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 ) – 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: 1. Selective attention 2. Familiarity detection 3. 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 ImageRetrieved 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?