Related Issues (20)
- See if we can do without FFTs in the PCE computation HOT 3
- Investigate if there is a 'best' way to convert similarity to distance for our similarity metrics
- Do a qualitative comparison of the similarity metrics HOT 1
- Find out what the best metric is to evaluate the clusterings that we create
- Use flat clustering algorithms from sklearn to cluster the patterns HOT 2
- See if we can somehow use T-SNE and other new clustering algorithms
- Use hierarchical clustering using scipy.cluster.hierarchy to cluster the patterns
- Investigate layered clustering, say first on camera brand, then model, then physical copies
- Implement the heuristics and final clustering algorithm into the Java program
- Create a GPU implementation for PCE without FFTs, if possible HOT 1
- Compute similarity scores for multiple rotations of the patterns HOT 1
- Create a simple GPU implementation for the NCC computation HOT 7
- Create a faster GPU kernel for the NCC implementation
- use DirectBuffers for CPU-GPU data transfers
- Create a command-line Python tool for clustering
- Use docopt instead of argparse for CLI
- Write clustit documentation in the README
- release clustit with Zenodo
- important _notice
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