Directionality Analysis - Revision history

Feralo at 12:44, 25 November 2016

2016-11-25T12:44:21Z

2016-11-25T12:44:10Z

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2015-01-07T17:56:18Z

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← Older revision		Revision as of 12:44, 25 November 2016
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−	Welcome to this lecture on '''Directionality Analysis'''.	+	Welcome to this lecture on '''Directionality Analysis'''

	(page created in November 2015, last modification November 2016)		(page created in November 2015, last modification November 2016)

← Older revision		Revision as of 12:44, 25 November 2016
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−	Welcome to this lecture on Directionality Analysis.	+	Welcome to this lecture on '''Directionality Analysis'''.

	(page created in November 2015, last modification November 2016)		(page created in November 2015, last modification November 2016)

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 Welcome to this lecture on Directionality Analysis.
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 === Teaching Material ===
-Get '''slides''' from Google docs (ppt) [https://drive.google.com/open?id=0B95UheoCtOzoQjhxWHhKYlR2WEk&authuser=0 here]
+Get '''slides''' from Google docs (ppt) [https://drive.google.com/open?id=0BzuKeaCE7_Fyb2ZOTFVTN0RRdUU here]
 [[Image:edges_corners_lines_circles.png|250px]]
@@ Line 77: / Line 79: @@
-Get '''slides''' from Google docs (ppt) [https://drive.google.com/open?id=0B95UheoCtOzoSXBGbnZjdkZBSm8&authuser=0 here]
+Get '''slides''' from Google docs (ppt) [https://drive.google.com/open?id=0BzuKeaCE7_FyWkdOZC12b2NwQWM here]
 [[Image:structure_tensor.png|250px]]

@@ Line 55: / Line 55: @@
 |'''R. Klette, “Concise Computer Vision”,  Springer, 2014'''
 * Sections 2.3.3, (edges), 2.3.4 (corners), 2.4. (edges), 3.4 (lines and circles)
-* Get [https://drive.google.com/open?id=0B95UheoCtOzobnhLXzhfRnNxUnc&authuser=0 chapter 2] and [https://drive.google.com/open?id=0B95UheoCtOzobnhLXzhfRnNxUnc&authuser=0 chapter 3]
+* Get [https://drive.google.com/open?id=0B95UheoCtOzobnhLXzhfRnNxUnc&authuser=0 chapter 2] and [https://drive.google.com/open?id=0B95UheoCtOzoTEctYlZZX3hKcUE&authuser=0 chapter 3]
 * Site of the author (full book not available, but plenty of other resources): http://ccv.wordpress.fos.auckland.ac.nz/
 |}

@@ Line 56: / Line 56: @@
 * Sections 2.3.3, (edges), 2.3.4 (corners), 2.4. (edges), 3.4 (lines and circles)
 * Get [https://drive.google.com/open?id=0B95UheoCtOzobnhLXzhfRnNxUnc&authuser=0 chapter 2] and [https://drive.google.com/open?id=0B95UheoCtOzobnhLXzhfRnNxUnc&authuser=0 chapter 3]
-* Site of the author (book not available, but plenty of other resources): http://ccv.wordpress.fos.auckland.ac.nz/
+* Site of the author (full book not available, but plenty of other resources): http://ccv.wordpress.fos.auckland.ac.nz/
 |}

@@ Line 48: / Line 48: @@
 |'''Simon J.D. Prince, “Computer Vision:  Models, Learning, and Inference”, Cambridge University Press, 2012'''
 * Sections 13.1.3 (edges), 13.2 (Canny, Harris)
-* Full book available online: http://www.computervisionmodels.com/
+* Full book available online (plus plenty of other resources): http://www.computervisionmodels.com/
 |}
@@ Line 56: / Line 56: @@
 * Sections 2.3.3, (edges), 2.3.4 (corners), 2.4. (edges), 3.4 (lines and circles)
 * Get [https://drive.google.com/open?id=0B95UheoCtOzobnhLXzhfRnNxUnc&authuser=0 chapter 2] and [https://drive.google.com/open?id=0B95UheoCtOzobnhLXzhfRnNxUnc&authuser=0 chapter 3]
 |}

@@ Line 12: / Line 12: @@
 * Part 1 dedicated to the extraction of edges, corners, lines and circles from images
 * Part 2 dedicated to the detection of symmetric patterns via the Generalized Structure Tensor
@@ Line 21: / Line 26: @@
 [[Image:Vision-reasoning-levels.JPG|none|450px]]
@@ Line 69: / Line 60: @@
 == Part 2: Structure tensor ==
-[[Image:spirals.jpg|none|700px]]
+[[Image:st-crash.jpg|none|450px]]

← Older revision		Revision as of 17:53, 7 January 2015
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−	Part 2 is dedicated to the detection of symmetric patterns. The usefulness of this procedure stems from the fact that ~~they~~ appear in many real-world scenarios. ~~Detection~~ is done using the Generalized Structure Tensor, a mathematical tool that allows simple and direct detection in Cartesian coordinates using a single complex filter built from derivatives of Gaussians.	+	Part 2 is dedicated to the detection of symmetric patterns. The usefulness of this procedure stems from the fact that these patterns appear in many real-world scenarios. Their detection is done using the Generalized Structure Tensor, a mathematical tool that allows simple and direct detection in Cartesian coordinates using a single complex filter built from derivatives of Gaussians.