Fabric seam detection based on wavelet transform and CIELAB color space: A comparison

Abstract In calendering process, the fabric undergoes luster treatment to make the surface uniformly glossy at a high operation speed. It is easy to be misjudged for the seam detection by human vision in the process, especially for the seam detection of light fabric. To improve quality products and revenues, effective and fast feature extraction algorithms are urgently needed to solve the problem. This paper presents a new wavelet energy measure method and also compares it with a novel scheme for automated textural feature extraction implementation in CIELAB color space. In wavelet-based approach, energy measures are extracted in the best number decomposition. In the CIELAB color space, we proposed to calculate characteristic parameters included mean, standard deviation and variation coefficient (CV). Then the feature data obtained from the two approaches are analyzed to determine optimized threshold values in order to recognize seam information. Compared the detection results of the two algorithms, the characteristic parameters extraction in the CIELAB color space shows higher accuracies and more computationally efficient than wavelet-based approach on detecting fabric seam in calendering process.