diff --git a/Pipline.md b/Pipline.md index 4632a00..d9267d2 100644 --- a/Pipline.md +++ b/Pipline.md @@ -54,25 +54,25 @@ Two points belong to the same cluster only if: The T-DBSCAN algorithm is optimized with **KD-Tree**, this will help find the nearest points faster, if further optimizations are needed then we should look into improving the **Cash Hits** and maybe change the algorithm to use **SIMD** and **Multi-Threading**. # Douglas-Peucker Simplification -The Douglas–Peucker Simplification algorithm (often called the Ramer–Douglas–Peucker algorithm) is a technique used in computational geometry to reduce the number of points in a curve or line while preserving its overall shape. +The Douglas-Peucker Simplification algorithm (often called the Ramer-Douglas-Peucker algorithm) is a technique used in computational geometry to reduce the number of points in a curve or line while preserving its overall shape. Imagine you have a line made of many points (like a GPS path or a drawn shape). The algorithm removes unnecessary points so the line becomes simpler—but still looks almost the same. -Ramer–Douglas–Peucker_algorithm +Ramer-Douglas-Peucker_algorithm ### Why it's needed? Having hours of **GPS records** will slow down OSRM Map Matching, so to speed it up, we reduce and simplify number of points, using line simplification algorithm. We choosed Ramer-Douglas-Peucker Algorithm as it the most common algorithm but there is other like: -- Visvalingam–Whyatt -- Reumann–Witkam +- Visvalingam-Whyatt +- Reumann-Witkam - Opheim simplification - Lang simplification -- Zhao–Saalfeld +- Zhao-Saalfeld - Imai-Iri ### How it works (step-by-step) -Ramer–Douglas–Peucker_algorithm +Ramer-Douglas-Peucker_algorithm - Start with endpoints - Take the first and last point of the line segment.