision & Biomimetic Object Recognition

Computer Vision &Biomimetic Object Recognition Bruce A. Draper Department of Computer Science January 28, 2008

Background : Computer Vision • The computer vision community specializes in the interpretation of image data • 3D reconstruction • Stereo analysis (up to N cameras) • Motion analysis • Includes image stabilization, image mosaicing, control • Mapping & Measurement • Object recognition • Model based • Knowledge based • Learned (supervised or unsupervised) • Traditionally funded by the military, but the domain of applications is expanding

Computer Vision Resources • CVPR & related conferences since 1983 (PRIP 1977-82) • Hosted 1999 CVPR in Ft. Collins • ICCV, ECCV, ACCV, ICPR, ICVS, … • Technical Committee of the IEEE (PAMI) • Journals • IEEE Trans. On Pattern Analysis and Machine Intelligence (PAMI) • Computer Vision and Image Understanding (CVIU) • International Journal of Computer Vision (IJCV) • Machine Vision and Applications (MVA) • IEEE Trans. on Image Processing (TIP) • Pattern Recognition • On-line tools and resources • CVOnline (web site resource) • OpenCV (open library of computer vision algorithms)

Background : Personal • Object recognition • Knowledge-based & learned • Applications • Face recognition • Evaluation of face recognition algorithms & covariates • With R. Beveridge (CS), G. Givens (Stats) • Modeling faces as hihg dimensional manifolds • With M. Kirby (Math), C. Peterson (Math), R. Beveridge (CS) • Landmark recognition for self-driving cars • Visual where am I? • Automatic population of geospatial data bases • Build semantic & temporal maps from satellite images • Biologically-inspired Cognitive Architectures (DARPA BICA) • With S. Kosslyn (Harvard) • Counting nesting seagulls on islands off the coast of Maine

What is this? Dirty little secret: computer vision systems can’t do this yet (not in general) Well, there’s a truck, driving over some rocks, with mountains in the background

My goal • Learn to recognize objects by mimicing human vision • At the level of regional functional anatomy • End-to-end systems that work! • Three examples of how human vision influences design: • Selective attention • Familiarity detection • Goal-directed object detection

Selective Attention • Human vision is selective • Overt attention : eye & head movements • Covert attention : internal data selection

Familiarity vs Recognition • People recognize whether an image is familiar before they recognize what it is So we show our system (SeeAsYou) a series of images…

Familiarity vs Recognition (II) • Then we give it new images, and ask it to retrieve “similar” images from the data set  Novel Image Retrieved Image

More examples  

Next… recognition • Did we recognize the leopard on the previous slide? • No, the answer was an image, not symbolic • Did we match the leopard image? • Depends: we matched it to a cheetah • If the goal was to match spotted cats (or wildlife, or …), we got it right • If the goal was to find leopards, then no. • Current research : top-down verification of specific goals based on evidential reasoning

Looking for new applications • Image inspection tasks currently done by humans • Rule of thumb : if people can’t do it, neither can our system • Object recognition • Not just measurement • Lots of data, limited training labels

Thank You Questions?

Computer Vision & Biomimetic Object Recognition