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A large-scale image retrieval approach is also designed to examine and classify each cell of a testing image by comparing it with a massive database, e.g., half-million cells extracted from the training dataset.
Figure 14 illustrates an example of aligning a testing image with the reference image under 10 dB SNR condition.
When matching, a testing image will be represented just based on available trained bases, none of which corresponds to occluded areas.
To compare the alignment accuracy of different systems, the training images, which are used to train neural networks, and the testing images, which are used to check the alignment accuracy, are generated by the way described in Table 5. Figure 13 depicts an alignment example for a testing image on three different systems.
Similar(56)
Reconstructions from a test image were performed.
For a test image, a color constancy algorithm is selected according to the inference process and the rules previously defined.
Then, we propose a matching method of local similar structure statistical matching (LSSSM) to obtain the similarity image from a test image to the local structure feature set.
It can selectively reconstruct the background and the weld region of a test image while suppressing defective regions via sparsity reconstruction.
Upon booting the microcontroller, the code initializes communication I/O ports A through D, and fills the static RAM with vertical color bars as a test image until an actual picture is downloaded (see results).
(a) A test image showing an example from car class.
For (a) test image 1, (b) test image 2, (c) test image 3, (d) test image 4, and (e) test image 5.
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