Adding the copyright file, also explaining more stuff in the readme.

Copyright.txt

@@ -0,0 +1,46 @@
+CCNF license
+-------------------
+(Academic, non-commercial, not-for-profit licence)
+
+Copyright (c) 2014 Tadas Baltrusaitis
+All rights reserved.
+
+Redistribution and use in source and binary forms, with or without 
+modification, are permitted provided that the following conditions are met:
+
+    * The software is provided under the terms of this licence stricly for
+      academic, non-commercial, not-for-profit purposes.
+    * Redistributions of source code must retain the above copyright notice, 
+      this list of conditions (licence) and the following disclaimer.
+    * Redistributions in binary form must reproduce the above copyright 
+      notice, this list of conditions (licence) and the following disclaimer 
+      in the documentation and/or other materials provided with the 
+      distribution.
+    * The name of the author may not be used to endorse or promote products 
+      derived from this software without specific prior written permission.
+    * As this software depends on other libraries, the user must adhere to and 
+      keep in place any licencing terms of those libraries.
+    * Any publications arising from the use of this software, including but
+      not limited to academic journal and conference publications, technical
+      reports and manuals, must cite one of the following works:
+
+    Tadas Baltrusaitis, Peter Robinson, and Louis-Philippe Morency. 
+    Constrained Local Neural Fields for robust facial landmark detection in the wild.
+    in IEEE Int. Conference on Computer Vision Workshops, 300 Faces in-the-Wild Challenge, 2013.   
+
+    Vaiva Imbrasait?, Tadas Baltru�aitis, Peter Robinson. 
+    CCNF for continuous emotion tracking in music.
+    in IEEE Int. Conference on Multimedia and Expo, Multimedia Affective Computing, 2014.
+
+THIS SOFTWARE IS PROVIDED BY THE AUTHOR "AS IS" AND ANY EXPRESS OR IMPLIED 
+WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF 
+MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO 
+EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, 
+INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, 
+BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, 
+DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY 
+OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING 
+NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, 
+EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+
+-----------------------------------

README.md

@@ -1,6 +1,26 @@
-CCNF
-====
+These files contain the libraries needed to train and test Continuous Conditional Neural Fields (CCNF) and Continuous Conditional Random Fields (CCRF).
 
-Libraries for training and testing Continuous Conditional Neural Fields with a number of sample problems and other baselines.
+The project was tested on Matlab R2012b and R2013a (can't guarantee compatibility with other versions).
 
-TODO add more readme stuff.
+Some of the experiments rely on the availability of mex compiled liblinear (http://www.csie.ntu.edu.tw/~cjlin/liblinear/) and libsvm (http://www.csie.ntu.edu.tw/~cjlin/libsvm/) on your machine.
+
+--------------------------------------- Copyright information -----------------------------------------------------------------------------	
+
+Copyright can be found in the Copyright.txt
+
+--------------------------------------- Code Layout -----------------------------------------------------------------------------
+./CCNF - the training and inference libraries for CCNF
+./CCRF - the training and inference libraries for CCRF
+
+./music_emotion - emotion in music prediction experiments, comparing the use of CCNF, CCRF, Neural Net (CCNF without edge), and SVR models
+    results/ - the results from running the experiments
+
+./patch_experts - training code for patch expert training (for facial landmark detection), more in the readme.txt in the relevant folder
+    ccnf_training/ - training CCNF patch experts (for the Constrained Local Neural Fields for robust facial landmark detection in the wild paper)
+    data_preparation/ - converting image and landmark datasets to the right formats
+    svr_training/ - training SVR patch experts (the standard CLM patch experts)
+
+--------------------------------------- Final remarks -----------------------------------------------------------------------------	
+
+I did my best to make sure that the code runs out of the box but there are always issues and I would be grateful for your understanding that this is research code and not a commercial
+level product. However, if you encounter any probles please contact me at Tadas.Baltrusaitis@cl.cam.ac.uk for any bug reports/questions/suggestions. 

music_emotion/readme.txt

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+This code is in support of the "CCNF for continuous emotion tracking in music" paper by Vaiva Imbrasaite, Tadas Baltrusaitis, and Peter Robinson
+
+This code provides the code for training and testing emotion in music prediction models.
+
+The relevant dataset is packaged, you only need mex compiled liblinear (http://www.csie.ntu.edu.tw/~cjlin/liblinear/) and libsvm (http://www.csie.ntu.edu.tw/~cjlin/libsvm/) for experiment recreation.
+
+To run the experiments use:
+Script_CCNF.m trains and tests a linear-chain CCNF
+Script_CCNF_no_edge.m trains and tests an unconnected CCNF (basically a Neural Network)
+Script_SVR_CCRF.m trains and tests a CCRF model that relies on SVR-rbf predictions
+Script_SVR_linear.m trains and tests a linear SVR model
+Script_SVR_rbf.m trains and tests an RBF kernel SVR model

patch_experts/readme.txt

@@ -4,8 +4,10 @@ This code provides the code for training the patch experts used in the experimen
 
 You have to have the relevant datasets, to run the code, the in-the-wild datasets can be found at http://ibug.doc.ic.ac.uk/resources/facial-point-annotations/), the annotations are provided. The Multi-PIE dataset can be acquired from - http://www.multipie.org/, you will need to ask the authors of the dataset for the annotations. 
 
-CCNF folder contains the CCNF training library.
+./data_preparation/ folder contains scripts to prepare data for CCNF (CLNF) and SVR patch expert training
 
-clnf folder contains scripts to prepare data for CLNF and SVR patch expert training and the training code itself. I suggest starting with dataset preparation then with SVR and CCNF patch expert training.
+./ccnf_training/ and ./svr_training/ folders contain the training code itself.
 
-The trained patch experts can then be used with the C++ CLM_framework and the Matlab fitting algorithms.
+I suggest starting with dataset preparation then with SVR and CCNF patch expert training.
+
+The trained patch experts can then be used with the C++ CLM_framework (TODO link) and the Matlab fitting algorithms (TODO link).