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@Bellaktris
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This pull request is clone of the #25

This pull request is the part of my Google Summer of Code work. Here I implemented fast edge detection using structured random forests which was recently proposed by Piotr Dollar, state-of-the-art image inpainting algorithm proposed by Kaiming He, and two simple image processing techniques: dct-based image denoising, and simple color balance.

@vpisarev vpisarev self-assigned this Aug 11, 2014
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@Bellaktris, please, fix the errors reported by our buildbot (including possible merge errors). In order to integrate your PR, it should be "all-green": http://pullrequest.opencv.org/#/summary/contrib

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@Bellaktris, this PR stays unattended for more than a month. Can you please take care of it? We need to finally integrate this PR.

@Bellaktris
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Actually I can't, I have already fixed all errors which I could reproduce or at least understand, but there is one regarding python autogenerated headers, I asked my mentor for help and he promised to look at this problem at weekend.

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excellent!

vpisarev added a commit that referenced this pull request Aug 17, 2014
Structured edge detection & State-of-the-art image inpainting
@vpisarev vpisarev merged commit 286d6ff into opencv:master Aug 17, 2014
@Bellaktris Bellaktris deleted the gsoc branch August 17, 2014 20:42
bmegli pushed a commit to Extend-Robotics/opencv_contrib that referenced this pull request Jul 11, 2022
* Add recording file playback functionality

* overwrite color_enabled and depth_enabled dependent on recording tracks

* synchronized capture and imu data in playback mode

* protect access on playback handle for getting next capture and imu sample

* Fixed minor issues from pull request review

* Skipping unsynchronized captures where necessary when using a recording
allnes pushed a commit to allnes/opencv_contrib that referenced this pull request Jun 9, 2024
* Added Conv1D transformation

* Added PRelu transformation

* Minor fixes

* Create workaround for NormalizeL2

* Fixed PReLU transformation

* Extending Interpolate op support

* Support Conv with const weights only
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3 participants