Sparse Matrix Operations for Images

Implement sparse matrix operations for efficient image processing using scipy.sparse.

Many image operations produce sparse results (few non-zero values). Sparse representation saves memory and speeds up computation.

Sparse format (COO - Coordinate format):

• Store only (row, col, value) for non-zero entries
• Memory: O(nnz) instead of O(m×n)

Common sparse operations:

• Conversion: dense ↔ sparse
• Matrix-vector multiplication
• Element access and modification

Applications in CV:

• Graph-based segmentation (sparse affinity matrices)
• Optical flow (sparse motion fields)
• Feature matching (sparse correspondence matrices)