Mathematics & Computing · Fractal Geometry
Quantify the Complexity Hidden in Nature
An interactive scientific tool for computing fractal dimensions of natural patterns using the box-counting method — underpinned by original academic research in fractal geometry.
From Image to Fractal Dimension in 4 Steps
A rigorous OpenCV pipeline runs server-side for reproducibility and scientific accuracy.
Preprocess
Upload any image. Choose Full Mask, Boundary (Canny edge), or Texture (morphological gradient) extraction mode.
Threshold
Otsu auto-thresholding, adaptive localized, or manual — converts to a precise binary mask.
Box Count
Non-empty boxes counted across scales (powers of 2) with 4 grid offsets for maximum accuracy.
Regress
Log-log linear regression yields D and R². Confidence intervals and sensitivity analysis validate every result.
Built for Scientific Rigour
Reliability Dashboard
Every analysis returns a quality score (0–100), 95% confidence interval, standard error, and threshold sensitivity test.
Specimen Comparison Engine
Overlay your log-log regression line against 11 dissertation specimens on a shared auto-scaled D3 chart. ΔD computed instantly.
Algorithm Microscope
Watch the box-counting grid render live on the binary image at every scale. Step through each counting level interactively.
Multi-Mode Preprocessing
3 analysis modes × 3 threshold methods = 9 preprocessing combinations. Auto-reanalysis fires on every change with 600ms debounce.
Built With
Production-grade tools chosen for reproducibility and performance.
Frontend
Backend
Data Flow Architecture
Ready to Analyze Your First Image?
Upload a leaf, coastline, or any natural pattern. Results in seconds.