Fernando/SignalAnalysisPhDCourse - Revision history

Feralo at 20:20, 24 March 2020

2020-03-24T20:20:15Z

Feralo at 12:49, 25 November 2016

2016-11-25T12:49:14Z

Feralo at 12:24, 22 November 2016

2016-11-22T12:24:45Z

Feralo at 11:39, 15 November 2016

2016-11-15T11:39:17Z

Feralo at 16:40, 14 November 2016

2016-11-14T16:40:06Z

Feralo at 22:02, 11 November 2016

2016-11-11T22:02:49Z

Feralo at 22:01, 11 November 2016

2016-11-11T22:01:34Z

Feralo at 21:59, 11 November 2016

2016-11-11T21:59:16Z

Feralo at 20:38, 10 November 2016

2016-11-10T20:38:35Z

Feralo at 20:37, 10 November 2016

2016-11-10T20:37:52Z

← Older revision		Revision as of 20:20, 24 March 2020
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	Office hours: by appoinment.		Office hours: by appoinment.

−	Contact: feralo(~~AHTT~~)hh.se.	+	Contact: feralo(AT)hh.se.

@@ Line 56: / Line 56: @@
 '''Signal Analysis in 2D'''
-Directionality analysis - materials [http://islab.hh.se/mediawiki/Directionality_Analysis here]
+Directionality analysis - <span style="color:#ff0000">materials [http://islab.hh.se/mediawiki/Directionality_Analysis here]</span>
 * Structure tensor, HOGs, Gabor
 * Edges, corners
 Feature analysis
-* Segmentation, clustering
+* Segmentation, clustering <span style="color:#ff0000">materials [http://islab.hh.se/mediawiki/Segmentation here]</span>
-* Feature extraction, pattern matching and classification
+* Feature extraction, pattern matching and classification <span style="color:#ff0000">materials [http://islab.hh.se/mediawiki/Feature_Extraction_And_Matching here]</span>

@@ Line 56: / Line 56: @@
 '''Signal Analysis in 2D'''
-Directionality analysis
+Directionality analysis - materials [http://islab.hh.se/mediawiki/Directionality_Analysis here]
 * Structure tensor, HOGs, Gabor
 * Edges, corners

@@ Line 51: / Line 51: @@
 Discrete-time systems
 *Time-domain analysis: LTI systems, properties, convolution - <span style="color:#ff0000">slides [https://drive.google.com/open?id=0BzuKeaCE7_FyYmQ1NGIwMHcxMlE here] (Google docs)</span>
-*Frequency-domain analysis: DFT, other orthogonal basis/wavelets, filters - <span style="color:#ff0000">slides [https://drive.google.com/open?id=0BzuKeaCE7_FyX1c1TVd3ZWVFcU0 here] (Google docs)</span>
+*Frequency-domain analysis: DFT, other orthogonal basis/wavelets, filters - <span style="color:#ff0000">slides [https://drive.google.com/open?id=0BzuKeaCE7_FyX1c1TVd3ZWVFcU0 here] and notes about sampling [https://drive.google.com/open?id=0BzuKeaCE7_FyVURIQkZJYUhrSTA here] (Google docs)</span>

@@ Line 51: / Line 51: @@
 Discrete-time systems
 *Time-domain analysis: LTI systems, properties, convolution - <span style="color:#ff0000">slides [https://drive.google.com/open?id=0BzuKeaCE7_FyYmQ1NGIwMHcxMlE here] (Google docs)</span>
-*Frequency-domain analysis: DFT, other orthogonal basis/wavelets, filters - <span style="color:#ff0000">slides available soon</span>
+*Frequency-domain analysis: DFT, other orthogonal basis/wavelets, filters - <span style="color:#ff0000">slides [https://drive.google.com/open?id=0BzuKeaCE7_FyX1c1TVd3ZWVFcU0 here] (Google docs)</span>

@@ Line 40: / Line 40: @@
 ===Course content===
-'''<span style="color:#ff0000">Introductory slides [https://drive.google.com/open?id=0BzuKeaCE7_Fyb1puei1IY18yWG8 here] (Google docs)</span>'''
+<span style="color:#ff0000">Introductory slides [https://drive.google.com/open?id=0BzuKeaCE7_Fyb1puei1IY18yWG8 here] (Google docs)</span>
 '''Signal Analysis in 1D'''
-Discrete-time signals - '''<span style="color:#ff0000">slides [https://drive.google.com/open?id=0BzuKeaCE7_FycWpuYkF6a1Q3WjA here] (Google docs)</span>'''
+Discrete-time signals - <span style="color:#ff0000">slides [https://drive.google.com/open?id=0BzuKeaCE7_FycWpuYkF6a1Q3WjA here] (Google docs)</span>
 * Types and properties of signals
 * Sampling of signals
 Discrete-time systems
-*Time-domain analysis: LTI systems, properties, convolution - '''<span style="color:#ff0000">slides [https://drive.google.com/open?id=0BzuKeaCE7_FyYmQ1NGIwMHcxMlE here] (Google docs)</span>'''
+*Time-domain analysis: LTI systems, properties, convolution - <span style="color:#ff0000">slides [https://drive.google.com/open?id=0BzuKeaCE7_FyYmQ1NGIwMHcxMlE here] (Google docs)</span>
-*Frequency-domain analysis: DFT, other orthogonal basis/wavelets, filters - '''<span style="color:#ff0000">slides available soon</span>'''
+*Frequency-domain analysis: DFT, other orthogonal basis/wavelets, filters - <span style="color:#ff0000">slides available soon</span>

@@ Line 93: / Line 93: @@
 * Richard Szeliski, “Computer Vision: Algorithms and Applications”, Springer, 2011: available online (http://szeliski.org/Book/), including slide sets from other courses. As you will notice from the Table of Contents of the book, research in this area is HUGE - we will cover only a small part. The format of this book is "to-cover-it-all", not leaving any available reference out, so it is more like a literature review (thus its length).
-* J. Bigun, Vision with Direction, Springer, 2006. It presents the frequently used techniques to analyze images in a common framework – directional image processing.
+* J. Bigun, Vision with Direction, Springer, 2006. It presents the frequently used techniques to analyze images in a common framework – directional image processing, covering tools that are widely used in our lab.
 * R. Klette, “Concise Computer Vision”, Springer, 2014. Not available online, but the author maintains a webpage with slides and source code: http://ccv.wordpress.fos.auckland.ac.nz. Rather than "covering-it-all", this book covers essential selected topics at intro-level, but without sacrificing insight and detailed view.
 * Simon J.D. Prince, “Computer Vision: Models, Learning, and Inference”, Cambridge University Press, 2012. available online (http://www.computervisionmodels.com/) with plenty of other resources. This book covers the field under the common framework of probabilistic models, so it can be more difficult to digest without the proper background.