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The semantic inference is formulated as a template matching problem, where the semantic units parsed from voice input are the "samples" to be inspected and the semantic units in the predefined library are the feature templates.
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Its recent extensions to grey level images have proven its ability to solve various template matching problems.
For example, it does not suffer from timing and template matching problems, and it circumvents processing at high frequencies, thereby reducing the on-chip circuit complexity and power consumption.
We pose the problem as a template-matching problem that tries to find the location of the overlapping area of the images inside the other image.
Then, Förstner (1987) further evaluated precision, controllability and robustness for planning purpose in vision based measurement problems, which included template matching, absolute orientation and relative orientation.
The newly proposed methods, such as template pruning and filtering [19], template-like dimension reduction of speech observations [20], and template matching in the second-pass decoding search [21], are beginning to address this problem.
Figure 6 Template matching schematic.
The template matching algorithm iterates through the templates from largest to smallest searching for matches.
The work is divided up on iris template matching boundaries, not within a template match.
It employs RULES-3 inductive learning algorithm and template matching technique.
Here we implement microtubule tracing using 3D template matching.
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