max edge distanceSimplificationkeyFigure 4-1: A sample process for the Douglas-Peucker algorithmThe Douglas-Peucker algorithm is for the selection of representative points to simplify a curvecomposed of line segments. It uses a point-to-edge distance tolerance. The algorithm starts with a crudesimplification that is the single edge joining the first and last vertices of the original polyline. It thencomputes the perpendicular distance of all intermediate vertices to that edge. The vertex that is furthestaway from that edge, and that has a computed distance that is larger than a specified tolerance, will bemarked as a key and added to the simplification. This process will recurse for each edge in the currentsimplification until all vertices of the original polyline are within tolerance of the simplification results.This process is illustrated in Figure 4-1. (4) The following program is a non-recursive solution. Fill out the numbered blanks in the programand highlight your answers with the corresponding numbers.# Assume all points are not selected at the beginningdef DouglasPeuker_NR(points, threshold):stack = Stack () # initialize a stack# push a tuple indicating the index of the starting and ending pointstack.push((0, _®_))while note = stack.pop()ifO_:points[e[0]].selected= Truepoints[e[1]]. selected = Trueelse:maxIdx, maxDist = farthestPoint (points[_@_:,6_])maxIdx += e[0]if maxDist >= threshold:stack.push((_®_, maxIdx))stack.push((_O_, e[1]))else:points[e[0]].selected = Truepoints[e[1]].selected= True

Working of a Douglas Peuskar Algorithm

Answered: The following program is a…

The following program is a non-recursive solution. Fill out the numbered blanks in the program and highlight your answers with the corresponding numbers.

Database System Concepts

7th Edition

ISBN:9780078022159

Author:Abraham Silberschatz Professor, Henry F. Korth, S. Sudarshan

Publisher:Abraham Silberschatz Professor, Henry F. Korth, S. Sudarshan

Chapter1: Introduction

Section: Chapter Questions

Problem 1PE

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Concept explainers

Computational Systems

The computation systems can be defined as the systems that are capable of solving a problem that includes calculations either mathematical or logical, and are able to produce the result as an output. For example, a simple calculator that is given a set of numbers and operators as input and produces the result as an output can be defined as a computational system. The above example is a simple illustration only, the computational systems range from simple devices to complex devices such as the systems that control the SpaceX companies, Starlink satellites which are being quite recently seen in the skies at many places globally.

Question

max edge distance
Simplification
key
Figure 4-1: A sample process for the Douglas-Peucker algorithm
The Douglas-Peucker algorithm is for the selection of representative points to simplify a curve
composed of line segments. It uses a point-to-edge distance tolerance. The algorithm starts with a crude
simplification that is the single edge joining the first and last vertices of the original polyline. It then
computes the perpendicular distance of all intermediate vertices to that edge. The vertex that is furthest
away from that edge, and that has a computed distance that is larger than a specified tolerance, will be
marked as a key and added to the simplification. This process will recurse for each edge in the current
simplification until all vertices of the original polyline are within tolerance of the simplification results.
This process is illustrated in Figure 4-1.

(4) The following program is a non-recursive solution. Fill out the numbered blanks in the program
and highlight your answers with the corresponding numbers.
# Assume all points are not selected at the beginning
def DouglasPeuker_NR(points, threshold):
stack = Stack () # initialize a stack
# push a tuple indicating the index of the starting and ending point
stack.push((0, _®_))
while not
e = stack.pop()
if
O_:
points[e[0]].selected
= True
points[e[1]]. selected = True
else:
maxIdx, maxDist = farthestPoint (points[_@_:,
6_])
maxIdx += e[0]
if maxDist >= threshold:
stack.push((_®_, maxIdx))
stack.push((_O_, e[1]))
else:
points[e[0]].selected = True
points[e[1]].selected
= True

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