Inspection of tire tread defects using image processing and pattern recognition techniques

1993 ◽  
Author(s):  
Penny Chen ◽  
Gary D. Shubinsky ◽  
Kwan-Hwa Jan ◽  
Chien-An Chen ◽  
Oliver Sidla ◽  
...  
Keyword(s):  
Image Processing ◽  
Pattern Recognition ◽  
Pattern Recognition Techniques

Automatic Detection of the Cracks on the Concrete Railway Sleepers

2019 ◽  
Vol 33 (09) ◽  
pp. 1955010
Author(s):  
Seyed Amir Hossein Tabatabaei ◽  
Ahmad Delforouzi ◽  
Muhammad Hassan Khan ◽  
Tim Wesener ◽  
Marcin Grzegorzek
Keyword(s):  
Image Processing ◽  
Pattern Recognition ◽  
Intelligent Systems ◽  
Crack Detection ◽  
Image Data ◽  
Experimental Comparison ◽  
Pattern Recognition Techniques ◽  
Comparison Results ◽  
Recognition Systems ◽  
Computer Recognition

A vision-based method for detecting the cracks in the concrete sleepers of the railway tracks will be introduced in this paper. The method is able to detect and partially classify the cracks of the concrete sleepers in two successive steps based on the image processing and pattern recognition techniques. The method has been implemented on the acquired image data frames followed by the analysis, experimental, comparison results and evaluation. The presented results are reasonable which indicates the goodness of the introduced method. The preliminary results of this work have been presented in [A. Delforouzi, A. H. Tabatabaei, M. H. Khan and M. Grzegorzek, A vision-based method for automatic crack detection in railway sleepers, in Kurzynski, M., Wozniak, M., Burduk, R. (eds.), Proceedings of the 10th International Conference on Computer Recognition Systems CORES 2017, Polanica Zdroj, Poland. CORES 2017. Advances in Intelligent Systems and Computing, Vol. 578 (Springer, Cham, 2018), pp. 130–139, doi: 10.1007/978-3-319-59162-9_14].

Pattern-recognition image processing of 3D atom-probe data

1994 ◽  
Vol 52 ◽  
pp. 832-833
Author(s):  
Patrick P. Camus ◽  
David J. Larson ◽  
Thomas F. Kelly
Keyword(s):  
Image Processing ◽  
Pattern Recognition ◽  
Lattice Structure ◽  
A Priori ◽  
Three Dimensional ◽  
Empirical Work ◽  
Atom Probe ◽  
Theoretical Description ◽  
Pattern Recognition Techniques ◽  
Near Future

The ultimate three-dimensional atom probe (3DAP) system would have sufficient spatial resolution so that the crystal structure of a material could be determined directly from the atomic positions. Aberrations in the trajectories of ions evaporated from the specimen are the primary limitation on the lateral resolution of AP analysis. In the near future, it does not seem likely that these aberrationsmay be corrected physically because there is no theoretical description and there has been very little empirical work. If the lattice is known a priori, a suggestion was proposed to force the atoms to their nearest lattice sites. This work reports progress that has been made using Fourier transform (FT) and pattern recognition techniques to reconstruct an original lattice structure from simulated 3DAPdata and subsequently to force atoms to pick their nearest lattice point. Usually FT techniques areused in image processing to reduce the image noise, not actually to shift features in the image.

Digital image processing and pattern recognition techniques for the detection of cancer

2009 ◽  
Vol 42 (6) ◽  
pp. 1015-1016 ◽  
Author(s):  
Jinshan Tang ◽  
Raj Rangayyan ◽  
Jianhua Yao ◽  
Yongyi Yang
Keyword(s):  
Image Processing ◽  
Pattern Recognition ◽  
Digital Image Processing ◽  
Digital Image ◽  
Pattern Recognition Techniques

scholarly journals Image processing for automated erythrocyte classification.

1976 ◽  
Vol 24 (1) ◽  
pp. 195-201 ◽  
Author(s):  
J W Bacus ◽  
M G Belanger ◽  
R K Aggarwal ◽  
F E Trobaugh
Keyword(s):  
Image Processing ◽  
Pattern Recognition ◽  
Digital Image Processing ◽  
Red Cells ◽  
Target Cells ◽  
Gray Level ◽  
Red Cell ◽  
Pattern Recognition Techniques ◽  
Sickle Cells ◽  
Central Gray

Digital image processing and pattern recognition techniques were applied to determine the feasibility of a natural n-space subgrouping of normal and abnormal peripheral blood erythrocytes into well separated categories. The data consisted of 325 digitized red cells from 11 different cell classes. The analysis resulted in five features: (a) size, (b) roundness, (c) spicularity, (d) eccentricity and (e) central gray level distribution. These features separated the data into six distinct condensed subgroups of red cells. Each subgroup consisted of morphologically similar cells: (a) macrocytes, (b) normocytes, (c) schistocytes, acanthocytes and burr cells, (d) microcytes and spherocytes, (e) elliptocytes, sickle cells and pencil forms and (f) target cells. The concept of a quantitative "red cell differential" was introduced, utilizing these subgroup definitions to establish subpopulations of red cells, with quantifiable indices for the diagnosis of anemia, at the specimen level.

scholarly journals Computer analysis of cervical cells. Automatic feature extraction and classification.

1978 ◽  
Vol 26 (11) ◽  
pp. 1000-1017 ◽  
Author(s):  
J Holmquist ◽  
E Bengtsson ◽  
O Eriksson ◽  
B Nordin ◽  
B Stenkvist
Keyword(s):  
Image Processing ◽  
Pattern Recognition ◽  
Feature Extraction ◽  
Computer Analysis ◽  
Single Cells ◽  
Pattern Recognition Techniques ◽  
Automatic Feature Extraction ◽  
Computerized Image Processing ◽  
Atypical Cells ◽  
Cervical Cells

A prescreening instrument for cervical smears using computerized image processing and pattern recognition techniques requires that single cells in the specimen can be automatically isolated and analyzed. This paper describes a dual wavelength method for automatic isolation of the cytoplasm and nuclei of cells. Density-oriented, shape-oriented and texture-oriented parameters were calculated and evaluated for more than 600 cells. It is shown that the computer can be taught to distinguish between normal and atypical cells with an accuracy of ca. 97%, while human classification reproducibility is ca. 95%. In addition, an attempt to assign a measure of atypia to individual cells is described.

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