Rank: 96 based on 696 downloads (last 30 days) and 10 files submitted
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Masayuki Tanaka

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http://like.silk.to/matlab/

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Files Posted by Masayuki View all
Updated   File Tags Downloads
(last 30 days)
Comments Rating
18 Jul 2014 Screenshot Guided Upsampling and Residual Interpolation This tool upsamples the data with the high-resolution guidance image. Author: Masayuki Tanaka upsampling, image processing, filter 26 0
07 May 2014 Screenshot Noise Level Estimation from a Single Image It can precisely estimate noise level from a single image. Author: Masayuki Tanaka image processing 88 12
  • 5.0
5.0 | 3 ratings
13 Jan 2014 Screenshot Deep Neural Network It provides deep learning tools of deep belief networks (DBNs). Author: Masayuki Tanaka machine learning 259 35
  • 4.63636
4.6 | 11 ratings
13 Jan 2014 Screenshot Face Parts Detection It detects faces with left eye, right eye, mouth, and norse. Author: Masayuki Tanaka image processing, face, computer vision 163 48
  • 4.38889
4.4 | 19 ratings
25 Nov 2013 Screenshot GUI Image Mask Sample It is a sample code to draw and erase mask on an image by GUI. It gives you hints to develop the GUI Author: Masayuki Tanaka gui, image processing, image 22 0
  • 2.0
2.0 | 1 rating
Comments and Ratings by Masayuki View all
Updated File Comments Rating
20 Jul 2014 Face Parts Detection It detects faces with left eye, right eye, mouth, and norse. Author: Masayuki Tanaka

Hi Sapio,

Thank you for your comment.
I think the bbox provides in the pixel coordinate. Please see the documentation of the computer vision toolbox for details of the bounding box.

Thank you.

18 Jul 2014 Deep Neural Network It provides deep learning tools of deep belief networks (DBNs). Author: Masayuki Tanaka

Hi Andre Flipe,

Honestly, I could not get what kind of the network structure you want. But, if you set [10, 2] when create a DBN, it means that 10 nodes for input and 2 nodes for output without hidden nodes.

Thanks.

18 Jul 2014 Deep Neural Network It provides deep learning tools of deep belief networks (DBNs). Author: Masayuki Tanaka

Hi Ari,

I have run testDNN. But, I could not reproduce the error which you mentioned.

Thanks.

18 Jul 2014 Deep Neural Network It provides deep learning tools of deep belief networks (DBNs). Author: Masayuki Tanaka

Hi Alena,

readme.txt is in the folder of mnist. Please check it!

Thanks.

13 May 2014 Face Parts Detection It detects faces with left eye, right eye, mouth, and norse. Author: Masayuki Tanaka

Hi Gowtham and sanaz,

What kind of modification did you apply? Would you share your modification?
Thanks.

Comments and Ratings on Masayuki's Files View all
Updated File Comment by Comments Rating
20 Jul 2014 Face Parts Detection It detects faces with left eye, right eye, mouth, and norse. Author: Masayuki Tanaka Tanaka, Masayuki

Hi Sapio,

Thank you for your comment.
I think the bbox provides in the pixel coordinate. Please see the documentation of the computer vision toolbox for details of the bounding box.

Thank you.

19 Jul 2014 Face Parts Detection It detects faces with left eye, right eye, mouth, and norse. Author: Masayuki Tanaka L, Sapio

Hello,Mr.Tanaka.

First,thank you for a code.

How can I obtain coordiantes of rectangle?

As I got,bbox is not an array of pixel coordiantes of rectangles.

18 Jul 2014 Deep Neural Network It provides deep learning tools of deep belief networks (DBNs). Author: Masayuki Tanaka Tanaka, Masayuki

Hi Andre Flipe,

Honestly, I could not get what kind of the network structure you want. But, if you set [10, 2] when create a DBN, it means that 10 nodes for input and 2 nodes for output without hidden nodes.

Thanks.

18 Jul 2014 Deep Neural Network It provides deep learning tools of deep belief networks (DBNs). Author: Masayuki Tanaka Tanaka, Masayuki

Hi Ari,

I have run testDNN. But, I could not reproduce the error which you mentioned.

Thanks.

18 Jul 2014 Deep Neural Network It provides deep learning tools of deep belief networks (DBNs). Author: Masayuki Tanaka Tanaka, Masayuki

Hi Alena,

readme.txt is in the folder of mnist. Please check it!

Thanks.

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