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Figure 5a shows the original motion image and Figure 5b shows the result of the noise removal methods of the spatial filtering and the morphological processing on our experimental motion image.
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The final step in the classification consists in morphological processing of the binarized image.
The present study investigated the morphological processing of novel compound words in overt speech production.
The special issue ends with two papers on morphological processing for segmentation and compression.
We also use morphological processing for noise reduction.
We use a spatial filtering and a morphological processing for noise reduction.
It combines intensity thresholding with morphological processing to detect and segment nodule candidates simultaneously.
According to the experimental results, it is indicated that the morphological processing based on the optimization of stroke coding can be applied to a real-time detection system.
Since both types of derived words have the same surface structure, our findings suggest that morphological processing is based on underlying morphological complexity, independent of overt affixation.
In this paper, a morphological processing method based on stroke coding is used to eliminate the burr and the growth line break (as shown in Fig. 6) and to avoid the processing of a large amount of redundant information.
After the optimization of the image processing, the broken line and the edge burr of the rail defects are eliminated completely, and a smooth and connected track defect region is obtained, as shown in Fig. 7. Fig. 7 Morphological processing results based on stroke coding.
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