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algorithms

Here I present are some of the optimized algorithms (Dynamic programming, Divide & conquer, Greedy) I implemented in the online course Algorithmic Design and Techniques on Edx.com by UC San Diego.

audio-effect-mixer

Building an Audio mixer on Simulink.

deep-learning-for-dendritic-spines-detection

Benchmarking Yolov2, Faster-RCNN and Shape-Priors-CNN on dendritic spines detection

facility-location-with-gurobi

Finding the optimal distribution of a number of resources over 36 cities of Hauts de Seine, France. Python + Gurobi Implementation.

image-inpainting-via-sparse-representation

Image inpainting using Lasso regression (sparse representation). Python implementation, with ablation study, analysis and discussion presented as a Jupyter Notebook

imagecoregistration

Image registration using sift and RANSAC

legendre-moments-for-pattern-recognition-and-reconstruction

Image Analysis with Legendre Moment Descriptors C implementation

mask_rcnn

Mask R-CNN for object detection and instance segmentation on Keras and TensorFlow

multi-agent-system-with-jade

Multi-agents collaborate to explore an environment, collect goods and avoid obstacles. Jade and Dedale frameworks.

primality-tests

Fermat and Miller-Rabin primality tests as well as different important functions.

qopp

refiners

A microframework on top of PyTorch with first-class citizen APIs for foundation model adaptation

unsupervised-optical-flow

Pytorch implementation of the paper: Unsupervised Learning of Optical Flow via Brightness Constancy and Motion Smoothness.