Defect Detection in Textures through the Use of Entropy as a Means for Automatically Selecting the Wavelet Decomposition Level.

This paper presents a robust method for defect detection in textures, entropy-based automatic selection of the wavelet decomposition level (EADL), based on a wavelet reconstruction scheme, for detecting defects in a wide variety of structural and statistical textures. Two main features are presented. One of the new features is an original use of the normalized absolute function value (NABS) calculated from the wavelet coefficients derived at various different decomposition levels in order to identify textures where the defect can be isolated by eliminating the texture pattern in the first decomposition level. The second is the use of Shannon's entropy, calculated over detail subimages, for automatic selection of the band for image reconstruction, which, unlike other techniques, such as those based on the co-occurrence matrix or on energy calculation, provides a lower decomposition level, thus avoiding excessive degradation of the image, allowing a more accurate defect segmentation. A metric analysis of the results of the proposed method with nine different thresholding algorithms determined that selecting the appropriate thresholding method is important to achieve optimum performance in defect detection. As a consequence, several different thresholding algorithms depending on the type of texture are proposed.

Sensors systems for the automation of operations in the ship repair industry.

Hull cleaning before repainting is a key operation in the maintenance of ships. For years, a method to improve such operation has been sought by means of the robotization of techniques such as grit blasting and ultra high pressure water jetting. Despite this, it continues to be standard practice in shipyards that this process is carried out manually because the developed robotized systems are too expensive to be widely accepted by shipyards. We have chosen to apply a more conservative and realistic approach to this problem, which has resulted in the development of several solutions that have been designed with different automation and operation range degrees. These solutions are fitted with most of the elements already available in many shipyards, so the installation of additional machinery in the workplace would not be necessary. This paper describes the evolutionary development of sensor systems for the automation of the preparation process of ship hull surfaces before the painting process is performed. Such evolution has given rise to the development of new technologies for coating removal.