Hoshen–Kopelman algorithm: Difference between revisions

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== Percolation theory ==

[[Percolation theory]] is the study of the behavior and [[statistics]] of [[Cluster|clusters]] on [[Lattice graph|lattices]]. Suppose we have a large square lattice where each cell can be occupied with the [[probability]] <code>p</code> and can be empty with the probability <code>1&nbsp;–&nbsp;''p''</code>. Each group of neighboring occupied cells forms a cluster. Neighbors are defined as cells having a common side but not those sharing only a corner i.e. we consider [[Pixel connectivity|4x4 neighborhood.]] that is (top, bottom, left, and right). Each occupied cell is occupied independentlyindependent of the status of its neighborhood. The number of clusters, athe size of each cluster and their distribution are important topics in percolation theory.

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== Hoshen–Kopelman algorithm for cluster finding ==

In this algorithm, we scan through a grid looking for occupied cells and labeling them with cluster labels. The scanning process is called as [[Raster scan|Raster Scan]]. The algorithm begins with scanning the grid cell by cell and check if the cell is occupied, ifor not. If the cell is occupied, then this cellit must be labelledlabeled with a cluster label. This cluster label is decided based on the neighbors of thethat cell. which(For havethis beenwe previouslyare scannedgoing andto labelled,use and[[Union-find ifalgorithm|Union-Find Algorithm]] which is explained in the next section.) If the cell doesn’t have any occupied neighbors then, a new label is assigned to the cell.<ref name=":0" />

== Union-find algorithm ==

The union-findThis algorithm is a simple method for computing [[equivalence class]]es. Calling the function <code>union(x,y)</code> specifies that, items <code>x</code> and <code>y</code> are members of the same equivalence class. Because equivalence relations are [[Transitive relation|transitive]]; all the items equivalent to <code>x</code> are equivalent to all the items equivalent to <code>y</code>. Thus for any item <code>x</code>, there is a set of items which are all equivalent to <code>x</code> are;. thisThis set is the equivalence class of which <code>x</code> is a member. A second function <code>find(x)</code> returns a representative member of the equivalence class to which <code>x</code> belongs.

== Pseudo-code ==

So inDuring the raster[[Raster scan|Raster Scan]] of the grid in question, each timewhenever an occupied cell is encountered, aneighboring checkcells isare donescanned to seecheck whether this cell has any neighboringof cells whothem have already been scanned. If sowe find already scanned neighbors, first athe <code>union</code> operation is performed, to specify that these neighboring cells are in fact members of the same equivalence class. Then a the<code>find</code> operation is performed to find a representative member of that equivalence class with which to label the current cell. Ifwill onbe the other hand, the current cell has no neighbors, it is assigned a new, previously unused, labellabeled. The entire grid is processed in this way. The grid can be raster-scanned a second time, performing only <code>find</code> operations at each cell, to re-label the cells with their final assignment of a representative element.

Following pseudo-code is referred from one of the University of California Berkeley's projects.<ref>{{cite web|url=https://www.ocf.berkeley.edu/~fricke/projects/hoshenkopelman/hoshenkopelman.html |title=The Hoshen-Kopelman Algorithm |website=Ocf.berkeley.edu |date=2004-04-21 |accessdate=2016-09-17}}</ref>

On the other hand,if the current cell has no neighbors, it is assigned a new, previously unused, label. The entire grid is processed in this way.

Following pseudo-code is referred from one[https://www.ocf.berkeley.edu/~fricke/ ofTobin theFricke's] Universityimplementation of Californiathe Berkeley'ssame projectsalgorithm.<ref name=":0">{{cite web|url=https://www.ocf.berkeley.edu/~fricke/projects/hoshenkopelman/hoshenkopelman.html |title=The Hoshen-Kopelman Algorithm |website=Ocf.berkeley.edu |date=2004-04-21 |accessdate=2016-09-17}}</ref>

<strong>Raster Scan and Labeling on the Grid</strong>

largest_label = 0;

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}

<strong>Union</strong>

void union(int x, int y) {

labels[find(x)] = find(y);

}<br>

<strong>Find</strong>

int find(int x) {

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* <code>grid[0][3]</code> is occupied so check cell to the left which is unoccupied so we increment the current label value and assign the label to the cell as <code>2</code>.

* <code>grid[0][4]</code>, <code>grid[0][5]</code> and <code>grid[1][0]</code> are unoccupied so they are not labeled.

* <code>grid[1][1]</code> is occupied so check cell to the left and above, both the cells are unoccupied so assign a new label <code>3</code>.

* <code>grid[1][2]</code> is occupied so check cell to the left and above, only the cell to the left is occupied so assign the label of a cell on the left to this cell <code>3</code>.

* <code>grid[1][3]</code> is occupied so check cell to the left and above, both the cells are occupied, so merge the two clusters and assign the cluster label of the cell above to the cell on the left and to this cell i.e. <code>2</code>. (Merging using union algorithm will label all the cells with label <code>3</code> to <code>2</code>)

* <code>grid[1][4]</code> is occupied so check cell to the left and above, only the cell to the left is occupied so assign the label of a cell on the left to this cell <code>2</code>.

* <code>grid[1][5]</code> , <code>grid[2][0]</code> and <code>grid[2][1]</code> are unoccupied so they are not labeled.

* <code>grid[2][2]</code> is occupied so check cell to the left and above, only cell above is occupied so assign the label of the cell above to this cell <code>2</code>.

* <code>grid[2][3]</code> , <code>grid[2][4]</code> and <code>grid[2][5]</code> are unoccupied so they are not labeled.

* <code>grid[3][0]</code> is occupied so check cell above which is unoccupied so we increment the current label value and assign the label to the cell as <code>4</code>.

* <code>grid[3][1]</code> is occupied so check cell to the left and above, only the cell to the left is occupied so assign the label of the cell on the left to this cell <code>4</code>.

* <code>grid[3][2]</code> is unoccupied so it is not labeled.

* <code>grid[3][3]</code> is occupied so check cell to the left and above, both the cells are unoccupied so assign a new label <code>5</code>.

* <code>grid[3][4]</code> is occupied so check cell to the left and above, only the cell to the left is occupied so assign the label of the cell on the left to this cell <code>5</code>.

* <code>grid[3][5]</code> , <code>grid[4][0]</code> and <code>grid[4][1]</code> are unoccupied so they are not labeled.

* <code>grid[4][2]</code> is occupied so check cell to the left and above, both the cells are unoccupied so assign a new label <code>6</code>.

* <code>grid[4][3]</code> is occupied so check cell to the left and above, both, cell to the left and above are occupied so merge the two clusters and assign the cluster label of the cell above to the cell on the left and to this cell i.e. <code>5</code>. (Merging using union algorithm will label all the cells with label <code>6</code> to <code>5</code>).

* <code>grid[4][4]</code> is unoccupied so it is not labeled.

* <code>grid[4][5]</code> is occupied so check cell to the left and above, both the cells are unoccupied so assign a new label <code>7</code>.

* <code>grid[5][0]</code> , <code>grid[5][1]</code> , <code>grid[5][2]</code> and <code>grid[5][3]</code> are unoccupied so they are not labeled.

* <code>grid[5][4]</code> is occupied so check cell to the left and above, both the cells are unoccupied so assign a new label <code>8</code>.

* <code>grid[5][5]</code> is occupied so check cell to the left and above, both, cell to the left and above are occupied so merge the two clusters and assign the cluster label of the cell above to the cell on the left and to this cell i.e. <code>7</code>. (Merging using union algorithm will label all the cells with label <code>8</code> to <code>7</code>).

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== Applications ==

* Segmentation and Clustering of a [[Binary image|Binary Image]] <ref>{{cite web|url=http://www.scialert.net/qredirect.php?doi=jas.2008.2474.2479&linkid=pdf |format=PDF |title=Journal of Applied Sciences |issn=1812-5654 |website=Scialert.net |accessdate=2016-09-17}}</ref>

* Determination of Nodal Domain Area and Nodal Line Lengths <ref>{{cite web|url=https://webhome.weizmann.ac.il/home/feamit/nodalweek/c_joas_nodalweek.pdf |format=PDF |title=Introduction to the Hoshen-Kopelman algorithm and its application to nodal ___domain statistics |author=Christian Joas |website=Webhome.weizmann.ac.il |accessdate=2016-09-17}}</ref>

* [[Connectivity (graph theory)|Nodal Connectivity Information]]

* Modeling of [[Electrical resistivity and conductivity|electrical conduction.]]

== MoreSee clustering algorithmsAlso ==

* [[K-means clustering|K-means clustering algorithm]]

* [[Fuzzy clustering|Fuzzy clustering algorithm]]

* Gaussian (EM[[Expectation–maximization algorithm|Expectation Maximization]]) clustering algorithm

* Clustering Methods <ref>{{Cite web|url=https://web.stanford.edu/class/cs345a/slides/12-clustering.pdf|title=Clustering|last=|first=|date=|website=|publisher=|access-date=}}</ref>

* C-means Clustering Algorithm <ref>{{Cite web|url=https://sites.google.com/site/dataclusteringalgorithms/fuzzy-c-means-clustering-algorithm|title=Fuzzy c-means clustering|last=|first=|date=|website=|publisher=|access-date=}}</ref>

== References ==