Introduction to Human and Computer Vision project
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Project folder tree:
|-- Dataset |-- test |-- train |-- gt |-- mask |--train_split |-- gt |-- mask |-- MeanShift_mask |--validation_split |-- gt |-- mask |-- circular_hough |-- colorspace |-week1 |-week2 |-- evaluation |-- dataset_analysis |-- ColorDistributionHist |-- color_segmentation |-- k_means |-- Mean_Shift |-- k_means_ycbcr
WEEK 1:
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TASK 1 and TASK 2: To run these tasks you must to run launch_characteristics_extraction.m script that is available in dataset_analysis folder.
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TASK 3: To run this task (that is in color_segmentation folder) you must to run algorithms scripts that has algorithm name like k_means.m, these scripts has folder for each one. Optionally, we can execute ColorDistributionHist to know the maximum and minimum values for each signal RGB channel.
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TASK 4: Run launch_detection.m with mask path in order to obtain performance evaluation.
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TASK 5: Use EqualizeImage(Img) function in order to obtain a equalize image.
WEEK 2:
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TASK 1: Run morphology_test.m
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TASK 2: Run Launch_MyDilateAndMyerode.m
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TASK 3: Run Launch_YcbCrAndHSVWithOpening.m
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TASK 4: In week2/histogram_back_projection, run main.m
WEEK 3:
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TASK 1: Run CCL.m
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TASK 2: Run main.m
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TASK 3: Run main.m
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TASK 5: In Task5.m
WEEK 4:
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