- Conference date: 2–4 February 2010
- Location: Gold Coast (Australia)
Computer vision and digital image processing comprises varieties of applications, where some of these used in image processing include convolution, edge detection as well as contrast enhancement. This paper analyzes execution time optimization analysis between Sobel and Canny image processing algorithms in terms of moving objects edge detection. Sobel and Canny edge detection algorithms have been described with pseudo code and detailed flow chart and implemented in C and MATLAB respectively on different platforms to evaluate performance and execution time for moving cars. It is shown that Sobel algorithm is very effective in case of moving multiple cars and blurs images with efficient execution time. Moreover, convolution operation of Canny takes 94–95% time of total execution time with thin and smooth but redundant edges. This also makes more robust of Sobel to detect moving cars edges.
- Edge detection
- Image analysis
- Image processing
- Contrast sensitivity
- Digital image processing
Daniel Baumann, Mark G. Jackson, Peter Adshead, Alexandre Amblard, Amjad Ashoorioon, Nicola Bartolo, Rachel Bean, Maria Beltrán, Francesco de Bernardis, Simeon Bird, Xingang Chen, Daniel J. H. Chung, Loris Colombo, Asantha Cooray, Paolo Creminelli, Scott Dodelson, Joanna Dunkley, Cora Dvorkin, Richard Easther, Fabio Finelli, Raphael Flauger, Mark P. Hertzberg, Katherine Jones‐Smith, Shamit Kachru, Kenji Kadota, Justin Khoury, William H. Kinney, Eiichiro Komatsu, Lawrence M. Krauss, Julien Lesgourgues, Andrew Liddle, Michele Liguori, Eugene Lim, Andrei Linde, Sabino Matarrese, Harsh Mathur, Liam McAllister, Alessandro Melchiorri, Alberto Nicolis, Luca Pagano, Hiranya V. Peiris, Marco Peloso, Levon Pogosian, Elena Pierpaoli, Antonio Riotto, Uroš Seljak, Leonardo Senatore, Sarah Shandera, Eva Silverstein, Tristan Smith, Pascal Vaudrevange, Licia Verde, Ben Wandelt, David Wands, Scott Watson, Mark Wyman, Amit Yadav, Wessel Valkenburg and Matias Zaldarriaga
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