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Proceedings Paper

Optimized static and video EEG rapid serial visual presentation (RSVP) paradigm based on motion surprise computation
Author(s): Deepak Khosla; David J. Huber; Rajan Bhattacharyya
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Paper Abstract

In this paper, we describe an algorithm and system for optimizing search and detection performance for “items of interest” (IOI) in large-sized images and videos that employ the Rapid Serial Visual Presentation (RSVP) based EEG paradigm and surprise algorithms that incorporate motion processing to determine whether static or video RSVP is used. The system works by first computing a motion surprise map on image sub-regions (chips) of incoming sensor video data and then uses those surprise maps to label the chips as either “static” or “moving”. This information tells the system whether to use a static or video RSVP presentation and decoding algorithm in order to optimize EEG based detection of IOI in each chip. Using this method, we are able to demonstrate classification of a series of image regions from video with an azimuth value of 1, indicating perfect classification, over a range of display frequencies and video speeds.

Paper Details

Date Published: 2 May 2017
PDF: 10 pages
Proc. SPIE 10200, Signal Processing, Sensor/Information Fusion, and Target Recognition XXVI, 102000X (2 May 2017); doi: 10.1117/12.2262911
Show Author Affiliations
Deepak Khosla, HRL Labs., LLC (United States)
David J. Huber, HRL Labs., LLC (United States)
Rajan Bhattacharyya, HRL Labs., LLC (United States)

Published in SPIE Proceedings Vol. 10200:
Signal Processing, Sensor/Information Fusion, and Target Recognition XXVI
Ivan Kadar, Editor(s)

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