MatchPoint - Revision history

Bruno: Really needs a rewrite

2008-04-24T22:03:41Z

Really needs a rewrite

Stereo sl: /* Feature detection */

2008-02-27T17:52:09Z

‎Feature detection

Stereo sl: /* Feature description */

2008-02-27T17:50:18Z

‎Feature description

Stereo sl: /* Feature description */

2008-02-27T17:49:25Z

‎Feature description

Stereo sl: /* Introduction */

2008-02-27T17:47:49Z

‎Introduction

Stereo sl at 17:35, 27 February 2008

2008-02-27T17:35:41Z

Stereo sl: /* References */

2008-02-27T17:34:50Z

‎References

Stereo sl: /* References */

2008-02-27T17:33:29Z

‎References

Stereo sl: /* Feature description */

2008-02-27T17:22:52Z

‎Feature description

Stereo sl: /* Feature description */

2008-02-27T17:18:36Z

‎Feature description

@@ Line 1: / Line 1: @@
 == Introduction ==
-MatchPoint is a next generation CP generator. The result of a GSoC2007 project [[SoC2007_project_Feature_Descriptor]]. Currently it offers only keypoint detection but not matching(matching was part of other GSoC 07 project that was not carried out) and can currently be used only as a replacement for generatekeys from autopano.
+MatchPoint is a next generation CP generator. The result of a GSoC2007 project [[SoC2007_project_Feature_Descriptor]]. Currently it offers only keypoint detection but not matching (matching was part of other GSoC 07 project that was not carried out) and can currently be used only as a replacement for generatekeys from [[autopano-sift]] or [[autopano-sift-C]].
-Goal of this MatchPoint is to create a complete control point suite that will be used by hugin as a replacement(or at least an alternative) to existing autopano suites.
+A current version of matchpoint can be obtained via the [[hugin]] project.
 If you want to use MatchPoint in the process of creating panoramic images now(when not all features are implemented) you will have to use autopano-sift also.
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 MatchPoint features also offers two ways of convolution:
 * box filter: faster and preferable way
-* sliding window(implemented for convinience but needs debug): slower, more accurate but also more sensitive to noise
+* sliding window(implemented for convenience but needs debug): slower, more accurate but also more sensitive to noise
 ''' Finding maxima '''<br />
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 Next step is to select points with high values of their determinants(regardless of scale where points where detected) that represent invariant keypoints that will be used further processing. This is achieved using a fixed threshold which should be set low(because otherwise it may happen that no points would be detected).
-Then non-maxima supression is applied(only points with local maxima of determinants are selected).
+Then non-maxima suppression is applied(only points with local maxima of determinants are selected).
-At this point we have a list of points that can vary in length, because threshold from previous step is hardcoded. This can result in over 200.000 points for larger images and that is too much(regardless of image size we need the same amount of control points for all images - min 3 control point/overlapping images), so we need to strip down the list below 10.000 points(this number is set intuitivelly and it is based on consumed time). (Note: this work is progress).
+At this point we have a list of points that can vary in length, because threshold from previous step is hardcoded. This can result in over 200.000 points for larger images and that is too much(regardless of image size we need the same amount of control points for all images - min 3 control point/overlapping images), so we need to strip down the list below 10.000 points (this number is set intuitively and it is based on consumed time). (Note: this work is progress).
 === Feature description ===
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 * [http://www.eecs.berkeley.edu/Research/Projects/CS/vision/shape/sc_digits.html Shape Context descriptors]
 [[Category:Community:Project]]

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 === Feature detection ===
-Points are detected with Hessian Detector using box filters. Here is a description of detection process over time. This may not be entirely compatible flow with the algorithm's flow, because some parts are simultaneous(e.q. first two steps).
+Points are detected with Hessian Detector using box filters. Here is a description of detection process over time. This may not be entirely compatible flow with code's flow, because some parts are simultaneous(e.q. first two steps).
 ''' Convolution of pixels '''<br />

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 Steps in creating descriptors:
-# detect edges in the region around the interest point. MatchPoint uses vigra's API(Canny edge detection) for this.
+# detect edges in the region around the interest point(region size depends on the scale at which point was detected). MatchPoint uses vigra's API(Canny edge detection) for this.
 # based on relative location of edge elements create a histogram with 36 values. Use log-polar values. Edge point contribution to the histogram is based on it's magnitude and orientation.
 See reference papers for details.

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 ''' Shape Context descriptors '''<br />
 To each interest point a 36 element descriptor is assigned. Elements of this descriptors are organized according to Shape Context descriptor proposed by [http://www.eecs.berkeley.edu/Research/Projects/CS/vision/shape/sc_digits.html S. Belongie, J. Malik and J. Puzicha] and adapted by [http://www.robots.ox.ac.uk/~vgg/research/affine/index.html K. Mikolajczyk] for the purpose of finding control points.
-Steps in creating point's descriptor:
-# detect edges in the surrounding region. MatchPoint uses vigra's API(Canny edge detection) for this.
+Steps in creating descriptors:
 # based on relative location of edge elements create a histogram with 36 values. Use log-polar values. Edge point contribution to the histogram is based on it's magnitude and orientation.
 See reference papers for details.

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 == Introduction ==
-MatchPoint is a next generation CP generator. The result of a GSoC2007 project [[SoC2007_project_Feature_Descriptor]], it is still very experimental. Read more [http://groups.google.com/group/hugin-ptx/browse_thread/thread/cba2b2ce94dd9054 here]
+MatchPoint is a next generation CP generator. The result of a GSoC2007 project [[SoC2007_project_Feature_Descriptor]]. Currently it offers only keypoint detection but not matching(matching was part of other GSoC 07 project that was not carried out) and can currently be used only as a replacement for generatekeys from autopano.
 == Command line usage ==

@@ Line 61: / Line 61: @@
 * [http://www.vision.ee.ethz.ch/~surf/ Speeded Up Robust Features - SURF]
 * [http://www.robots.ox.ac.uk/~vgg/research/affine Matlab suite for testing, papers for detection, descriptors and evaluation by K. Mikolajczyk]
+* [http://www.eecs.berkeley.edu/Research/Projects/CS/vision/shape/sc_digits.html Shape Context descriptors]
 [[Category:Community:Project]]

@@ Line 59: / Line 59: @@
 ==References==
-<references />
+* [http://www.vision.ee.ethz.ch/~surf/ Speeded Up Robust Features - SURF]
 [[Category:Community:Project]]

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 ''' Orientation assignment '''<br />
 Scale invariance is achieved by assigning main orientation of the interest point using special algorithm proposed by Herbert Bay. Efficiency of this algorithm has not been yet tested, therefore the executable of MatchPoint does not use any orientation assignment.
 ''' Shape Context descriptors '''<br />