A Multi-Level Colour Thresholding Based Segmentation Approach for Improved Identification of the Defective Region in Leather Surfaces

Vision systems are widely adopted for defect detection in leather surface to overcome difficulties of labour intensive, time consuming manual inspection process. Suitable image processing techniques need to be developed for accurate detection of leather defects. Existing gray scale based image processing techniques require conversion of colour images using a single threshold value and it also lacks sensitivity for detecting the leather defects due to the random and texture surface of the leather. This work presents a colour image processing approach for improved identification of leather defects using a multi-level thresholding function. In this work, the leather images are processed in ‘Lab’ colour domain for improving the human perception of discriminating the leather defects. In the present work, the specific range of values for the colour attributes is identified using the colour histogram to detect the different leather defects. MATLAB software routine is developed for identifying defects in specific ranges of colour attributes and the results are presented. From the results, it is found that proposed method provides a simpler approach for identifying the defective regions based on the colour attributes of the surface with improved human perception. The proposed methodology can be implemented in graphical processing units for efficiently detecting several types of defects using specific thresholds for the automated real-time inspection of leather defects.