connectedComponents: Find Connected Components in a binary Image
In swarm-lab/Rvision: Computer Vision Library for R

connectedComponents

R Documentation

Find Connected Components in a binary Image

Description

connectedComponents computes the connected components (i.e. areas of contiguous non-zero pixels) of a binary image.

Usage

connectedComponents(
  image,
  connectivity = 8,
  algorithm = "bolelli",
  table = TRUE,
  stats = TRUE,
  target = "new"
)

Arguments

`image`	An an 8-bit (8U) single-channel `Image` object.
`connectivity`	The connectivity neighborhood to decide whether 2 pixels are contiguous. This parameter can take two values: 4: the neighborhood of a pixel are the four pixels located above (north), below (south), to the left (west) and right (east) of the pixel. 8 (the default): the neighborhood of a pixel includes the four 4-neighbors and the four pixels along the diagonal directions (northeast, northwest, southeast, and southwest).
`algorithm`	A character string specifying the connected components labeling algorithm to use. This parameter can take six values: "grana"/"BBDT": BBDT algorithm for 8-way connectivity, SAUF algorithm for 4-way connectivity. "wu"/"SAUF": SAUF algorithm for 8-way connectivity, SAUF algorithm for 4-way connectivity. "bolelli"/"spaghetti" (the default): Spaghetti algorithm for 8-way connectivity, Spaghetti4C algorithm for 4-way connectivity.
`table`	A boolean indicating whether the coordinates of the pixels of each component should be returned.
`stats`	A boolean indicating whether the statistics of the connected components should be returned.
`target`	The location where the results should be stored. It can take 2 values: "new": a new `Image` object is created and the results are stored inside (the default). An `Image` object: the results are stored in another existing `Image` object. In this case, `target` must be a single channel image with a 16U or 32S bit depth. Note that this will replace the content of `target`.

Value

A list with 1 to 4 items:

n:: the number of connected components in the image. It is always returned.
table:: if table=TRUE, a matrix with 3 columns representing the identity of the connected components (label), and the x-y coordinates of the pixels they are composed of.
stats:: if stats=TRUE, a matrix with 8 columns representing the identity of the connected components (label), the x-y coordinates of their centroidd, the left and top coordinates of their bounding boxes, the width and height of their bounding boxes, and their surface areas in pixels.
labels:: if target="new" a 32S single-channel image in which each pixel of each connected component is represented by the identity number of the component, and the background pixels by zero.

Author(s)

Simon Garnier, garnier@njit.edu

References

Costantino Grana, Daniele Borghesani, and Rita Cucchiara. Optimized Block-Based Connected Components Labeling With Decision Trees. IEEE Transactions on Image Processing, 19(6):1596–1609, 2010.

Kesheng Wu, Ekow Otoo, and Kenji Suzuki. Optimizing two-pass connected-component labeling algorithms. Pattern Analysis and Applications, 12(2):117–135, Jun 2009.

Federico Bolelli, Michele Cancilla, and Costantino Grana. Two More Strategies to Speed Up Connected Components Labeling Algorithms. In Image Analysis and Processing - ICIAP 2017, volume 10485, pages 48–58. Springer, 2017.

Federico Bolelli, Stefano Allegretti, Lorenzo Baraldi, and Costantino Grana. Spaghetti Labeling: Directed Acyclic Graphs for Block-Based Connected Components Labeling. IEEE Transactions on Image Processing, 29(1):1999–2012, 2019.

Federico Bolelli, Stefano Allegretti, and Costantino Grana. One dag to rule them all. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2021.

Examples

dots <- image(system.file("sample_img/dots.jpg", package = "Rvision"))
dots_gray <- changeColorSpace(dots, "GRAY")
dots_bin <- dots_gray < 200
cc <- connectedComponents(dots_bin)

swarm-lab/Rvision documentation built on Aug. 4, 2024, 2:29 p.m.