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en:projects:details:neuralsemproject [2012/11/17 17:23] 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. | ||
| - | 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. | + | In this project, we are interested in implementing some of 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). | The implementation can be either done in C or MATLAB (MATLAB is preferred). | ||
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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. | ||
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| + | \\ | ||
| + | \\ | ||
| + | \\ | ||
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| + | ====== 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}} | ||