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Fabric defect identification has constituted a key part in the online quality control regarding the textile manufacturing process. Focusing on the limitations of conventional fabric defect identification methods in representing local geometry of fabric defects, a fully adaptive image processing technique called twodimensional empirical mode decomposition is introduced in this paper to extract the identification feature of fabric defect and segment the fabric defect region. The firstly decomposed intrinsic mode component is capable of maintaining fine-scale patterns of the fabric defect, while the secondly decomposed intrinsic mode component possesses the main energy of medium-scale patterns. As a result, both energy and local scale parameters along warp or weft direction are derived from either first intrinsic mode component or second intrinsic mode component to locate the defect pixels in the fabric. Simulation results have shown that the proposed method is characterized by high accuracy with fast computational speed. Keywords-2D empirical mode decomposition; intrinsic mode component; fabric defect

Fabric Defect Identification Based on 2D Empirical Mode Decomposition