TODO: Add random sampling to patch-based training

Added note for future enhancement: mix salient + random samples.

Rationale:
- Salient point detection focuses on edges/corners
- Random samples improve generalization across entire image
- Prevents overfitting to only high-gradient regions

Proposed implementation:
- Default: 90% salient points, 10% random samples
- Configurable: --random-sample-percent parameter
- Example: 64 patches = 58 salient + 6 random

Location: train_cnn_v2.py
- TODO in _detect_salient_points() method
- TODO in argument parser

Co-Authored-By: Claude Sonnet 4.5 <noreply@anthropic.com>

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