Teledyne Scientific and Imaging, LLC

Teledyne Licensing, LLC

Rockwell Scientific Licensing LLC

Mario Aguilar

Ming Qian

Aaron B Hawkins

Mark Alan Peot

Claudio M Privitera

Patrick M Connolly

Karen Zachery

Jiangying Zhou

Mark A Peot

Aaron T Hawkins

EEG-based acceleration of second language learning is accomplished by measuring via single-trial EEG a learner's cognitive response to the presentation (visual or auditory) of language learning materials and updating a user model of latent traits related to language-learning skills in accordance with the cognitive response. The user model is suitably updated with each trial, each trial being triggered by learner fixation on a portion of visual materials and/or a next phoneme in auditory materials. Additional discrimination may be achieved through the use of saccades or fixation duration features.

EEG-based acceleration of second language learning

A computer vision system provides a universal scene descriptor (USD) framework and methodology for using the USD framework to extract multi-level semantic metadata from scenes. The computer vision system adopts the human vision system principles of saliency, hierarchical feature extraction and hierarchical classification to systematically extract scene information at multiple semantic levels.

Biologically-inspired metadata extraction (BIME) of visual data using a multi-level universal scene descriptor (USD)

Fixation-locked measurement of brain activity generates time-coded cues indicative of whether an operator exhibited a significant cognitive response to task-relevant stimuli. The free-viewing environment is one in which the presentation of stimuli is natural to the task encompassing both pre- and post-fixation stimuli and the operator is allowed to move his or her eyes naturally to perform the task.

Fixation-locked measurement of brain responses to stimuli

The human neural response is coupled with computer pattern analysis for robust single-event detection of significant non-stationary brain responses triggered upon occurrence of a task-relevant stimulus. Classifier performance is enhanced fusing together the outputs of multiple different classifiers albeit multiple spatial classifiers to extract a temporal pattern as the brain response evolves, time and frequency-based spatio-temporal classifiers, and/or EEG and pupillary classifiers.

Coupling human neural response with computer pattern analysis for single-event detection of significant brain responses for task-relevant stimuli

Bayesian super-resolution techniques fuse multiple low resolution images (possibly from multiple bands) to infer a higher resolution image. The super-resolution and fusion concepts are portable to a wide variety of sensors and environmental models. The procedure is model-based inference of super-resolved information. In this approach, both the point spread function of the sub-sampling process and the multi-frame registration parameters are optimized simultaneously in order to infer an optimal estimate of the super-resolved imagery. The procedure involves a significant number of improvements, among them, more accurate likelihood estimates and a more accurate, efficient, and stable optimization procedure.

Bayesian approach for sensor super-resolution

A computationally efficient and robust approach for monitoring the instantaneous pupil response as a correlate to significant cognitive response to relevant stimuli derives data samples d(n) of the pupil diameter (area), v(n) of pupil velocity and a(n) of pupil acceleration from the pupillary response and segments the data samples into a sequence of time-shifted windows. A feature extractor extracts a plurality of spatio-temporal pupil features from the data samples from the response and baseline periods in each window. A classifier trained to detect patterns of the extracted spatio-temporal pupil features for relevant stimuli generates an output indicative of the occurrence of absence of a significant cognitive response in the subject to a relevant stimulus.

Fusion-based spatio-temporal feature detection for robust classification of instantaneous changes in pupil response as a correlate of cognitive response

A multi-scale filter pyramid is applied to one or more components of a multi-component input image to produce a fused and enhanced image that can be mapped to a display, such as a color display.

Multi-scale image fusion

An improved apparatus and method adaptively compresses the dynamic range of an image from a sensor by rescaling the value of every pixel to fit within the dynamic range of a low dynamic range display while preserving local image contrast and global scene appearance.

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Cary, NC, United States of America

Mario Aguilar