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Robinson compass mask

From Wikipedia, the free encyclopedia

In image processing, a Robinson compass mask is a type of compass mask used for edge detection. It has eight major compass orientations,[1] each will extract the edges in respect to its direction. A combined use of compass masks of different directions could detect the edges from different angles.

Technical explanation

The Robinson compass mask[2] is defined by taking a single mask and rotating it to form eight orientations:

The direction axis[3] is the line of zeros in the matrix. Robinson compass mask is similar to kirsch compass masks, but is simpler to implement. Since the matrix coefficients only contains 0, 1, 2, and are symmetrical, only the results of four masks[4] need to be calculated, the other four results are the negation of the first four results. An edge, or contour is an tiny area with neighboring distinct pixel values. The convolution of each mask with the image would create a high value output where there is a rapid change of pixel value, thus an edge point is found. All the detected edge points would line up as edges.

Example

An example of Robinson compass masks applied to the original image. Obviously, the edges in the direction of the mask is enhanced.

original_image
Robinson North Mask
Robinson NorthWest Mask
Robinson West Mask
Robinson SouthWest Mask
Robinson South Mask
Robinson SouthEast Mask
Robinson East Mask
Robinson NorthEast Mask

References

  1. ^ Babu C.R., Sreenivasa Reddy E., Prabhakara Rao B. (2015) Age Group Classification of Facial Images Using Rank Based Edge Texture Unit (RETU). In: Mandal J., Satapathy S., Kumar Sanyal M., Sarkar P., Mukhopadhyay A. (eds) Information Systems Design and Intelligent Applications. Advances in Intelligent Systems and Computing, vol 340. Springer, New Delhi
  2. ^ Dr. Borislav D Dimitrov, Dr. Vishal Goyal, Mr. Nehinbe Joshua, Mr. Vassilis Papataxiarhis "International Journal of Computer Science Issues(Volume 8, Issue1)", IJCSI PUBLICATION, Jan 2011,
  3. ^ S Edy Victor Haryanto, M. Y. Mashor, A. S. Abdul Nasir, H. Jaafar, "A fast and accurate detection of Schizont plasmodium falciparum using channel color space segmentation method", Cyber and IT Service Management (CITSM) 2017 5th International Conference on, pp. 1-4, 2017.
  4. ^ "Computer imaging: Digital image analysis and processing (Second ed.)" by Scott E Umbaugh, ISBN 978-1-4398-0206-9(2010)
This page was last edited on 14 June 2024, at 07:22
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