Canny Edge from Scratch
Implementing Canny Edge Detector from Scratch
Implementing Canny Edge Detector from Scratch
CNN based classifier for categorising dogs from cats
Classifying handwritten digits from the MNIST dataset of over 60000 images and achieving an accuracy of over 99.4%
Blending images by averaging them in the frequency domain
Separating textures using Mixture Model and EM algorithm
Predicting the survivals of test samples(/person) using some of the given cases result. This problem is based on feature analysis and classification.
Published in Journal 1, 2009
This paper is about the number 1. The number 2 is left for future work.
Recommended citation: Your Name, You. (2009). "Paper Title Number 1." Journal 1. 1(1). http://academicpages.github.io/files/paper1.pdf
Published in Journal 1, 2010
This paper is about the number 2. The number 3 is left for future work.
Recommended citation: Your Name, You. (2010). "Paper Title Number 2." Journal 1. 1(2). http://academicpages.github.io/files/paper2.pdf
Published in Journal 1, 2015
This paper is about the number 3. The number 4 is left for future work.
Recommended citation: Your Name, You. (2015). "Paper Title Number 3." Journal 1. 1(3). http://academicpages.github.io/files/paper3.pdf
Published:
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Published:
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Undergraduate course, University 1, Department, 2014
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Workshop, University 1, Department, 2015
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