scholarly journals Layer contour characterization in additive manufacturing through image binarization

2021 ◽  
Vol 1193 (1) ◽  
pp. 012067
Author(s):  
D Blanco ◽  
A Fernández ◽  
P Fernández ◽  
B J Álvarez ◽  
F Peña
Keyword(s):  
Digital Images ◽  
Median Filter ◽  
Threshold Value ◽  
Image Binarization ◽  
Actual Contour ◽  
Filter Size ◽  
Characterization Procedure ◽  
Flat Bed Scanner ◽  
Selection Of ◽  
Excellent Coverage

Abstract On-Machine Measurement adoption will be key to dimensional and geometrical improvement of additively manufactured parts. One possible approach based on OMM aims at using digital images of manufactured layers to characterize actual contour deviations with respect to their theoretical profile. This strategy would also allow for in-process corrective actions. This work describes a layer-contour characterization procedure based on binarization of digital images acquired with a flat-bed scanner. This procedure has been tested off-line to evaluate the influence of two of the parameters for image treatment, the median filter size (S f ) and the threshold value (T), on the dimensional/geometrical reliability of the contour characterization. Results showed that an appropriate selection of configuration parameters allowed to characterize the proposed test-target with excellent coverage and reasonable accuracy.

Selection of the efficient video data processing strategy based on the analysis of statistical digital images characteristics

2018 ◽  
Vol 91 (3) ◽  
pp. 107-114
Author(s):  
Mykhailo Palamar ◽  
Myroslava Yavorska ◽  
Mykhailo Strembitskyi ◽  
Volodymyr Strembitskyi
Keyword(s):  
Data Processing ◽  
Digital Images ◽  
Video Data ◽  
Processing Strategy ◽  
Efficient Video ◽  
Selection Of

scholarly journals Monotonic Functions Method and Its Application to Staging of Patients with Prostate Cancer According to Pretreatment Data

2021 ◽  
Vol 11 (9) ◽  
pp. 3836
Author(s):  
Valeri Gitis ◽  
Alexander Derendyaev ◽  
Konstantin Petrov ◽  
Eugene Yurkov ◽  
Sergey Pirogov ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Learning Algorithm ◽  
Binary Classification ◽  
Preoperative Staging ◽  
Threshold Value ◽  
Logical Function ◽  
Adequate Treatment ◽  
Monotonic Functions ◽  
Selection Of

Prostate cancer is the second most frequent malignancy (after lung cancer). Preoperative staging of PCa is the basis for the selection of adequate treatment tactics. In particular, an urgent problem is the classification of indolent and aggressive forms of PCa in patients with the initial stages of the tumor process. To solve this problem, we propose to use a new binary classification machine-learning method. The proposed method of monotonic functions uses a model in which the disease’s form is determined by the severity of the patient’s condition. It is assumed that the patient’s condition is the easier, the less the deviation of the indicators from the normal values inherent in healthy people. This assumption means that the severity (form) of the disease can be represented by monotonic functions from the values of the deviation of the patient’s indicators beyond the normal range. The method is used to solve the problem of classifying patients with indolent and aggressive forms of prostate cancer according to pretreatment data. The learning algorithm is nonparametric. At the same time, it allows an explanation of the classification results in the form of a logical function. To do this, you should indicate to the algorithm either the threshold value of the probability of successful classification of patients with an indolent form of PCa, or the threshold value of the probability of misclassification of patients with an aggressive form of PCa disease. The examples of logical rules given in the article show that they are quite simple and can be easily interpreted in terms of preoperative indicators of the form of the disease.

A Spatial Median Filter for noise removal in digital images

2008 ◽  
Author(s):  
James C. Church ◽  
Yixin Chen ◽  
Stephen V. Rice
Keyword(s):  
Digital Images ◽  
Median Filter ◽  
Noise Removal ◽  
Spatial Median

scholarly journals Sistem Pendukung Keputusan Pemilihan Mobil Bekas Menggunakan Metode Analythical Hierarchy Process

2021 ◽  
Vol 3 (2) ◽  
pp. 380-386
Author(s):  
Gushelmi Gushelmi ◽  
Dodi Guswandi
Keyword(s):  
Selection Process ◽  
Threshold Value ◽  
Internet Technology ◽  
A Value ◽  
Used Cars ◽  
Hierarchy Process ◽  
Selection Of ◽  
Comparison Data

Showroom Ragasa Motor Padang is a showroom that sells various types of used cars. The old system of selecting used cars in The Ragasa Motor Padang Showroom is that customers come directly to the address of this Showroom and the selection process is still done by manual means. With the development of internet technology today is increasing rapidly and in order to be accessible to everyone, the AHP can do a comparison of the criteria in pairs on the selection of used cars and can determine the consistency of the comparison data paired with a threshold value of < 0.1. The purpose of this research is to make it easier for customers to choose used cars quickly and accurately, as well as the application of programs used to make it easier for customers to use them. The result of this study is the SPK System that was built to be able to take the decision of the selection of used cars in the Showroom Ragasa Motor Padang with the selection of the 2nd alternative with a value of 2.55 as the best choice.

scholarly journals Mean-Based Breakpoint Selection on Circular Histogram

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Jiulun Fan ◽  
Jipeng Yang
Keyword(s):  
Selection Criteria ◽  
Color Image ◽  
Selection Criterion ◽  
Threshold Value ◽  
Simple Expression ◽  
Circular Data ◽  
Cumulative Distribution ◽  
Color Model ◽  
Hsi Color Model ◽  
Selection Of

Circular histogram represents the statistical distribution of circular data; the H component histogram of HSI color model is a typical example of the circular histogram. When using H component to segment color image, a feasible way is to transform the circular histogram into a linear histogram, and then, the mature gray image thresholding methods are used on the linear histogram to select the threshold value. Thus, the reasonable selection of the breakpoint on circular histogram to linearize the circular histogram is the key. In this paper, based on the angles mean on circular histogram and the line mean on linear histogram, a simple breakpoint selection criterion is proposed, and the suitable range of this method is analyzed. Compared with the existing breakpoint selection criteria based on Lorenz curve and cumulative distribution entropy, the proposed method has the advantages of simple expression and less calculation and does not depend on the direction of rotation.

scholarly journals Defect Detection in Striped Images Using a One-Dimensional Median Filter

2020 ◽  
Vol 10 (3) ◽  
pp. 1012
Author(s):  
Wei-Chen Lee ◽  
Pei-Ling Tai
Keyword(s):  
Image Processing ◽  
Defect Detection ◽  
Median Filter ◽  
Detection Methods ◽  
Manufacturing Processes ◽  
Image Binarization ◽  
Image Processing Algorithm ◽  
One Dimensional ◽  
Modern Manufacturing ◽  
The Difference

Defect detection is a key element of quality assurance in many modern manufacturing processes. Defect detection methods, however, often involve a great deal of time and manual work. Image processing has become widely used as a means of reducing the required detection time and effort in manufacturing. To this end, this study proposes an image-processing algorithm for detecting defects in images with striped backgrounds—defect types include scratches and stains. In order to detect defects, the proposed method first pre-processes images and rotates them to align the stripes horizontally. Then, the images are divided into two parts: blocks and intervals. For the blocks, a one-dimensional median filter is used to generate defect-free images, and the difference between the original images and the defect-free images is calculated to find defects. For the intervals, defects are identified using image binarization. Finally, the method superposes the results found in the blocks and intervals to obtain final images with all defects marked. This study evaluated the performance of the proposed algorithm using 65 synthesized images and 20 actual images. The method achieved an accuracy of 97.2% based on the correctness of the defect locations. The defects that could not be identified were those whose greyscales were very close to those of the background.

Corrosion Protection of Construction Steel

2020 ◽  
pp. 327-347
Author(s):  
Arkadeb Mukhopadhyay ◽  
Sarmila Sahoo
Keyword(s):  
Carbon Dioxide ◽  
Corrosion Resistance ◽  
Corrosion Rate ◽  
Passive Film ◽  
Surface Engineering ◽  
Protective Coatings ◽  
Threshold Value ◽  
Construction Steel ◽  
Steel Rebars ◽  
Selection Of

Reinforced concrete is one of the most versatile materials for construction. In spite of this, the performance is limited by corrosion, cracking, and spalling of the steel rebars. The steel embedded in the concrete is protected by a passive film from the corrosive attack of chlorides, carbon dioxide, and sulphates. As the concentration of chlorides, carbon dioxide, or sulphates increases above a certain threshold value at the concrete rebar interface, the passive film breaks and leads to a severe increase in the corrosion rate. Further, dynamic loading and the temperature of the surroundings also affect the durability of the reinforcements. The rebar may be protected from such a corrosion attack by the suitable selection of material, improving the concrete quality and tailoring its composition or application of protective coatings. The present chapter highlights and summarizes the different grades of steel for their high corrosion resistance. Further, surface engineering and application of corrosion resistance coatings for the prevention of corrosion of construction steel rebars has been also discussed elaborately.

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