List of Articles Segmentation

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  1 - Image Processing of steel sheets for Defect Detection by using Gabor Wavelet
  masoud shafiee mostafa sadeghi
  In different steps of steel production, various defects appear on the surface of the sheet. Putting aside the causes of defects, precise identification of their kinds helps classify steel sheet correctly, thereby it allocates a high percentage of quality control process More

  In different steps of steel production, various defects appear on the surface of the sheet. Putting aside the causes of defects, precise identification of their kinds helps classify steel sheet correctly, thereby it allocates a high percentage of quality control process. QC of steel sheet for promotion of product quality and maintaining the competitive market is of great importance. In this paper, in addition to quick review of image process techniques used, using image process by means of Gabor wavelet, a fast and precise solution for detection of texture defects in steel sheet is presented. In first step, the approach extracts considerable texture specification from image by using Gabor wavelet. The specification includes both different directions and different frequencies. Then using statistical methods, images are selected that have more obvious defects, and location of defects is determined. Offering the experimental samples, the accuracy and speed of the method is indicated. Manuscript profile
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  2 - Outdoor Color Scene Segmentation towards Object Detection using Dual-Resolution Histograms
  javad rasti monadjemi monadjemi abbas vafaei
  One of the most important problems in automatic outdoor scene analysis is the approach of segmentation towards object detection. The special characteristics of such images -like color variety, different luminance effects and color shades, abundant texture details, and d More

  One of the most important problems in automatic outdoor scene analysis is the approach of segmentation towards object detection. The special characteristics of such images -like color variety, different luminance effects and color shades, abundant texture details, and diversity of objects- lead to major challenges in the segmentation process. In the previous research, we proposed a k-means clustering algorithm in a multi-resolution platform for preliminary color segmentation. In this method, the texture details are deliberately expunged and apparent clusters are gradually removed in the blurred versions of the image to let more detailed classes expose in the more clarified versions. The performance of this step-by-step approach is relatively higher than the traditional k-means in color clustering for outdoor scene segmentation. In this paper, an adaptive method based on the circular hue histogram in a dual-resolution platform is suggested to detect the apparent clusters in the blurred images. Experimental results on two outdoor datasets show about 20% decrease in the pixel segmentation error as well as around 30% increase in both precision and speed in the convergence of the clustering algorithm. Manuscript profile
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  3 - Provide a method for customer segmentation using the RFM model in conditions of uncertainty
  mohammadreza gholamian azime mozafari
  20.1001.1.27170411.1395.8.30.4.6
  The purpose of this study is to provide a method for customer segmentation of a private bank in Shiraz based on the RFM model in the face of uncertainty about customer data. In the proposed framework of this study, first, the values ​​of RFM model indicators including e More

  The purpose of this study is to provide a method for customer segmentation of a private bank in Shiraz based on the RFM model in the face of uncertainty about customer data. In the proposed framework of this study, first, the values ​​of RFM model indicators including exchange novelty (R), number of exchanges (F) and monetary value of exchange (M) were extracted from the customer database and preprocessed. Given the breadth of the data, it is not possible to determine the exact number to determine whether the customer is good or bad; Therefore, to eliminate this uncertainty, the gray number theory was used, which considers the customer's situation as a range. In this way, using a different method, the bank's customers were segmented, which according to the results, customers were divided into three main sections or clusters as good, normal and bad customers. After validating the clusters using Don and Davis Boldin indicators, customer characteristics in each sector were identified and at the end, suggestions were made to improve the customer relationship management system. Manuscript profile
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  4 - Test case Selection based on Test-Driven Development
  Zohreh Mafi mirian mirian
  Test-Driven Development (TDD) is one of the test-first software production methods in which the production of each component of the code begins with writing the test case. This method has been noticed due to many advantages, including the readable, regular and short cod More

  Test-Driven Development (TDD) is one of the test-first software production methods in which the production of each component of the code begins with writing the test case. This method has been noticed due to many advantages, including the readable, regular and short code, as well as increasing the quality, productivity and reliability, and the possibility of regression testing due to the creation of a comprehensive set of unit tests. The large number of unit test cases produced in this method is considered as a strong point in order to increase the reliability of the code, however, the repeated execution of test cases increases the duration of the regression testing in this method. The purpose of this article is to present an algorithm for selecting test cases to reduce the time of the regression test in TDD method. So far, various ideas have been proposed to select test cases and reduce the regression test time. Most of these ideas are based on programming language and software production methods. The idea presented in this article is based on the program difference method and the nature of the TDD method. In this method, meaningful semantic and structural connections are created between unit tests and code blocks, and the test case selection is done based on these relationships. Manuscript profile
• Open Access Article
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  5 - Regression Test Time Reduction in Test Driven Development
  Zohreh Mafi mirian mirian
  Test-Driven Development (TDD) is one of the test-first software production methods in which the production of each component of the code begins with writing the test case. This method has been noticed due to many advantages, including the readable, regular, and short co More

  Test-Driven Development (TDD) is one of the test-first software production methods in which the production of each component of the code begins with writing the test case. This method has been noticed due to many advantages, including the readable, regular, and short code, as well as increasing quality, productivity, and reliability. The large number of unit test cases produced in this method is considered as an advantage (increases the reliability of the code), however, the repeated execution of test cases increases the regression test time. The purpose of this article is to present an algorithm for selecting test cases to reduce the time of the regression test in the TDD method. So far, various ideas have been proposed to select test cases. Most of these ideas are based on programming language and software production methods. The idea presented in this article is based on the program difference method and the nature of the TDD method, also a tool is written as a plugin in Java Eclipse. The provided tool consists of five main components: 1) Version Manager, 2) Code Segmentation, 3) Code Change Detection (in each version compared to the previous version), 4) Semantic Connection Creation (between unit tests and code blocks), and finally 5) Test Cases Selection. Manuscript profile