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en:projects:details:neuralsemproject [2012/11/17 17:22]
en:projects:details:neuralsemproject [2016/06/23 11:26]
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-/* This is the template for project details pages */ 
-  The database entry: 
-  "​type"​ is one of the following: phd theses, phd semester, master thesis, master semester, bachelor semester 
-  "​status"​ is one of the following: available, taken, completed (please upgrade accordingly!!!!!!!!!!) ​ 
-  "​by"​ should be filled as soon as the project is taken/​completed 
-  "​completed_dt"​ is the date when the project was completed (YYYY-MM-DD). ​ 
-  "​output_media"​ is the link to the pdf of the project (wiki syntax) 
-  "​table"​ must be "​projects"​ => don't touch it! 
----- dataentry project ---- 
-title : Implementing Various Feature Extracting Techniques from Visual (and Audio) Signals to Model Human Visual System 
-contactname:​ Amir Hesam Salavati 
-contactmail_mail:​ hesam.salavati@epfl.ch 
-contacttel: 021 - 693 81 37 
-contactroom:​ BC 160 
-type : master semester 
-status : available 
-created_dt : 2010-11-15 
-taken_dt : YYYY-MM-DD 
-completed_dt : YYYY-MM-DD 
-by : student name 
-output_media : en:​projects:​neural_storage_capacity.pdf|Download Abstract in PDF Format 
-table : projects 
-/* Description of the project */ 
-===== Background ===== 
-In computer vision, there are various different techniques to extract important features from images. These features are then later used in patten recognition,​ image classification,​ etc. Some of these techniques are comparable to some models of specific parts in human visual system. ​ 
-In this project, we are interested in implementing some the widely used techniques in feature learning (extraction) and applying them to a dataset of natural images. This usually corresponds to solving some optimization problem to find the features that represent the data more accurately. 
-The implementation can be either done in C or MATLAB (MATLAB is preferred). ​ 
-And here are some lines to give you an idea about why we are interested in this project: ​ 
-Once the feature extraction techniques are implemented,​ the learned features will then be used as inputs to a neural network which mimics some parts of human memory (neural associative memory). The ultimate goal would be to see if one will get better information storage capacities in artificial neural memories when the inputs are natural stimuli (such as images) and pre-processed before being stored. HEre, pre-processing refers to the feature extraction procedure. 
-This project is suitable for students interested in computer vision, neural networks and mathematics who prefer a combination of theoretical and empirical works. ​ 
-The prerequisites are: 
-1)Basic knowledge of linear algebra. ​ 
-2)Being familiar with a suitable programming language (MATLAB is preferred. But C/C++ is acceptable.) 
-Some knowledge about feature learning models in computer vision is not necessary but is deeply encouraged. ​