Skip to main content

Edge Detection

  • Chapter
  • First Online:
Digital Image Processing

Abstract

Edge detection is an important step in segmentation of the image and leads to object recognition. An edge is a line of interaction of two surfaces. Edges are detected using operators based on the first and second derivatives of the image. A high value or a zero-crossing of the response of the operators indicates an edge pixel. Derivatives are approximated by differences in digital images. Commonly used edge operators are presented with examples.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 49.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 64.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 99.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to D. Sundararajan .

Exercises

Exercises

9.1

Find the \(6\times 6\) filtered magnitude output and the edge map of x(m, n) obtained using the Sobel filters. The threshold is 1.2 times the average of the magnitude of the values of the filtered output.

*(i)

$$ x(m, n)=\left[ \begin{array}{r@{\quad }r@{\quad }r@{\quad }r@{\quad }r@{\quad }r@{\quad }r@{\quad }r} 227&{} 179&{} 45&{} 10&{} 14&{} 15&{} 14&{} 12\\ 227&{} 227&{} 177&{} 18&{} 15&{} 17&{} 15&{} 14\\ 227&{} 227&{} 225&{} 56&{} 8&{} 15&{} 18&{} 15\\ 227&{} 227&{} 227&{} 98&{} 9&{} 17&{} 18&{} 16\\ 227&{} 227&{} 227&{} 141&{} 7&{} 17&{} 18&{} 17\\ 227&{} 227&{} 227&{} 194&{} 26&{} 9&{} 14&{} 14\\ 227&{} 227&{} 227&{} 214&{} 92&{} 0&{} 11&{} 11\\ 227&{} 227&{} 227&{} 212&{} 184&{} 64&{} 5&{} 14 \end{array} \right] $$

(ii)

$$ x(m, n)=\left[ \begin{array}{r@{\quad }r@{\quad }r@{\quad }r@{\quad }r@{\quad }r@{\quad }r@{\quad }r} 168&{} 153&{} 111&{} 58&{} 0&{} 0&{} 0&{} 42\\ 159&{} 161&{} 114&{} 79&{} 9&{} 2&{} 5&{} 42\\ 134&{} 181&{} 124&{} 67&{} 86&{} 20&{} 15&{} 26\\ 117&{} 180&{} 122&{} 79&{} 74&{} 47&{} 46&{} 47\\ 152&{} 181&{} 132&{} 70&{} 36&{} 38&{} 40&{} 49\\ 156&{} 171&{} 117&{} 59&{} 73&{} 77&{} 61&{} 67\\ 174&{} 166&{} 124&{} 84&{} 69&{} 57&{} 55&{} 55\\ 172&{} 159&{} 129&{} 93&{} 84&{} 31&{} 22&{} 23 \end{array} \right] $$

(iii)

$$ x(m, n)=\left[ \begin{array}{r@{\quad }r@{\quad }r@{\quad }r@{\quad }r@{\quad }r@{\quad }r@{\quad }r} 13&{} 10&{} 13&{} 9&{} 7&{} 5&{} 4&{} 2\\ 6&{} 10&{} 12&{} 10&{} 6&{} 6&{} 2&{} 0\\ 19&{} 19&{} 12&{} 11&{} 6&{} 3&{} 2&{} 2\\ 19&{} 19&{} 18&{} 18&{} 10&{} 5&{} 2&{} 3\\ 19&{} 18&{} 16&{} 17&{} 14&{} 10&{} 7&{} 6\\ 17&{} 14&{} 15&{} 15&{} 10&{} 9&{} 8&{} 6\\ 13&{} 10&{} 11&{} 13&{} 12&{} 9&{} 8&{} 3\\ 14&{} 9&{} 9&{} 9&{} 10&{} 10&{} 8&{} 5 \end{array} \right] $$

9.2

Find the filtered output and the edge map of x(m, n) obtained using a \(5\times 5\) LoG filter with \(\sigma =1\). The threshold is 0.75 times the average of the magnitude of the values of the filtered output. Assume replication at the borders.

(i)

$$ x(m, n)=\left[ \begin{array}{r@{\quad }r@{\quad }r@{\quad }r@{\quad }r@{\quad }r@{\quad }r@{\quad }r} 25&{} 17&{} 22&{} 8&{} 118&{} 186&{} 136&{} 133\\ 15&{} 38&{} 18&{} 13&{} 152&{} 147&{} 131&{} 150\\ 33&{} 32&{} 14&{} 9&{} 115&{} 165&{} 143&{} 173\\ 34&{} 14&{} 14&{} 12&{} 21&{} 64&{} 41&{} 179\\ 15&{} 14&{} 7&{} 18&{} 106&{} 137&{} 92&{} 195\\ 15&{} 24&{} 39&{} 156&{} 188&{} 194&{} 191&{} 197\\ 12&{} 24&{} 129&{} 204&{} 204&{} 206&{} 208&{} 195\\ 0&{} 7&{} 145&{} 206&{} 206&{} 209&{} 211&{} 200 \end{array} \right] $$

*(ii)

$$ x(m, n)=\left[ \begin{array}{r@{\quad }r@{\quad }r@{\quad }r@{\quad }r@{\quad }r@{\quad }r@{\quad }r} 147&{} 163&{} 179&{} 186&{} 191&{} 194&{} 197&{} 157\\ 160&{} 175&{} 182&{} 184&{} 184&{} 186&{} 162&{} 50\\ 141&{} 163&{} 170&{} 175&{} 174&{} 133&{} 38&{} 3\\ 91&{} 127&{} 135&{} 124&{} 85&{} 16&{} 0&{} 7\\ 113&{} 126&{} 121&{} 117&{} 18&{} 0&{} 1&{} 10\\ 136&{} 135&{} 125&{} 151&{} 99&{} 54&{} 8&{} 9\\ 148&{} 150&{} 159&{} 161&{} 149&{} 106&{} 89&{} 20\\ 142&{} 164&{} 178&{} 181&{} 168&{} 113&{} 120&{} 91 \end{array} \right] $$

(iii)

$$ x(m, n)=\left[ \begin{array}{r@{\quad }r@{\quad }r@{\quad }r@{\quad }r@{\quad }r@{\quad }r@{\quad }r} 72&{} 77&{} 66&{} 62&{} 50&{} 43&{} 66&{} 66\\ 75&{} 65&{} 61&{} 65&{} 50&{} 36&{} 64&{} 64\\ 67&{} 57&{} 62&{} 67&{} 48&{} 31&{} 63&{} 63\\ 55&{} 62&{} 64&{} 62&{} 16&{} 34&{} 61&{} 61\\ 41&{} 46&{} 47&{} 46&{} 11&{} 33&{} 59&{} 62\\ 39&{} 28&{} 58&{} 54&{} 25&{} 7&{} 50&{} 61\\ 41&{} 0&{} 49&{} 48&{} 28&{} 9&{} 6&{} 29\\ 63&{} 20&{} 6&{} 23&{} 16&{} 10&{} 7&{} 15 \end{array} \right] $$

Rights and permissions

Reprints and permissions

Copyright information

© 2017 Springer Nature Singapore Pte Ltd.

About this chapter

Cite this chapter

Sundararajan, D. (2017). Edge Detection. In: Digital Image Processing. Springer, Singapore. https://doi.org/10.1007/978-981-10-6113-4_9

Download citation

  • DOI: https://doi.org/10.1007/978-981-10-6113-4_9

  • Published:

  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-10-6112-7

  • Online ISBN: 978-981-10-6113-4

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics