Localization of the inspected chip image is one of the key problems with machine vision aided surface mount devices (SMD) and other micro-electronic equipments. This paper presents a new edge-directed subpixel edge lo...Localization of the inspected chip image is one of the key problems with machine vision aided surface mount devices (SMD) and other micro-electronic equipments. This paper presents a new edge-directed subpixel edge localization method. The image is divided into two regions, edge and non-edge, using edge detection to emphasize the edge feature. Since the edges of the chip image are straight, they have straight-line characteristics locally and globally. First, the line segments of the straight edge are located to subpixel precision, according to their local straight properties, in a 3×3 neighborhood of the edge region. Second, the subpixel midpoints of the line segments are computed. Finally, the straight edge is fitted using the midpoints and the least square method, according to its global straight property in the entire edge region. In this way, the edge is located to subpixel precision. While fitting the edge, the irregular points are eliminated by the angles of the line segments to improve the precision. We can also distinguish different edges and their intersections using the angles of the line segments and distances between the edge points, then give the vectorial result of the image edge with high precision.展开更多
The main methods of the second phase quantitative analysis in current material science researches are manual recognition and extracting by using software such as Image Tool and Nano Measurer. The weaknesses such as hi...The main methods of the second phase quantitative analysis in current material science researches are manual recognition and extracting by using software such as Image Tool and Nano Measurer. The weaknesses such as high labor intensity and low accuracy statistic results exist in these methods. In order to overcome the shortcomings of the current methods, the Ω phase in A1-Cu-Mg-Ag alloy is taken as the research object and an algorithm based on the digital image processing and pattern recognition is proposed and implemented to do the A1 alloy TEM （transmission electron microscope） digital images process and recognize and extract the information of the second phase in the result image automatically. The top-hat transformation of the mathematical morphology, as well as several imaging processing technologies has been used in the proposed algorithm. Thereinto, top-hat transformation is used for elimination of asymmetric illumination and doing Multi-layer filtering to segment Ω phase in the TEM image. The testing results are satisfied, which indicate that the Ω phase with unclear boundary or small size can be recognized by using this method. The omission of these two kinds of Ω phase can be avoided or significantly reduced. More Ω phases would be recognized （growing rate minimum to 2% and maximum to 400% in samples）, accuracy of recognition and statistics results would be greatly improved by using this method. And the manual error can be eliminated. The procedure recognizing and making quantitative analysis of information in this method is automatically completed by the software. It can process one image, including recognition and quantitative analysis in 30 min, but the manual method such as using Image Tool or Nano Measurer need 2 h or more. The labor intensity is effectively reduced and the working efficiency is greatly improved.展开更多
文摘Localization of the inspected chip image is one of the key problems with machine vision aided surface mount devices (SMD) and other micro-electronic equipments. This paper presents a new edge-directed subpixel edge localization method. The image is divided into two regions, edge and non-edge, using edge detection to emphasize the edge feature. Since the edges of the chip image are straight, they have straight-line characteristics locally and globally. First, the line segments of the straight edge are located to subpixel precision, according to their local straight properties, in a 3×3 neighborhood of the edge region. Second, the subpixel midpoints of the line segments are computed. Finally, the straight edge is fitted using the midpoints and the least square method, according to its global straight property in the entire edge region. In this way, the edge is located to subpixel precision. While fitting the edge, the irregular points are eliminated by the angles of the line segments to improve the precision. We can also distinguish different edges and their intersections using the angles of the line segments and distances between the edge points, then give the vectorial result of the image edge with high precision.
基金Project(51171209)supported by the National Natural Science Foundation of China
文摘The main methods of the second phase quantitative analysis in current material science researches are manual recognition and extracting by using software such as Image Tool and Nano Measurer. The weaknesses such as high labor intensity and low accuracy statistic results exist in these methods. In order to overcome the shortcomings of the current methods, the Ω phase in A1-Cu-Mg-Ag alloy is taken as the research object and an algorithm based on the digital image processing and pattern recognition is proposed and implemented to do the A1 alloy TEM （transmission electron microscope） digital images process and recognize and extract the information of the second phase in the result image automatically. The top-hat transformation of the mathematical morphology, as well as several imaging processing technologies has been used in the proposed algorithm. Thereinto, top-hat transformation is used for elimination of asymmetric illumination and doing Multi-layer filtering to segment Ω phase in the TEM image. The testing results are satisfied, which indicate that the Ω phase with unclear boundary or small size can be recognized by using this method. The omission of these two kinds of Ω phase can be avoided or significantly reduced. More Ω phases would be recognized （growing rate minimum to 2% and maximum to 400% in samples）, accuracy of recognition and statistics results would be greatly improved by using this method. And the manual error can be eliminated. The procedure recognizing and making quantitative analysis of information in this method is automatically completed by the software. It can process one image, including recognition and quantitative analysis in 30 min, but the manual method such as using Image Tool or Nano Measurer need 2 h or more. The labor intensity is effectively reduced and the working efficiency is greatly improved.
