Parallel Strip Segment Recognition and Application to Metallic Tubular Object Measure
The segmentation or the geometric analysis of specular object is known as a difficult problem in the computer vision domain. It is also true for the problem of line detection where the specular reflection implies numerous false positive line detection or missing lines located on the dark parts of the object. This limitation reduces its potential use for concrete industrial applications where metallic objects are frequent. In this work, we propose to overcome this limitation by proposing a new strategy which is not based on the image gradient as usually, but exploits the image intensity profile defined inside a parallel strip primitive. Associated to a digital straight segment recognition algorithm robust to noise, we demonstrate the efficiency of our proposed method with a real industrial application.
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