:pushpin: State of the art point location and neighbour finding algorithms for region quadtrees, in Go
Region quadtrees and efficient neighbour finding techniques in Go
Go-rquad proposes various implementations of region quadtrees.
A region quadtree is a special kind of quadtree that recursively subdivides a 2 dimensional space into 4 smaller and generally equal rectangular regions, until the wanted quadtree resolution has been reached, or no further subdivisions can be performed.
Region quadtrees can be used for image processing; in this case a leaf node represents a rectangular region of an image in which all colors are equal or the color difference is under a given threshold.
Region quadtrees may also be used to represent data fields with variable resolution. For example, the temperatures in an area may be stored as a quadtree where each leaf node stores the average temperature over the subregion it represents.
In this package, quadtrees implement the imgscan.Scanner
interface,
this provides a way to scan (i.e extract) the pixels in order to perform the subdivisions.
Node
interfacetype Node interface {
Parent() Node
Child(Quadrant) Node
Bounds() image.Rectangle
Color() Color
Location() Quadrant
}
Quadtree
interfaceA Quadtree
represents a hierarchical collection of Node
s, its API is
simple: access to the root Node and a way to iterate over all the leaves.
type Quadtree interface {
ForEachLeaf(Color, func(Node))
Root() Node
}
Locate
returns the leaf node of q
that contains pt
, or nil if q
doesn't contain pt
.
func Locate(q Quadtree, pt image.Point) Node
ForEachNeighbour
calls fn
for each neighbour of n
.
func ForEachNeighbour(n Node, fn func(Node))
BasicTree
and basicNode
BasicTree
is in many ways the standard implementation of Quadtree
, it just does the job.
CNTree
and CNNode
CNTree
or Cardinal Neighbour Quadtree implements state of the art techniques:
Bottom-up neighour finding technique. cf Hanan Samet 1981,
Neighbor Finding Techniques for Images Represented by Quadtrees, paper
Cardinal Neighbor Quadtree. cf Safwan Qasem 2015,
Cardinal Neighbor Quadtree: a New Quadtree-based Structure for Constant-Time Neighbor Finding, paper
Fast point location using binary branching method. cf Frisken, Perry 2002
Simple and Efficient Traversal Methods for Quadtrees and Octrees, paper
go-rquad is open source software distributed in accordance with the MIT License, which says:
Copyright (c) 2016 Aurélien Rainone
Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:
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