
In this study, quality of class of final product is intended to automatically determine by setting properly distribution and number of defects on wood. Nowadays number of defects and quality of wood is determined by their distribution. The most common type of defect is called knot by reducing the resistance of a wooden part. Selection and classification of wood manually is difficult, costly and time-consuming process. At this point automatic defect detection methods have been proposed to affect the quality of the final product and defects are accurately automatically identified and analyzed. To this end, quality determination has been made according to TS 11790 and EN 13990 standards on databases that are containing wood images.
| selected citations These citations are derived from selected sources. This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | 4 | |
| popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network. | Top 10% | |
| influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | Average | |
| impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network. | Average |
