Texture extraction of wear particles based on improved random hough transform and visual saliency

Abstract Texture recognition is very important and necessary for the identification of wear particles. In order to extract distinctive textures from the images of wear particles, a new texture extraction method based on improved random Hough transform and visual saliency of wear particles is proposed in this paper. This method mimics what human eyes see when they analyze wear particle images. Firstly, Canny detector is used to obtain the initial texture seed image. Secondly, the improved random Hough transform based on the connectivity of pixels is applied on texture seed images to extract the texture primitives, such as lines or circles, which represent visual saliency of the texture on wear particles. Finally, through the statistics of texture primitives, the regularity and randomness of texture pattern could be analyzed so as to determine the type of wear particles. Experimental results show that this method is effective to distinguish the severe sliding, fatigue wear particles and black oxides, it is an intuitive and fast method.