Numerical recipes in C (2nd ed.): the art of scientific computing
Numerical recipes in C (2nd ed.): the art of scientific computing
Modeling visual attention via selective tuning
Artificial Intelligence - Special volume on computer vision
Alignment by Maximization of Mutual Information
International Journal of Computer Vision
A Model of Saliency-Based Visual Attention for Rapid Scene Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Comparison of Simularity Measures for use in 2D-3D Medical Image Registration
MICCAI '98 Proceedings of the First International Conference on Medical Image Computing and Computer-Assisted Intervention
Multi-modal Volume Registration Using Joint Intensity Distributions
MICCAI '98 Proceedings of the First International Conference on Medical Image Computing and Computer-Assisted Intervention
The Correlation Ratio as a New Similarity Measure for Multimodal Image Registration
MICCAI '98 Proceedings of the First International Conference on Medical Image Computing and Computer-Assisted Intervention
Mixed Reality Merging of Endoscopic Images and 3-D Surfaces
MICCAI '00 Proceedings of the Third International Conference on Medical Image Computing and Computer-Assisted Intervention
An improved illumination model for shaded display
SIGGRAPH '79 Proceedings of the 6th annual conference on Computer graphics and interactive techniques
Volume composition and evaluation using eye-tracking data
ACM Transactions on Applied Perception (TAP)
Volume composition using eye tracking data
EUROVIS'06 Proceedings of the Eighth Joint Eurographics / IEEE VGTC conference on Visualization
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This paper presents a new technique for extracting visual saliency from experimental eye tracking data. An eye-tracking system is employed to determine which features that a group of human observers considered to be salient when viewing a set of video images. With this information, a biologically inspired saliency map is derived by transforming each observed video image into a feature space representation. By using a feature normalisation process based on the relative abundance of visual features within the background image and those dwelled on eye tracking scan paths, features related to visual attention are determined. These features are then back projected to the image domain to determine spatial areas of interest for unseen video images. The strengths and weaknesses of the method are demonstrated with feature correspondence for 2D to 3D image registration of endoscopy videos with computed tomography data. The biologically derived saliency map is employed to provide an image similarity measure that forms the heart of the 2D/3D registration method. It is shown that by only processing selective regions of interest as determined by the saliency map, rendering overhead can be greatly reduced. Significant improvements in pose estimation efficiency can be achieved without apparent reduction in registration accuracy when compared to that of using a non-saliency based similarity measure.