The nature of statistical learning theory
The nature of statistical learning theory
Algorithms for Defining Visual Regions-of-Interest: Comparison with Eye Fixations
IEEE Transactions on Pattern Analysis and Machine Intelligence
Automatic measurement of quality metrics for colonoscopy videos
Proceedings of the 13th annual ACM international conference on Multimedia
Learning an Interest Operator from Human Eye Movements
CVPRW '06 Proceedings of the 2006 Conference on Computer Vision and Pattern Recognition Workshop
A Visual Attention Based Region-of-Interest Determination Framework for Video Sequences*
IEICE - Transactions on Information and Systems
Extraction of visual features with eye tracking for saliency driven 2D/3D registration
Image and Vision Computing
Improving the quality of color colonoscopy videos
Journal on Image and Video Processing - Color in Image and Video Processing
Quantitative study of geological target spotting with the use of eye tracking
Proceedings of the 2010 workshop on Eye gaze in intelligent human machine interaction
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The labeling of large quantities of medical video data by clinicians is a tedious and time consuming task. In addition, the labeling process itself is rigid, since it requires the expert's interaction to classify image contents into a limited number of predetermined categories. This paper describes an architecture to accelerate the labeling step using eye movement tracking data. We report some initial results in training a Support Vector Machine (SVM) to detect cancer polyps in colonoscopy video, and a further analysis of their categories in the feature space using Self Organizing Maps (SOM). Our overall hypothesis is that the clinician's eye will be drawn to the salient features of the image and that sustained fixations will be associated with those features that are associated with disease states.