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Click to download PDF version Click to download BibTeX data Clik to view abstract K. Thakoor, S. Marat, P. Nasiatka, B. Mcintosh, F. Sahin, A. R. Tanguay, J. D. Weiland, L. Itti, Attention-Biased Speeded-Up Robust Features (AB-SURF): A Neurally-Inspired Object Recognition Algorithm for A Wearable Aid for the Visually Impaired, In: Proc. IEEE Conference on Multimedia and Expo (ICME), Workshop on Multimodal and Alternative Perception for the Visually Impaired People (MAP4VIP), Jul 2013. [2013 acceptance rate: 50.0%]

Abstract: Humans recognize objects effortlessly, in spite of changes in scale, position, and illumination. Emulating human recognition in machines remains a challenge. This paper describes computer vision algorithms aimed at helping visually-impaired people locate and recognize objects. Our neurally-inspired computer vision algorithm, called Attention Biased Speeded Up Robust Features (AB-SURF), harnesses features that characterize human visual attention to make the recognition task more tractable. An attention biasing algorithm selects the most task-driven salient regions in an image. Next, the SURF object recognition algorithm is applied on this narrowed subsection of the original image. Testing on images containing 5 different objects exhibits accuracies ranging from 80% to 100%. Furthermore, testing on images containing 10 objects yields accuracies between 63% and 96% for the 5 objects that occupy the largest area within the image subwindows chosen by attention biasing. A five-fold speed-up is attained using AB-SURF as compared to the time estimated for sliding window recognition on the same images.

Note: Best Student Paper Award

Themes: Model of Bottom-Up Saliency-Based Visual Attention, Computer Vision, Medical Research


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