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Due to the diversity of body movements and uncertainty of recording occasion, human action recognition is still a challenging task, especially in real world. This paper provides a new method of representing the video with mid-level vision representation which is extracted from the discriminative supervoxels. In the proposed method, the discriminative supervoxels we extracted through a learning phase...
This study aims at measuring last-night sleep quality from electroencephalography (EEG). We design a sleep experiment to collect waking EEG signals from eight subjects under three different sleep conditions: 8 hours sleep, 6 hours sleep, and 4 hours sleep. We utilize three machine learning approaches, k-Nearest Neighbor (kNN), support vector machine (SVM), and discriminative graph regularized extreme...
In the paper, we present a novel approach to modeling plants from images by detecting apex features. First, an effective algorithm is proposed to extract apex features in volumetric data recovered from the images. It provides position and pose information for assigning 3D generic leaves. Then, the 3D leaf shapes are determined by an optimization based on the volume. Finally, Branches are modeled by...
We present an approach to decomposing branching volume data into sub-branches. First, a metric is proposed for evaluating local convexities in volumetric data, and it is a criterion for global selection of tip points. Second, a multi-path growing strategy is adopted to segment the volumes based on a DFS transformation starting from the tips. Experiments show that this approach is capable of generating...
We present an algorithm to automatically extract skeletons for branched volumes by shape decomposition. First, a region growing strategy is adopted based on a distance transformation to decompose a volume into several meaningful components with simple topological structures. Then, the skeleton of each component is individually extracted. Finally, the skeletons of all the components are integrated...
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