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Recently, instant level labeling for supervised machine learning requires aconsiderable number of filled masks. In this paper, we propose an efficientautomatic region filling algorithm for complicated regions. Distinguishingbetween adjacent connected regions, the Main Filling Process scans through allpixels and fills all the pixels except boundary ones with either exterior orinterior label color. In this way, we succeed in classifying all the pixelsinside the region except boundary ones in the given image to form two groups: abackground group and a mask group. We then set all exterior label pixels tobackground color, and interior label pixels to mask color. With this algorithm,we are able to generate output masks precisely and efficiently even forcomplicated regions as long as boundary pixels are given. Experimental resultsshow that the proposed algorithm can generate precise masks that allow forvarious machine learning tasks such as supervised training. This algorithm caneffectively handle multiple regions, complicated `holes' and regions whoseboundaries touch the image border. By testing the algorithm on both toy andpractical images, we show that the performance of Scan-flood Fill(SCAFF) hasachieved favorable results.

Scan-flood Fill(SCAFF): an Efficient Automatic Precise Region Filling Algorithm for Complicated Regions