Optimized static and video EEG rapid serial visual presentation (RSVP) paradigm based on motion surprise computation
| Autor: | Rajan Bhattacharyya, Deepak Khosla, David J. Huber |
|---|---|
| Rok vydání: | 2017 |
| Předmět: |
Contextual image classification
Computer science business.industry media_common.quotation_subject Computation ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION 02 engineering and technology 021001 nanoscience & nanotechnology 01 natural sciences Motion (physics) 010309 optics Surprise Rapid serial visual presentation 0103 physical sciences Computer vision Artificial intelligence 0210 nano-technology business Decoding methods media_common |
| Zdroj: | SPIE Proceedings. |
| ISSN: | 0277-786X |
| DOI: | 10.1117/12.2262911 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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