RGBD cameras provide images of depth and 3D information of physical objects. These cameras, which are usually based on Time of Flight techniques, have become an alternative for detecting superficial defects in industrial production. Traditionally, the measurement of changes in thickness of a transparent flat glass has been carried out by means of point like techniques. Thus, for the measurement of an extended surface of the glass a scanning process is necessary. This drawback can be improved using cameras RGBD like the Kinect V2. In this article, a novel method for detecting the change in thickness of an area of interest of a glass is described. This proposal is based on the refraction of the light when it goes from air to glass, i.e., it is based on the change of direction and velocity of the light due to the change of refraction index between the media. This phenomenon is evident in the RGBD images of a known scene when a transparent flat glass is put before its background. The changes in the RGBD images of the scene with and without glass are caused by the increment in time of flight by the refraction in the glass. We obtained a correlation between the thicknesses of different glasses and the depth measurements for a controlled scene, by using an experimental setup with a Kinect V2 as RGBD camera. We processed the data using a K-means algorithm to determine the change of thickness in transparent flat glasses.
3D reconstruction of small objects is used in applications of surface analysis, forensic analysis and tissue reconstruction
in medicine. In this paper, we propose a strategy for the 3D reconstruction of small objects and the identification of some
superficial defects. We applied a technique of projection of structured light patterns, specifically sinusoidal fringes and
an algorithm of phase unwrapping. A CMOS camera was used to capture images and a DLP digital light projector for
synchronous projection of the sinusoidal pattern onto the objects. We implemented a technique based on a 2D flat pattern
as calibration process, so the intrinsic and extrinsic parameters of the camera and the DLP were defined. Experimental tests
were performed in samples of artificial teeth, coal particles, welding defects and surfaces tested with Vickers indentation.
Areas less than 5cm were studied. The objects were reconstructed in 3D with densities of about one million points per
sample. In addition, the steps of 3D description, identification of primitive, training and classification were implemented
to recognize defects, such as: holes, cracks, roughness textures and bumps. We found that pattern recognition strategies
are useful, when quality supervision of surfaces has enough quantities of points to evaluate the defective region, because
the identification of defects in small objects is a demanding activity of the visual inspection.
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