A method of image analysis is proposed for detection of local defects in materials with periodic regular texture. A general improvement and enlargement of vision system capabilities for versatility, full automatism, computational efficiency, and robustness in their application to the industrial inspection of periodic textured materials is pursued. In the proposed method, a multiscale and multiorientation Gabor filter scheme that imitates the early human vision process is applied to the sample under inspection. The designed algorithm automatically segments defects from the regular texture. A variety of examples of fabric inspection are presented. In all of them defects are successfully segmented from the texture background.

In this article we present a technique based on self-mixing interferometry as a method for the acquisition and reconstruction of the arterial pulse wave. A modification, of the classic fringe counting reconstruction algorithm is proposed, to deal with some of the problems caused by biological tissue surface roughness, therefore allowing a reconstruction of the arterial displacement with a resolution of 400nm. The traits of the arterial pulse wave have been retrieved with high detail, allowing their interpretation by a skilled practitioner. The heart beat measurements show a good agreement when compared to the readings of a commercial pulse-meter, therefore proving the versatility and the viability of the technique for the measurement of other cardiovascular signals.

Roughness of paper surface is an important parameter in paper manufacturing. Surface roughness measurement is one of the central measurement problems in paper industry. Surfaces are often coated and the amount of coating and method of application used depends on the roughness of the base paper [1], [2]. At the moment, air leak methods are standardized and employed in paper industry as roughness rating methods. Air leak rate between measured paper surface and a specified flat land is recorded by using specialized pneumatic devices under laboratory conditions. Such a measurement closely corresponds to the roughness of a surface, the greater the air leak the rougher the surface. Air leak methods are rather easy to apply to paper and give stable results, although they measure roughness indirectly, need laboratory conditions, and thus unsuitable for on-line use. To measure real topography of paper surface, it is scanned with mechanical or optical profilometers. These methods provide accurate information on surface topography, but also demand laboratory conditions. In our work, present a method of measure based in the analysis of the texture of speckle pattern on the surface. The image formed by speckle in the paper surface is considered as a texture, and therefore texture analysis methods are suitable for the characterization of paper surface. The results are contrasted to air leak methods, optical profilometers (confocal microscopy), and fringe projection.

Abstract. We present a method of measure of the roughness of the paper based on the analysis of a speckle pattern on the surface. Images of speckle over the surface of paper are captured by means of a simple configuration using a laser, beam expander, and a camera chargecoupled device (CCD). Then we use the normalized covariance function of the fields, leaving the surface to find the roughness. We compare the results obtained with the results obtained with a confocal microscope and the Bendtsen method that is a standard of the paper industry. This method can be considered as a noncontact surface profiling method that can be used online. C 2011 Society of Photo-Optical Instrumentation Engineers (SPIE).

Roughness of a paper surface is particularly important in paper and board destined to be printed. Surfaces are often coated and the amount of coating and method of application used depends on the roughness of the base paper. We present a method of measure of the roughness of the paper based in the analysis of speckle pattern on the surface. Images are captured by means of a simple configuration using a laser and a camera CCD. Then, we apply digital image processing using the co-occurrence matrix, so this method can be considered as a non-contact surface profiling method, that can be used online.