We are interested in studying the impact of applying various pre-processing methods on the detection efficiency of Viola-Jones detector. The pre-processing has to be blind so to be applied in all scenarios and still we do get better performance.
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Project Link: https://github.com/Bhartendu-Kumar/DIP
DIP project
DataSets:
- MIT CBCL
- BioID
- Yale
- Caltech
- SoF
- Orl
- Non - Face Dataset (Collected Heterogeneously)
Pre-Processing Methods Studied:
- Low Pass Filters:
- gaussian blur
- bilateral filter
- Deconvolutions
- Blind Deconvolution (richardson lucy)
- tv_chambolle
- BMD (partially implemented , not converging yet)
- Retinex
- SSR
- MSR
- retinex FM
- NMBM
- Normalization
- HOMO
- Histogram Manipulation
- CLAHE
- HE
- log intensity stretch
- full scale intensity stretch
The pipeline is as:
Step 1: Download the DataSets (from their owner sites) and use the scripts in the dataset_helper_scripts directory to convert all to greyscale and change extensions like (.gif and .pgm) to common extensions. Also arrange all images of a dataset in a single folder using these scripts.
Step 2: Arrange the data directory like shown has 2 subdirectories: faces non-faces These further have their particular sub-directories with name of the DATASET
----! Important, for illuastration "data" folder has the desired structure, just that it has few images
Step 3: Run all the "driver scripts" in the root directory of this repository. This will create an "output" directory with the result of all preprocessing methods.
Step 4: Use "csv_scripts" directory to get the performance metrics from the "output" directory
Step 5: "analysis" directory has the final performance csv files of each pre-procesing method
Step 6: Published report directory has the final findings in form of csv file.