Detection of Counterfeit Banknotes

Methodology involves the extraction and encoding of certain security features.
Given the prominence of the security thread in certain image planes, a clustering algorithm, k-means is applied for classification.
The latent image, segmented via template matching was encoded using HOG descriptor and classified with an SVM model.

Name		Name	Last commit message	Last commit date
Latest commit History 10 Commits
Latent Templates		Latent Templates
datasets		datasets
final paper and references		final paper and references
not used notes		not used notes
notes		notes
paper images		paper images
papers		papers
LatentImg_preTest		LatentImg_preTest
LatentImg_preTest.cpp		LatentImg_preTest.cpp
LatentImg_preTest.cpp~		LatentImg_preTest.cpp~
Latent_img		Latent_img
Latent_img.cpp		Latent_img.cpp
Latent_img.cpp~		Latent_img.cpp~
README.md		README.md
SecurityThread_preTest		SecurityThread_preTest
SecurityThread_preTest.cpp		SecurityThread_preTest.cpp
Thumbs.db		Thumbs.db
a		a
a.cpp		a.cpp
auto_canny.h		auto_canny.h
canny		canny
canny.cpp		canny.cpp
clustering		clustering
clustering.cpp		clustering.cpp
edge		edge
edge.cpp		edge.cpp
hist		hist
hist.cpp		hist.cpp
histogram		histogram
histogram.cpp		histogram.cpp
histogram.py		histogram.py
km		km
km.cpp		km.cpp
security_thread		security_thread
security_thread.cpp		security_thread.cpp
security_thread.cpp~		security_thread.cpp~
split		split
split.cpp		split.cpp
template_matching		template_matching
template_matching.cpp		template_matching.cpp
testLatentImg		testLatentImg
testLatentImg.cpp		testLatentImg.cpp
testLatentImg.cpp~		testLatentImg.cpp~
trainLatentImg		trainLatentImg
trainLatentImg.cpp		trainLatentImg.cpp
trainLatentImg.cpp~		trainLatentImg.cpp~

Provide feedback