CVML FunClub scope
R&D in computer vision and machine learning.
2020 objective: development of fast convolution algorithms for computer vision and machine learning.
Languages: C/C++, Python, CUDA.

Members: anyone loving  mathematics and/or having programming interests/expertise in C/C++, Python, CUDA.

Meetings: Every Friday, 6-7 pm EET, Prof. Pitas’ office, Department of Informatics, Mezzazine floor, Biology Building, University Campus, Aristotle University of Thessaloniki, Greece.

Fast Convolutions

2D convolutions play an extremely important role in machine learning, as they form the first layers of Convolutional Neural Networks (CNNs). They are also very important for computer vision (template matching through correlation, correlation trackers) and in image processing (image filtering/denoising/restoration). 3D convolutions are very important for machine learning (video analysis through CNNs) and for video filtering/denoising/restoration. 1D convolutions are extensively used in digital signal processing (filtering/denoising)  and analysis (also through CNNs).

Therefore, 1D/2D/3D convolution algorithms are very important both for machine learning and for signal/image/video processing and analysis. As their computational complexity is of the order O(N^2), O(N^4) and O(N^6) respectively their fast execution is a must.

Contact: Christos Papaioannidis