Three stepped calibration of structured light system with adaptive thresholding for 3D measurements

Sert, Eser; Taşkın, Deniz; Alkan, Ahmet

doi:10.1016/j.ijleo.2015.09.230

Cited by 4 publications

(2 citation statements)

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“…At the same time, image processing algorithms can also be applied on the obtained images. Because of these advantages, 3D imaging technologies are commonly used in the defense industry, medicine, education, machinery and many other fields [1] The notion of the Neutrosophic Set (NS) is based on the neutrosophy theory -which is a new branch of Philosophyand deals with the scope, origin, and nature of the neutralities [2] . Successful results can be obtained by using NS in image processing applications such as thresholding and segmentation.…”

Section: Introductionmentioning

confidence: 99%

“…A 3D model of the object is obtained by combining depth map and the original image of the object to be modeled. In this study, depth map images of 20 boxes used for experimental purposes were obtained via Structured Light (SL) system [1] designed by us. Experimental studies demonstrate that factors such as 3D modeling technology, ambient lighting and reflection can distort depth map due, which leads to problems in 3D modeling.…”

Section: Introductionmentioning

confidence: 99%

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A new modified neutrosophic set segmentation approach

Sert

2018

Computers & Electrical Engineering

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Segmentation is paramount to 3D video systems employing multi-view video-plus-depth data (MVD) to implement free-viewpoint navigation and comfortable 3D viewing, modeling, and comprehension. The Neutrosophic Set (NS) concept relies on the neutrosophy theory dealing with structures, and it focuses on the origin, scope, and nature of neutralities. NS used in this study is norm-entropy-based, and it is called Modified Neutrosophic Set Segmentation (MNSS). 3D modeling method via MNSS (3DMM_MNSS) proposed in this study improves the quality of single object 3D modeling through an NS stratagem called MNSS. 3DMM_MNSS is compared with 3D modeling method via OATS (3DMM_OATS) and with the 3D modeling method via FCMS (3DMM_FCMS). MNSS improves the quality of 3D modeling and prevents the problems arising from the depth map imprecision in terms of Figure of Merit (FOM), the Peak Signal-to-Noise Ratio (PSNR), and the Uniformity Measure (UM) as performance metrics.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%