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!Removing Camera Shake from a Single Image. Code for SIGGRAPH 2006 paper.
Thepackage includes the Matlab source code used in the paper, a comprehensive [[http://cs.nyu.edu/~fergus/research/deblur/README|README]] file and sample images. Please fill in the form [[http://cs.nyu.edu/~fergus/deblur_code_form.html|here]] and I will email you the code.
The
to:
!Removing Camera Shake from a Single Image.
[[http://cs.nyu.edu/~fergus/code/deblur_code_1_2.zip|Code for SIGGRAPH 2006 paper.]]
[[http://cs.nyu.edu/~fergus/code/deblur_code_1_2.zip|Code for SIGGRAPH 2006 paper.]]
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[[https://cs.nyu.edu/~silberman/datasets/nyu_depth_v2.html|Papers and Dataset download]]
to:
[[https://cs.nyu.edu/~silberman/datasets/nyu_depth_v2.html|Paper and Dataset download]]
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!! CVPR 2012 Tutorial
Speakers
Ruslan Salakhutdinov (Toronto), Graham Taylor (Guelph), Kai Yu (Baidu)
[[http://cs.nyu.edu/~fergus/tutorials/deep_learning_cvpr12/ | Course Webpage
to:
! NYU Depth Dataset v2
[[https://cs.nyu.edu/~silberman/datasets/nyu_depth_v2.html|Papers and Dataset download]]
[[https://cs.nyu.edu/~silberman/datasets/nyu_depth_v2.html|Papers and Dataset download]]
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!Fast 2D image convolution MEX file for Matlab under Linux.
Wrapper for Intel's Integrated Performance Primitives (IPP) libraries, also using OpenMP multi-threading. Requires 64-bit Intel CPU, up to date IPP libraries and Intel Compiler. Runs 5x-25x faster than Matlab's conv2 (or fft2 if kernel is large), check out the [[http://cs.nyu.edu/~fergus/code/ipp_timings.txt|timings]]. [[http://cs.nyu.edu/~fergus/code/ipp_mt_conv2.cpp|MEX file source]], [[http://cs.nyu.edu/~fergus/code/mexopts.sh|mexopts.sh MEX configuration]], [[http://cs.nyu.edu/~fergus/code/conv_test.m|Matlab test script]].
----
!Learning Object Categories from Google's Image Search. Datasets from ICCV 2005 paper.
The .tgz file below contains the images of the seven categories collected from Google which were used in our ICCV'05 paper, "Learning Object Categories from Google's Image Search".
[[http://cs.nyu.edu/~fergus/research/fergus_iccv05_google.tgz|Google images and labels from ICCV'05 paper]]
The 7 classes are: Airplane, Cars Rear, Face, Guitar, Leopard, Motorbike, Wrist Watch. In each subdirectory, the labels are contained within a Matlab file, Ground_Truth.mat. This has a tri-state label for each image. 0 = Junk, 1 = Intermediate and 2 = Good Example. The labeling was performed by an individual who has no knowledge of the algorithm.
[[http://cs.nyu.edu/~fergus/research/fergus_iccv05_google_validation.tgz|Google validation set from ICCV'05 paper]]
[[http://cs.nyu.edu/~fergus/research/iccv05_frame_ind.tgz|Train/test frame indices from ICCV'05 paper]]
[[http://cs.nyu.edu/~fergus/research/fergus_iccv05_watchleopardguitar.zip|Caltech test data: Guitars, Leopards, Wrist Watches used for testing in ICCV'05 Google paper]]
----
!Constellation Model. Code for CVPR'03 and CVPR'05 papers.
I have code available for distribution for both the CVPR'03 paper "Object Class Recognition by Unsupervised Scale-Invariant Learning" and also the CVPR'05 paper "A Sparse Object Category Model for Efficient Learning and Exhaustive Recognition". The CVPR'03 code is Linux only. The CVPR'05 code works under both Windows and Linux and uses the [[http://vxl.sourceforge.net/|VXL]] libraries. Please email me if you would like the code.
----
!Oxford Robotics Reading Group on EM
Here are some materials from the reading group I gave on EM in Oxford on 24/05/05.
[[http://cs.nyu.edu/~fergus/research/tutorial_em.ppt|Powerpoint slides]]. Demo [[http://cs.nyu.edu/~fergus/research/tutorial_em_matlabcode.zip|Matlab code]] for 2-D Gaussian mixtures
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! Deep Learning Methods for Vision
!! CVPR 2012 Tutorial
Speakers: Rob Fergus (NYU), Honglak Lee (Michigan), Marc'Aurelio Ranzato (Google)
Ruslan Salakhutdinov (Toronto), Graham Taylor (Guelph), Kai Yu (Baidu)
[[http://cs.nyu.edu/~fergus/tutorials/deep_learning_cvpr12/ | Course Webpage ]]
----
Added lines 1-47:
! Tiny Images Dataset download
The entire 80 million Tiny Image dataset can be downloaded [[http://horatio.cs.nyu.edu/mit/tiny/data/index.html|here]].
----
!Removing Camera Shake from a Single Image. Code for SIGGRAPH 2006 paper.
The code package includes the Matlab source code used in the paper, a comprehensive [[http://cs.nyu.edu/~fergus/research/deblur/README|README]] file and sample images. Please fill in the form [[http://cs.nyu.edu/~fergus/deblur_code_form.html|here]] and I will email you the code.
----
!Fast 2D image convolution MEX file for Matlab under Linux.
Wrapper for Intel's Integrated Performance Primitives (IPP) libraries, also using OpenMP multi-threading. Requires 64-bit Intel CPU, up to date IPP libraries and Intel Compiler. Runs 5x-25x faster than Matlab's conv2 (or fft2 if kernel is large), check out the [[http://cs.nyu.edu/~fergus/code/ipp_timings.txt|timings]]. [[http://cs.nyu.edu/~fergus/code/ipp_mt_conv2.cpp|MEX file source]], [[http://cs.nyu.edu/~fergus/code/mexopts.sh|mexopts.sh MEX configuration]], [[http://cs.nyu.edu/~fergus/code/conv_test.m|Matlab test script]].
----
!Learning Object Categories from Google's Image Search. Datasets from ICCV 2005 paper.
The .tgz file below contains the images of the seven categories collected from Google which were used in our ICCV'05 paper, "Learning Object Categories from Google's Image Search".
[[http://cs.nyu.edu/~fergus/research/fergus_iccv05_google.tgz|Google images and labels from ICCV'05 paper]]
The 7 classes are: Airplane, Cars Rear, Face, Guitar, Leopard, Motorbike, Wrist Watch. In each subdirectory, the labels are contained within a Matlab file, Ground_Truth.mat. This has a tri-state label for each image. 0 = Junk, 1 = Intermediate and 2 = Good Example. The labeling was performed by an individual who has no knowledge of the algorithm.
[[http://cs.nyu.edu/~fergus/research/fergus_iccv05_google_validation.tgz|Google validation set from ICCV'05 paper]]
[[http://cs.nyu.edu/~fergus/research/iccv05_frame_ind.tgz|Train/test frame indices from ICCV'05 paper]]
[[http://cs.nyu.edu/~fergus/research/fergus_iccv05_watchleopardguitar.zip|Caltech test data: Guitars, Leopards, Wrist Watches used for testing in ICCV'05 Google paper]]
----
!Constellation Model. Code for CVPR'03 and CVPR'05 papers.
I have code available for distribution for both the CVPR'03 paper "Object Class Recognition by Unsupervised Scale-Invariant Learning" and also the CVPR'05 paper "A Sparse Object Category Model for Efficient Learning and Exhaustive Recognition". The CVPR'03 code is Linux only. The CVPR'05 code works under both Windows and Linux and uses the [[http://vxl.sourceforge.net/|VXL]] libraries. Please email me if you would like the code.
----
!Oxford Robotics Reading Group on EM
Here are some materials from the reading group I gave on EM in Oxford on 24/05/05.
[[http://cs.nyu.edu/~fergus/research/tutorial_em.ppt|Powerpoint slides]]. Demo [[http://cs.nyu.edu/~fergus/research/tutorial_em_matlabcode.zip|Matlab code]] for 2-D Gaussian mixtures
The entire 80 million Tiny Image dataset can be downloaded [[http://horatio.cs.nyu.edu/mit/tiny/data/index.html|here]].
----
!Removing Camera Shake from a Single Image. Code for SIGGRAPH 2006 paper.
The code package includes the Matlab source code used in the paper, a comprehensive [[http://cs.nyu.edu/~fergus/research/deblur/README|README]] file and sample images. Please fill in the form [[http://cs.nyu.edu/~fergus/deblur_code_form.html|here]] and I will email you the code.
----
!Fast 2D image convolution MEX file for Matlab under Linux.
Wrapper for Intel's Integrated Performance Primitives (IPP) libraries, also using OpenMP multi-threading. Requires 64-bit Intel CPU, up to date IPP libraries and Intel Compiler. Runs 5x-25x faster than Matlab's conv2 (or fft2 if kernel is large), check out the [[http://cs.nyu.edu/~fergus/code/ipp_timings.txt|timings]]. [[http://cs.nyu.edu/~fergus/code/ipp_mt_conv2.cpp|MEX file source]], [[http://cs.nyu.edu/~fergus/code/mexopts.sh|mexopts.sh MEX configuration]], [[http://cs.nyu.edu/~fergus/code/conv_test.m|Matlab test script]].
----
!Learning Object Categories from Google's Image Search. Datasets from ICCV 2005 paper.
The .tgz file below contains the images of the seven categories collected from Google which were used in our ICCV'05 paper, "Learning Object Categories from Google's Image Search".
[[http://cs.nyu.edu/~fergus/research/fergus_iccv05_google.tgz|Google images and labels from ICCV'05 paper]]
The 7 classes are: Airplane, Cars Rear, Face, Guitar, Leopard, Motorbike, Wrist Watch. In each subdirectory, the labels are contained within a Matlab file, Ground_Truth.mat. This has a tri-state label for each image. 0 = Junk, 1 = Intermediate and 2 = Good Example. The labeling was performed by an individual who has no knowledge of the algorithm.
[[http://cs.nyu.edu/~fergus/research/fergus_iccv05_google_validation.tgz|Google validation set from ICCV'05 paper]]
[[http://cs.nyu.edu/~fergus/research/iccv05_frame_ind.tgz|Train/test frame indices from ICCV'05 paper]]
[[http://cs.nyu.edu/~fergus/research/fergus_iccv05_watchleopardguitar.zip|Caltech test data: Guitars, Leopards, Wrist Watches used for testing in ICCV'05 Google paper]]
----
!Constellation Model. Code for CVPR'03 and CVPR'05 papers.
I have code available for distribution for both the CVPR'03 paper "Object Class Recognition by Unsupervised Scale-Invariant Learning" and also the CVPR'05 paper "A Sparse Object Category Model for Efficient Learning and Exhaustive Recognition". The CVPR'03 code is Linux only. The CVPR'05 code works under both Windows and Linux and uses the [[http://vxl.sourceforge.net/|VXL]] libraries. Please email me if you would like the code.
----
!Oxford Robotics Reading Group on EM
Here are some materials from the reading group I gave on EM in Oxford on 24/05/05.
[[http://cs.nyu.edu/~fergus/research/tutorial_em.ppt|Powerpoint slides]]. Demo [[http://cs.nyu.edu/~fergus/research/tutorial_em_matlabcode.zip|Matlab code]] for 2-D Gaussian mixtures