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en:projects:details:neuralsemproject [2013/01/09 10:19]
amir
en:projects:details:neuralsemproject [2016/06/23 11:26] (current)
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   The database entry:   The database entry:
   "​type"​ is one of the following: phd theses, phd semester, master thesis, master semester, bachelor semester   "​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!!!!!!!!!!) ​+  "state" is one of the following: available, taken, completed (please upgrade accordingly!!!!!!!!!!) ​
   "​by"​ should be filled as soon as the project is taken/​completed   "​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). ​   "​completed_dt"​ is the date when the project was completed (YYYY-MM-DD). ​
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 contactroom:​ BC 160 contactroom:​ BC 160
 type : master semester type : master semester
-status ​available+state completed
 created_dt : 2010-11-15 created_dt : 2010-11-15
-taken_dt : YYYY-MM-DD +taken_dt : 2013-01-19 
-completed_dt : YYYY-MM-DD +completed_dt : 2013-06-12 
-by : student name +by : Diego Marcos Gonzalez ​ 
-output_media : en:​projects:​neural_storage_capacity.pdf|Download ​Abstract ​in PDF Format+output_media : en:​projects:​master_semester:​marcos_salavati_semester_project_report_2013.pdf|Download ​Project Report ​in PDF Format
 table : projects table : projects
 ====== ======
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 \\ \\
 /* Description of the project */ /* Description of the project */
-===== Background ​=====+===== Project Description ​=====
 \\ \\
 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 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. ​
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 Some knowledge about feature learning models in computer vision is not necessary but is deeply encouraged. ​ Some knowledge about feature learning models in computer vision is not necessary but is deeply encouraged. ​
 +
 +\\
 +\\
 +\\
 +
 +====== Report ======
 +The report ​ is available via the following link:
 +\\
 +{{:​en:​projects:​master_semester:​marcos_salavati_semester_project_report_2013.pdf|Implementing Some Feature Extracting Techniques to Model Human Visual System}}