Results 181 to 190 of about 4,716 (218)
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Tracking feature-based attention
Journal of Neural Engineering, 2019Abstract Objective . Feature-based attention (FBA) helps one detect objects with a particular color, motion, or orientation. FBA works globally; the attended feature is enhanced at all positions in the visual field.
Veronica C Chu, Michael D’Zmura
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Visualizing features and tracking their evolution
Computer, 1994We describe basic algorithms to extract coherent amorphous regions (features or objects) from 2 and 3D scalar and vector fields and then track them in a series of consecutive time steps. We use a combination of techniques from computer vision, image processing, computer graphics, and computational geometry and apply them to data sets from computational
Ravi Samtaney +3 more
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Active face and feature tracking
Proceedings 10th International Conference on Image Analysis and Processing, 2003This paper describes a method for the detection and tracking of human face and facial features. Skin segmentation is learnt from samples of an image. After detecting a moving object, the corresponding area is searched for clusters of pixels with a known distribution.
Luis Jordao +3 more
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Image stabilization by features tracking
Proceedings 10th International Conference on Image Analysis and Processing, 2003This paper describes a technique for image stabilization in video sequences. The warping that compensates for the camera motion is computed from tracked features in the images. In order to cope with moving objects, a robust technique is used to compute homographies.
A. Censi +2 more
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Reduction of the uncertainty in feature tracking
Applied Intelligence, 2018It is difficult to establish feature correspondences between distant viewpoints for panoramic images. For reliable navigation and development a human-like capability of interaction with the surrounding environment, we need a method of reduction of the uncertainty in feature tracking.
Anna A. Gorbenko, Vladimir Yu. Popov
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Feature Tracking with Skeleton Graphs
2003A way to analyse large time-dependent data sets is by visualization of the evolution of features in these data. The process consists of four steps: feature extraction, feature tracking, event detection, and visualization.
Benjamin Vrolijk +2 more
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Perceptual tracking of edge features
Proceedings of 1st International Conference on Image Processing, 2002Presents an approach for extracting edge features directly from grey-level images. The approach is based on the principle of perceptual organization, which simulates to human visual process on edge perception in certain aspects. >
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Dynamic feature fusion with spatial-temporal context for robust object tracking
Pattern Recognition, 2022Ke Nai, Zhiyong Li, Haidong Wang
exaly