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A major limitation of metabolomics science is the vast amount of detected but structurally not characterized or putatively classified "features", a chromatographic peak in LC-MS analysis, an m/ z ratio or complex mass spectrum or a chemical shift in NMR analysis.
The detected braking states and the other classified features related to the motion are reflected to the screen of the Android tablet to inform the rider about the riding and motion conditions.
A new collaborative tracking approach is introduced which takes advantage of classified features.
The experimental results show successful tracking compared with the collaborative tracker that does not use the classified features.
When more than one desired moving objects bounding boxes are close enough, the collaborative tracker is activated and it exploits the advantages of the classified features to localize each object precisely as well as updating the objects shape models more precisely by assigning again the classified features to the objects.
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Then for each category or group, a prototype vector is formed by averaging all classified feature vectors in it.
Zhang et al. [13] have classified feature-based approaches into four types: 1. Local features; 2.
Also, Fig. 12 illustrates the connectivity matrix and cluster crossing curve of classified feature vectors.
Figure 6a c shows the connectivity matrix C, cluster crossing curve, and the classified feature vector for Example (1).
Fig. 6 a Connectivity matrix C, b cluster crossing curve, and (c) the classified feature vector for Example (1).
With Step 1, the processing chain then starts at the beginning where the previously classified feature vectors are broadcasted to the neighborhood.
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