Description
Wavelet transform has many applications such as image watermarking, feature extraction, and so on.
In this product, we collect 30 sample python code about the wavelet transform.
If you want to use the wavelet transform in your project or thesis, this collection helps you to get more familiar with using WT.
Some topics :
- image compression
- genre classification task
- Facial recognition
- CWT based wavelet denoising
- Solving the partial differential equations
- EEG-based Emotion Classification
- Image by the content categorization
- Cat Classification
- Stock return prediction
- Noise Removal
- Fast Wavelet-Based Visual Classification
- Segmenting using 2D Discrete Wavelet Transform (DWT) and conducting temporary experiments, i.e. doing a temporary classification with global feature extraction results. With the results of the global feature extraction, testing is done to several existing machine learning models, including are Logistic Regression, LDA, KNN, Decision Tree, Gaussian Naive Bayes, Random Forest, and SVM.
- Wavelet Deep Neural Network
- Use scene classification (Text watermark, Image watermark, Audio watermark, Video watermark), PSNR: Peak signal-to-noise ratio, used to measure the quality of the image after watermarking, the larger the number, the better, more than 35, it is difficult to see the difference with the naked eye.
- Music genre classifier ( a system that predicts the genre of a song)
- Classic algorithms of image fusion (Image Fusion based wavelet, Image fusion based PCA, arithmetic average method)
- Smoke Detection (haralick texture features)
- Compressive or compressed sensing
- Feature extraction based on DWT.
- Dense estimation of 2D/3D optical flow on wavelet bases
- Feature extraction from MultiObject Spectrograph data
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