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Still, given the costs of thoroughly testing a candidate detection algorithm, the time maybe warranted.
Furthermore, as machine vision systems are often limited to one or two wavelengths due to practical considerations including cost, exhaustive search algorithms based-on optimizing the output of candidate detection algorithms should be cost-effective.
Each spatiotemporal segment represents one candidate detection.
For a given candidate detection, each feature is compared to its threshold.
A preliminary experiment using only GTM for candidate detection has already been reported in [30].
Thus, it is indispensable to establish methods for specific candidate detection in vivo.
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Next, we track the candidate detections over time and join temporally coherent detections in consecutive frames.
The next step is to construct tubes from the set of candidate detections.
The trained model is applied to individual frames of wildlife video sequences to identify candidate detections.
In an unsegmented video, we find all candidate detections as described in Section 3.3.
All blobs that remain after background subtraction are treated as candidate detections.
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