Scanning image binarization algorithm for full 3D shape

1998 ◽  
Author(s):  
Bo Liu ◽  
Ling Yang ◽  
Jian Zhang
2021 ◽  
pp. 1-15
Author(s):  
Milan Ćurković ◽  
Andrijana Ćurković ◽  
Damir Vučina

Image binarization is one of the fundamental methods in image processing and it is mainly used as a preprocessing for other methods in image processing. We present an image binarization method with the primary purpose to find markers such as those used in mobile 3D scanning systems. Handling a mobile 3D scanning system often includes bad conditions such as light reflection and non-uniform illumination. As the basic part of the scanning process, the proposed binarization method successfully overcomes the above problems and does it successfully. Due to the trend of increasing image size and real-time image processing we were able to achieve the required small algorithmic complexity. The paper outlines a comparison with several other methods with a focus on objects with markers including the calibration system plane of the 3D scanning system. Although it is obvious that no binarization algorithm is best for all types of images, we also give the results of the proposed method applied to historical documents.


2016 ◽  
pp. 154-161
Author(s):  
Jakub Leszek Pach ◽  
Piotr Bilski

In this paper, we present a novel method of detecting text lines in handwritten documents based on the Block-Based Hough Transform. To maximize its efficiency, the robust binarization algorithm was applied. It is based on the Gaussian filtering and tackles the non-uniform luminance. The proposed technique consists of three steps: preprocessing, detecting of potential text lines and eliminating the false ones. The first step covers the image binarization, extraction of connected components and selection of supporting connected components based on the local maxima in the vertical histogram stripes. Secondly, the appropriate subset of connected components supplemented by one-point components is selected. Finally, the block-based Hough transform is applied to detect potential text lines and found the ones identified incorrectly. The proposed method is applied to the analysis of the fifteenth century Latin manuscripts. Our approach is more effective than the traditional ones, in the best cases by twenty percent.


2013 ◽  
Vol 718-720 ◽  
pp. 1094-1099
Author(s):  
Yuan Luo ◽  
Xue Qiang Tang

In MEMS parameter measure based on vision,the process of binarization is critical. In the situation of unbalancing illumination and noise background,the performance of traditional binarization method degrades. In this paper, a binarization method based on wavelet analysis and an optimal box counting (OBC) fractal dimension algorithm is proposed. At first,wavelet analysis is used to eliminate the effect of illumination distribution.Then the MEMS image binarization based on OBC reduces the effect of noise. Experiments show that, the method can get a considerable binarization result.


Author(s):  
C.L. Woodcock

Despite the potential of the technique, electron tomography has yet to be widely used by biologists. This is in part related to the rather daunting list of equipment and expertise that are required. Thanks to continuing advances in theory and instrumentation, tomography is now more feasible for the non-specialist. One barrier that has essentially disappeared is the expense of computational resources. In view of this progress, it is time to give more attention to practical issues that need to be considered when embarking on a tomographic project. The following recommendations and comments are derived from experience gained during two long-term collaborative projects.Tomographic reconstruction results in a three dimensional description of an individual EM specimen, most commonly a section, and is therefore applicable to problems in which ultrastructural details within the thickness of the specimen are obscured in single micrographs. Information that can be recovered using tomography includes the 3D shape of particles, and the arrangement and dispostion of overlapping fibrous and membranous structures.


2017 ◽  
Author(s):  
Ashly Senske ◽  
◽  
Claire Marvet ◽  
Sultan Akbar ◽  
Silishia Wong ◽  
...  

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