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CUDA-Connected-Component-Labelling
Parallel GPU Implementation of Connected Component Labelling (CCL). Connected-component labeling is used in computer vision to detect connected regions in binary digital imagesRoomNet
A lightweight ConvNet (~700 KB) to classify pictures of different rooms of a house/apartment with 88.9 % accuracyCPU-Connected-Component-Labelling
This is a fast custom algorithm having O(n) linear time & O(n) memory complexity implemented on the CPU for solving the famous connected component labelling problem. The algorithm implemented here, takes the image and the label of the connected region and spits out the number of such regions in the image.SHMatrix
A neat C++ custom Matrix class to perform super-fast GPU (or CPU) powered Matrix/Vector computations with minimal code, leveraging the power of cuBLAS where applicable.naive-dqn-maze
Maze Solver from scratch (under 260 lines) using Naive Reinforcement Learning with Q-Table constructionLens-Smear-Detection
Python implementation of a Computer Vision algorithm to automatically extract lens smear regions from a batch of input images.Link-State-Routing-Simulator-CS-542
A C++ simulator to simulate Link State Routing protocols used in Computer Networkspark.ai
Codebase to perform real-time detection+tracking of cars across different camera sourceshigh-vision
microsoft ai hackathon submission for cloud coverage segmentationBusiness-Expansion-Engine-CS579
A Python application which analyses the social network pertaining to a given twitter handle and gives recommendations on the people in the network most likely to embrace a given product or service.Love Open Source and this site? Check out how you can help us