A rapid, automated flaw segmentation method using morphological reconstruction to grade wood flooring

Region-Growing Algorithms （RGAs） are used to grade the quality of manufactured wood flooring. Traditional RGAs are hampered by prob- lems of long segmentation time and low inspection accuracy caused by neighborhood search. We used morphological reconstruction with the R com- ponent to construct a novel flaw segmentation method. We initially designed two template images for low and high thresholds, and these were used for seed optimization and inflation growth, respectively. Then the extraction of the flaw skeleton from the low threshold image was realized by applying the erosion termination rules. The seeds in the flaw skeleton were optimized by the pruning method. The geodesic inflection was applied by the high threshold template to realize rapid growth of the flaw area in the floor plate, and region filling and pruning operations were applied for margin optimization. Experi- ments were conducted on 512×512, 256×256 and 128×128 pixel sizes, re- spectively. The 256×256 pixel size proved superior in time-consumption at 0.06 s with accuracy of 100%. But with the region-growing method the same process took 0.22 s with accuracy of 70%. Compared with RGA, our pro- posed method can realize more accurate segmentation, and the speed and accuracy of segmentation can satisfy the requirements for on-line grading of wood flooring.

The Research of Contrast Enhancement Algorithm in Laser Projection Display System

High-contrast is one of the main advantages in laser projection display, and the method of DCC (Dynamic Contrast Control) is the main way to increase the contrast. Generally, image pre-processing is necessary for eliminating noise and decreasing the over-highlight. In this paper, we proposed and actualized a method by following 3 steps: Firstly, the original image was analyzed statistically to get the scope of gray-scale distribution and average gray-scale;and then the image was divided into a number of sub-images. The sub-images whose pixels are higher than a certain threshold in both number and range, are applied image segmentation by certain growth rules. The sub-images satisfied with the growth rules are marked 1, and the rests are marked 0. Secondly, the sub-images are uniting. A sub-image has 3 relations between 8 sub-images around it: 1 and 1, 1 and 0, 0 and 0. The sub-images marked 1 are uniting together, and the sub-images marked 0 are uniting together. Without affecting the visual vision, all over-highlight pixels were reduced in a certain proportion. Lastly, based on the application of DCC, the whole image signals were enlarged and the brightness of light sources were reduced, so as to achieve the desired effect in contrast enhancement.