SYNTCV Project


Title: New Techniques for Syntactical Pattern Recognition

Goal: Develop and apply grammar based machine learning algorithms to solve agriculture and livestock problems

Team: Hemerson Pistori (UCDB), Marcelo Borth (IFMS), Kleber Padovani (IFMS), Amaury de Castro Jr (UFMS)

Collaborators: Peter Flach (UoB), Andrew Calway (UoB), Rob Frampton (UoB), Tilo Burghardt (UoB)

Grants and Fundings: CNPQ

Main Software: syntsce_0.4.zip

Slides

- Grammars and Learning for Scene Classification: A Survey (UNDER CONSTRUCTION) [pdf] [pptx] [conceptual map]

- Zhu, Song-Chun and Mumford, David A Stochastic Grammar of Images [pdf] [ppt] [Original Paper]

- Adaptive Automata and Grammars (University of Bristol, November, 2011) [pdf] [ppt]





Links* Datasets

Geom Dataset 
SUN Benchmark Dataset for Scene Recognition 
Ponce's 15 Scenes Categories Dataset


* Grammar Inference Tools

FUNGI Toolkit in Objective Caml 
SEQUITUR Implementation of SEQUITUR algorithm in C 
GIToolBox Toolbox for Matlab 
Inference of Regular Languages in C++ 


* Conferences and Journals

ICGI 2012 


* Research Groups, Lab and Projects

Grammar Inference Technology Applications in Software Engineering - The University of Alabama 


* Some People

Colin de la Higuera, Nantes University, France 
Hasan Ibne Akram, Hasan Ibne Akram, Germany 
Song-Chun Zhu, University of California, USA 
King-Sun Fu, Purdue University, USA (In Memoriam) 
Jeffrey Heinz, University of Delaware, USA 
François Coste, INRIA, France 
Yusuburi Sakakibara, Keio, Japan 


* Others

Grammatical Induction Website by Menno Van Zaanen