A detection method on wood defects of CT image using multifractal spectrum based on fractal brownian motion

Wood has long been an important building material. The wide variety in appearance, strength properties and the possibility of different dimensions are some of the reasons why wood is still very attractive for this purpose. Wood nondestructive testing technology is a new and comprehensive subject. In recent years it has achieved fast development. Computed tomography offers great potential for non-destructive testing of the internal structure of wood. X-ray computed tomography (CT) scanning technology has been applied to the detection of internal defects in the logs for the purpose of obtaining prior information, which can be used to arrive at better log sawing decision. A method in CT image edge detection by using multifractal theory based on fractal Brownian motion is applied in the paper. The Holder exponent of image pixels, derived from fractal dimension estimation algorithm based on the fractional Brownian motion model and calculated from all the sub areas in wood image one by one, is computed, then its multifractal spectrum is estimated and different image pixels are classified, smoothing edge point and singular edge point.