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.
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已经将项目的关键文件上传，包含微博爬虫、LDA主题分析和情感分析三个部分。 1.微博爬虫 实现微博评论爬取和微博用户信息爬取，一天大概十万条。 2.LDA主题分析 实现文档主题抽取，包括数据清洗及分词、主题数的确定（主题一致性和困惑度）和最优主题模型的选择（暴力搜索）。 3.情感分析 实现评论文本的
Multi-label Classification with Partial Annotations using Class-aware Selective Loss Paper | Pretrained models Official PyTorch Implementation Emanuel
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AdaConv — Simple TensorFlow Implementation [Paper] : Adaptive Convolutions for Structure-Aware Style Transfer (CVPR 2021) Note This repository does no
Code for Active Learning at The ImageNet Scale. This repository implements many popular active learning algorithms and allows training with torch's DDP.
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