The speckle reduction on the multidimensional television signals is an important task in measurement machine vision systems. The greatest error due to speckle is detected in triangulation optical sensors if measuring objects have a periodic structure. When measuring such objects, a random interference pattern determines speckle noise. Speckle noise can be reduced by a two-camera profile sensor. Cameras capture the multidimensional image of an object in different angles and the interference pattern on the images will also be different. This allows removing it from the image. The first feature of processing is signal superposition in conditions of distortion. This problem is solved by preprocessing. The second problem is speed processing of superposition. It is solved using pyramid transformation and optical flow estimation. The developed speckle noise reducing technique was tested on multidimensional television signals with images of drill-pipe threads. In the comparison of single-camera profile sensors the error of the shape estimation of the object decreased from 0.20 mm to 0.05 mm.
We describe iteration algorithm for multidimensional television signals superposition of optical triangulation sensor for speckle reducing, which distorts the measurements of object counter. The object of research is the drill-pipe thread. The measurements of such objects are very sensitive to the speckle. The estimation error can be about 0.20 mm and higher. But value of tolerance is about 0.05 мм. The developed method for speckle reducing includes the preliminary stage of superposition of two multidimensional signals. The location of speckle on signals is different. The different location is defined by object offset relatively the laser of optical triangulation sensor. The multidimensional television signals superposition allows estimating the location of the speckle noise on images. Removing of the image region with the speckle noise reduces the error of object contour estimation. In issue of television signals processing the one of important problem has been distortion of multidimensional signals. It connects with distortion of triangulation sensor optical elements. The method of superposition on background of optical distortions and speckle noise is described in this article.
This article has been described some features of processing multidimensional optical signals for measurement solid deforming. The object shape is measured by optical triangulation scanner. The solid deforming is defined by compare measured profile and underwear profile of aim object. The developing algorithm for profiles comparison has been shown in this research. It consists by two stages. The first stage is splitting set of measuring point’s profile on subset. The subset is certain part of object, such as straight line and circular arc. The second stage is superposition part of objects and corresponding line’s equation. The sample of superposition measured rail profile and underwear rail profile, compassion with existing method of solid deforming has been shown in conclusion.
KEYWORDS: Video surveillance, Optical signal processing, Optical components, Diagnostics, Detection and tracking algorithms, Signal processing, Video, Video processing, Image processing, Image filtering, Algorithm development
Processing of optical signals, which are received from CCD sensors of video cameras, allows to extend the functionality
of video surveillance systems. Traditional video surveillance systems are used for saving, transmitting and preprocessing
of the video content from the controlled objects. Video signal processing by analytics systems allows to get
more information about object’s location and movement, the flow of technological processes and to measure other
parameters. For example, the signal processing of video surveillance systems, installed on carriage-laboratories, are used
for getting information about certain parameters of the railways. Two algorithms for video processing, allowing
recognition of pedestrian crossings of the railways, as well as location measurement of the so-called “Anchor Marks”
used to control the mechanical stresses of continuous welded rail track are described in this article. The algorithms are
based on the principle of determining the region of interest (ROI), and then the analysis of the fragments inside this ROI.
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