Object classification with basic computer vision techniques.
Final assignment for the computer vision course I took at university. The task was to classify objects in a picture using any tools we learned during the course. I used histogram equalization, Otsu thresholding, image dilation and other morphological operators for preprocessing and OpenCV's connected component labelling function for classifying objects according to their Hu moments.
Light Gradient Boosting Machine LightGBM is a gradient boosting framework that uses tree based learning algorithms. It is designed to be distributed a
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Hierarchical Attention Networks for Document Classification This is an implementation of the paper Hierarchical Attention Networks for Document Classi
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DI Orchestrator DI Orchestrator is designed to manage DI (Decision Intelligence) jobs using Kubernetes Custom Resource and Operator. Prerequisites A w
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UCPhrase: Unsupervised Context-aware Quality Phrase Tagging To appear on KDD'21...[pdf] This project provides an unsupervised framework for mining and
Orchestrating Distributed Materials Acceleration Platform Tutorial This tutorial for orchestrating distributed materials acceleration platform was pre
Learning-based Camera Calibration A Learning-based Camera Calibration Toolbox Paper The pdf file can be found here. @misc{zhang2022learningbased,
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TeachMyAgent: a Benchmark for Automatic Curriculum Learning in Deep RL Paper Website Documentation TeachMyAgent is a testbed platform for Automatic Cu
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Cluster-GCN: An Efficient Algorithm for Training Deep and Large Graph Convolutional Networks This repository contains a TensorFlow implementation of "
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Optimal space decomposition based-product quantization for approximate nearest neighbor search Abstract Product quantization(PQ) is an effective neare
DenseNAS The code of the CVPR2020 paper Densely Connected Search Space for More Flexible Neural Architecture Search. Neural architecture search (NAS)
kNN_From_Scratch I implemented the k nearest neighbors (kNN) classification algorithm on python. This algorithm is used to predict the classes of new
Jupyter notebooks for the code samples of the book "Deep Learning with Python"
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