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Of course, since the update mask M1 is based on test of brightness, color distortion, and RGB values only, it may cause some foreground values (which pass the weak detector test based on these properties) also to be inserted in the model.
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Because returning signals are so weak, detectors must be extremely sensitive, even if relatively small.
The new detectors vary with respect to their richness, starting from weak detectors that highlight only the strong edges and are basically noise free, to strong detectors that also highlight weak edges and fine details, but exhibit significant amount of noise.
The weak detectors for each image are extracted in four steps as follows.
We can generate 5 template) × 8(fragment) × 4 filter) × 2(class)=320 weak detectors for each image.
Strong detectors are knowledge-driven features that are supposed to provide strong hints, while weak detectors are randomly extracted patterns to allow the learning algorithm to consider subtle characteristics of a class.
For each cell image, we extracted 452 features that include 132 strong (e.g. morphology, texture, moment and intensity features) and 320 weak detectors, as described in Section 3.6.
Then for a given image either for training or testing, apply normalized cross-correlation between the given image and the fragment i to find where the fragment i occurs and then record the maximum correlation as the i -th component in the vector of weak detectors for the given image.
In the first stage, a weak shadow detector filters out impossible shadow values from the set of foreground pixels by imposing mild constraints on brightness, color distortion, and RGB values.
We concentrate on the case of that unwanted evolution being slow, i.e., weak qubit-detector coupling.
The appearance of bimodal resonances gives a high versatility to neurons as weak signals detectors.
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