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The Study of Image Processing on Windows Mobile Embeded System

Author: ZhangXiaoFeng
Tutor: JinZuoDong
School: Southwest Jiaotong University
Course: Electrical system control and IT
Keywords: Hough transform Embedded Systems Windows Mobile
CLC: TP391.41
Type: Master's thesis
Year: 2010
Downloads: 75
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Abstract


Count inventory or sales process after production of the steel pipe are in industrial production , is an onerous and inefficient work . Domestic count method is a manual count . Due to the impact of the technical proficiency of workers , physical and emotional state , and many other factors , the counting accuracy of the results can not be stable . Shows , driven by market demand , by means of image processing technologies and embedded platform portability advantages , developed the counting system of steel pipe in order to reduce the labor intensity of the related employees , to become a problem worthy of study . Introduced Windows Mobile embedded systems and software environment to build . Steel pipe cross-sectional images on the handheld device camera acquisition , image preprocessing , target identification and counting . Identification and counting of the pipe cross-sectional images of the oval as the purpose of the study , and the combination of oval steel research and development of recognition counting system based on the Windows Mobile smartphone platform for embedded systems . Image preprocessing part , the completion of the removal of the image format conversion , gray , and noise , and the introduction of the edge detection process , the detection of the step portion of the gray value to the edge of the projection of the target object on the next threshold value split to create favorable conditions to reduce the complexity of the threshold segmentation threshold selection , to enhance the efficiency of the entire Windows Mobile embedded operating system . Finally, the binary image after thresholding segmentation refinement process , the goal of complete extracted from the background . In accordance with the randomized Hough transform (RHT) method to detect the oval of five random point sampling to determine the parameters of the ellipse , this will lead to invalid sampling , waste of system resources . This article in accordance with the existing two - point sampling random Hough transform algorithm flow improvement and optimization , under the premise of maintaining the recognition efficiency , the goal of the system to improve recognition speed can be obtained . This paper presents a method of combining image processing technology and embedded systems and comprehensive study of its concrete realization . The experiments show that the number of statistical process improvement in the application of this thesis has a certain degree of accuracy and value .

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CLC: > Industrial Technology > Automation technology,computer technology > Computing technology,computer technology > Computer applications > Information processing (information processing) > Pattern Recognition and devices > Image recognition device
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