Edge detection image using ARM microcontroller
Main Article Content
Abstract
In this document is described as an image edge detector was developed, this technique is useful in a wide range of applications such as metrology, face recognition, pattern detection because it determines the border between two regions with different characteristics. the development board STM32FI-DISC was used, although this microcontroller is not designed for digital image processing (PDI) as microcontrollers Texas Instruments, Analog Device, etc. has a speed high processing and uses 32-bit program. The edge detector module uses the ARM microcontroller STmicroelectronics adding a camera to take pictures and is presented on a display thin film transistor (TFT) image where the edge detector was applied using the Sobel filter to determine movement object by a difference images. In addition, the detector implemented in Matlab filter edges and the images analyzed with the Matlab software and obtained the development board finding its likeness, being the images with a high degree of similarity buildings, geometric figures and printed circuits compared. Thus it was possible to apply an edge detection algorithm on this hardware at low cost.
Keywords
ARM, edge detection, digital image processing, Sobel Filter ARM, detector de bordes, Filtro de Sobel. procesamiento digital de imágenes.
References
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[5] STMicroelectronics, «STMicroelectronics,» [En línea]. Available: http://www2.st.com/content/ccc/resource/technical/document/datasheet/03/b4/b2/36/4c/72/49/29/DM00071990.pdf/files/DM00071990.pdf/jcr:content/translations/en.DM00071990.pdf. [Último acceso: 23 Abril 2016].
[6] S. Pratibha, M. Diwakar y C. Sangam, «Application of Edge Detection for Brain,» International Journal of Computer Applications, vol. 58, nº 16, pp. 21-25, 2012.
[7] M. Anupam y K. Debaditya, «Motion analysis in video surveillance using edge detection,» IOSR Journal of Computer Engineering, vol. 12, nº 6, pp. 10-15, 2013.
[8] Y. Ramadevi, T. Sridevi, B. Poornima y B. Kalyani, «Segmentation and Object Recognition using edge detection techniques,» International Journal of Computer Science & Information Technology(IJCSIT), vol. 2, nº 6, pp. 153-161, 2010.
[9] U. Sefercik y O. Gulegen, «Edge Detection in Geologic Formation Extraction: Close Range and Remote Sensing Case Studies,» 2012.
[10] N. Singh, D. Gandhi y K. P. Singh, « Recognition System Using a Canny Edge Detection and a Circular Hough Transform,» International Journal of Advances in Engineering & Technology, vol. 1, nº 2, pp. 221-228, 2011.
[11] Omnivision, «datasheet OV7670,» [En línea]. Available: http://www.voti.nl/docs/OV7670.pdf. [Último acceso: 23 abril 2016].
[12] R. Green y M. Zechner, Beginning Android Games, New York: Apress, 2012, pp. 89-91.
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[14] Wide, «2.4” QVGA (320x240) TFT-LCD,» [En línea]. Available: http://www.wide.hk/pdf/240320.pdf. [Último acceso: 23 abril 2016].
[15] S. Labs, «CP2102,» [En línea]. Available: https://www.silabs.com/Support%20Documents/TechnicalDocs/CP2102-9.pdf. [Último acceso: 23 Abril 2016].
[16] C. Solomon y T. Breckon, Fundamentals of Digital Image Processing, Oxford: Wiley-Blackwell, 2011, pp. 97-103.
[17] L. Shapiro y G. Stockman, Computer Vision, New Jersey: Prentice-Hall, 2001, pp. 156-166.
[18] S. Jayaraman, Digital Imagen Processing, New Delhi: Tata McGraw Hill, 2009, pp. 127-132.
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Published
Oct 31, 2016
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Section
Scientific Paper