A Computational Approach to Edge Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Use of gray value distribution of run lengths for texture analysis
Pattern Recognition Letters
An extension to the randomized Hough transform exploiting connectivity
Pattern Recognition Letters
Digital Image Processing
Texture information in run-length matrices
IEEE Transactions on Image Processing
Computer Vision and Classification Techniques on the Surface Finish Control in Machining Processes
ICIAR '08 Proceedings of the 5th international conference on Image Analysis and Recognition
International Journal of Computer Applications in Technology
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Tool wear dramatically affects the texture of the machined surface. Analyzing the texture of machined surfaces has been shown to be promising for tool wear monitoring. However, most methods have their limitations when applied to real environments, where the geometric features of machined surface depend on the machining operation, and where image quality is affected by illumination and other factors. Problems of non-uniform illumination and image noise can be reduced by applying image segmentation and image enhancement techniques. This paper discusses our work on statistical and structural approaches for analyzing machined surfaces and investigates the correlation between tool wear and quantities characterizing machined surfaces. The column projection method can be used for machined surfaces with highly repetitive and regular textures while the connectivity oriented fast Hough transform based method is able to characterize surfaces produced by various machining processes and conditions. Our results clearly indicate that tool condition monitoring which is defined as the ability to distinguish between a sharp, a semi-dull, or a dull tool can be successfully accomplished by analysis of statistical and structural information extracted from the machined surface.