A novel material detection method using femtosecond laser, confocal imaging and image processing enabling endpointing in fast inspection of microelectronics

2021 ◽  
pp. 114287
Author(s):  
Adrian Phoulady ◽  
Nicholas May ◽  
Hongbin Choi ◽  
Sina Shahbazmohamadi ◽  
Pouya Tavousi
Author(s):  
Chen Liu ◽  
Yude Dong ◽  
Yanli Wei ◽  
Jiangtao Wang ◽  
Hongling Li

The internal structure analysis of radial tires is of great significance to improve vehicle safety and during tire research. In order to perform the digital analysis and detection of the internal composition in radial tire cross-sections, a detection method based on digital image processing was proposed. The research was carried out as follows: (a) the distribution detection and parametric analysis of the bead wire, steel belt, and carcass in the tire section were performed by means of digital image processing, connected domain extraction, and Hough transform; (b) using the angle of location distribution and area relationship, the detection data were optimized through coordinate and quantity relationship constraints; (c) a detection system for tire cross-section components was designed using the MATLAB platform. Our experimental results showed that this method displayed a good detection performance, and important practical significance for the research and manufacture of tires.


2013 ◽  
Vol 378 ◽  
pp. 478-482
Author(s):  
Yoshihiro Mitani ◽  
Toshitaka Oki

The microbubble has been widely used and shown to be effective in various fields. Therefore, there is an importance of measuring accurately its size by image processing techniques. In this paper, we propose a detection method of microbubbles by the approach based on the Hough transform. Experimental results show only 4.49% of the average error rate of the undetected microbubbles and incorrectly detected ones. This low percentage of the error rate shows the effectiveness of the proposed method.


2011 ◽  
Vol 2 (1) ◽  
Author(s):  
Thomas Adi Purnomo Shidi ◽  
Suyoto Suyoto

Abstrak. Metode Baru Deteksi Tepi untuk Batik Indonesia. Didalam paper ini, diusulkan sebuah metode pendeteksi baru untuk motif batik. Deteksi tepi sudah sangat sering digunakan didalam pemrosesan gambar. Batik motif adalah salah satu contoh gambar yang memiliki bentuk yang unik dan menarik untuk dianalisis. Metode yang digunakan pada paper ini adalam metode canny dan prewit dan akan menghasilkan metode baru yaitu metode Thomas. Perbedaan antara metode dan hasil akan dilihat dari sisi ketepatan, qualitas hasil dan kejelasan. Contoh batik yang akan digunakan adalah motif parang, motife lereng dan udan liris. Ketiga batik tersebut memiliki pola  yang unik. Kata kunci : Canny, Prewitt, Thomas, Batik, Parang, Lereng, Udan liris. Abstract. New Edge Detection Method for Indonesian Batik. In this paper, we propose a new edge detection analysis method on batiks motif. Edge detection has been oftenly  used in computer vision and image processing. Indonesian  Batiks motif are some example of graphic picture that has unique pattern that interesting to analyse. The method that used for example on this paper are canny and prewit and produce a new method, thomas method. the different  amongs the method, the result of comparison appears on quality, accuracy and clarity. The example that we use are parang batiks motive, lereng batiks motive, and udan liris batiks motive. Three of batiks motive above are have unique pattern. Keywords: Canny, Prewitt, Thomas, Batik, Parang, Lereng, Udan liris.


2011 ◽  
Vol 346 ◽  
pp. 731-737 ◽  
Author(s):  
Jin Feng Yang ◽  
Man Hua Liu ◽  
Hui Zhao ◽  
Wei Tao

This paper presents an efficient method to detect the fastener based on the technologies of image processing and optical detection. As feature descriptor, the Direction Field of fastener image is computed for template matching. This fastener detection method can be used to determine the status of fastener on the corresponding track, i.e., whether the fastener is on the track or missing. Experimental results are presented to show that the proposed method is computation efficiency and is robust for fastener detection in complex environment.


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