• http://web.eecs.utk.edu/~itamar/Papers/BICA2009.pdf
• http://www.ece.utk.edu/~itamar/Papers/BICA2011T.pdf
• http://web.eecs.utk.edu/~itamar/Papers/AI_MAG_2011.pdf
• http://research.microsoft.com/en-us/um/people/dongyu/nips2009/papers/Arel-DeSTIN_NIPS%20tpk2.pdf
• http://goertzel.org/Goertzel_AAAI11.pdf
• http://goertzel.org/DeSTIN_OpenCog_paper_v1.pdf
• http://goertzel.org/papers/DeSTIN_OpenCog_paper_v2.pdf
• http://www.springerlink.com/content/264p486742666751/fulltext.pdf
• http://goertzel.org/VisualAttention_AGI_11.pdf
• http://goertzel.org/Uniform_DeSTIN_paper.pdf
• http://goertzel.org/papers/Uniform_DeSTIN_paper_v2.pdf
• http://goertzel.org/papers/CogPrime_Overview_Paper.pdf

• Since it's working with ROS, some of the source files are updated to work with OpenCV in ROS
• Some tools for generating image labels are uploaded
• A 33 classes classification code and result are uploaded.

• Update BeliefExporter.h for generating features more flexible [DONE]
• Testing classification result in larger dataset [DONE]
• Growing DeSTIN map with videos
  1. Each scene is trained by fixed number of frames (frames are also saved).
  2. Save DeSTIN and generate features.
  3. Robot move and change scene.
  4. The first frame of new scene will be feed to all saved DeSTINs and extract features.
  5. Calcualte average distance based on saved features.
  6. If the distances are larger than all DeSTINs, then create new DeSTIN, or enable the most similar DeSTIN and train again.
• Set up GitHub repo for ROS version
• Working with color images
• Combine depth image channel (Optional)

Implementing uniform destin described here: http://wiki.opencog.org/w/DestinOpenCog

Creating enhanced visualization capabilities.

For more detailed development progress see Diary.md

Instructions below are for Ubuntu.

See Windows build instructions at https://github.com/opencog/destin/blob/master/WindowsBuild.md

Dependencies: CMake ( >= 2.8 ) OpenCV

Its been hard to pin down the exact opencv packages required for different versions of Ubuntu. Until we get this straightened out, use synaptic or the Ubuntu Software Center and do a search for opencv and install all the opencv related libs you can see. There may be around 10 different opencv libraries.

Java Bindings: JDK, ant and SWIG 2.x

Python Bindings: python-dev (tested with python 2.7), python-opencv, and SWIG 2.x

To build DrentheDestin, CUDA SDK is required.

Individual parts can be skipped by commenting out their ADD_SUBDIRECTORY line in the CMakeLists.txt Some examples:

• If you dont have CUDA for example, comment out the "ADD_SUBDIRECTORY(DrentheDestin)" in Destin/CMakeLists.txt

• If you dont want to install any language bindings comment out "ADD_SUBDIRECTORY(Bindings)" in Destin/CMakeLists.txt.

• If you want python bindings but not Java, then in Destin/Bindings/CMakeLists.txt comment out "add_subdirectory(Java)"

Building:

$ git clone http://github.com/opencog/destin.git
$ cd destin
$ git submodule init
$ git submodule update
$ cd Destin
$ cmake . 
$ make

Rebuilding:

During development sometimes CMake is not able to detect dependencies between the different built libraries. So it is recommened to do a "make clean" and "make -j4" (j4 lets you use multiple cores) from the Destin directory to make sure everyting is building fresh.

Building Java Bindings:

The native jni bindings lib is built with CMake, the actual java program which uses it is built with java_build.sh:

$ cd Destin/Bindings/Java
$ ./java_build.sh
$ ./java_run.sh

Currently the most interesting things to see are the scripts in the Destin/Bindings/Python directory. These scripts are meant to be used interactivly so I recommend installing and using idle for python ( search for and install the idle package).

To see a self organizing map (SOM):

Manually download http://www.cs.toronto.edu/~kriz/cifar-10-binary.tar.gz

$ tar xf cifar-10-binary.tar.gz
$ cd cifar-10-batches-bin
$ pwd

Note the absolute path

$ cd Destin/Bindings/Python

Edit som.py and edit the cifar_dir variable near the top and change it to the absolute path of the cifar-10-batches-bin directory

$ idle som.py

Two windows appear, select the som.py window and click Run->Run Module (F5). Training will begin, after less than 5 minutes the SOM will popup.

To see DeSTIN train on an video and see its output:

$ cd Destin/Bindings/Python
$ idle dostuff.py

Two windows should appear.

- select the dostuff.py window
- click Run -> Run Modedule ( F5 )

You should see a video of a hand and windows for 8 layers.

In the other "Python Shell" window you can interact with the code after its done with 500 frames ( about a minute). To make it continue processing type go(100) to have it process 100 more frames. You can make it train on webcam input instead by changing the line:

vs = pd.VideoSource(False, "hand.m4v")

to

vs = pd.VideoSource(True, "")