SGER: Stochastic Algorithms for Visual Search and Recognition

University Of California-Los Angeles, Los Angeles CA

Investigators

Abstract

SGER Proposal 0240148 DDMCMC Algorithms for Rapid Search and Detection PI: A.L. Yuille University of California, LA The proposed study is to design and implement a rapid visual search algorithm called DDMCMC, which uses data-driven feedforward visual cues to drive a Monte Carlo algorithm. This approach combines the speed of feedforward algorithms with the high quality performance of top-down model instantiation algorithms. DDMCMC was developed for the image segmentation problem by the Co-PI and was demonstrated to give fast and very accurate segmentation results on large datasets (with groundtruth). DDMCMC is generalized to the search and detection task by using techniques such as AdaBoost learning to determine effective feedforward algorithms to drive the MCMC algorithm. The broader impact is to develop computer vision algorithms to help the blind and visually impaired (though we will also use this grant to train graduate students). To achieve this, a pilot study of the DDMCMC algorithm will be developed for the specific task of searching for, and then reading, informational signs (the results can be communicated to a visually impaired user by a speech synthesizer). This study will be aided by researchers at the Smith-Kettlewell Eye Research Institute (SKERI) who include two blind engineers. The proposed algorithms will be designed and tested on image datasets taken by blind volunteers using head or body mounted cameras (to ensure the realism of our approach). Researchers at SKERI will also give feedback on the practicality of the approach and how it compares with alternative technologies for this task (none of which use computer vision). Computer vision has enormous potential to help the visually disabled provided fast and effective algorithms are developed.