boofcv.alg.segmentation.watershed

Class WatershedVincentSoille1991

java.lang.Object

boofcv.alg.segmentation.watershed.WatershedVincentSoille1991

Direct Known Subclasses:

WatershedVincentSoille1991.Connect4, WatershedVincentSoille1991.Connect8

public abstract class WatershedVincentSoille1991
extends java.lang.Object

Fast watershed based upon Vincient and Soille's 1991 paper [1]. Watershed segments an image using the idea of immersion simulation. For example, the image is treated as a topological map and if you let a droplet of water flow down from each pixel the location the droplets cluster in defines a region. Two different methods are provided for processing the image, a new region is created at each local minima or the user provides an initial seed for each region for it to grow from. The output will be a segmented image with watersheds being assign a value of 0 and each region a value > 0. Watersheds are assigned to pixels which are exactly the same distance from multiple regions, thus it is ambiguous which one it is a member of.

If the image is processed with process(GrayU8) then a new region is created at each local minima and assigned a unique ID > 0. The total number of regions found is returned by getTotalRegions(). This technique will lead to over segmentation on many images.

Initial seeds are provided with a call to process(GrayU8, GrayS32). No new regions will be created. By providing an initial set of seeds over segmentation can be avoided, but prior knowledge of the image is typically needed to create the seeds.

NOTES:

• For faster processing, the internal labeled image has a 1 pixel border around it. If you call getOutput() this border is removed automatically by creating a sub-image.
• Connectivity is handled by child sub-classes. An index of neighbors could have been used, but the additional additional array access/loop slows things down a little bit.
• Watersheds are included. To remove them using RemoveWatersheds
• Pixel values are assumed to range from 0 to 255, inclusive.

[1] Vincent, Luc, and Pierre Soille. "Watersheds in digital spaces: an efficient algorithm based on immersion simulations." IEEE transactions on pattern analysis and machine intelligence 13.6 (1991): 583-598.

Nested Class Summary

Nested Classes

Modifier and Type	Class and Description
static class	WatershedVincentSoille1991.Connect4 Implementation which uses a 4-connect rule
static class	WatershedVincentSoille1991.Connect8 Implementation which uses a 8-connect rule

Field Summary

Fields

Modifier and Type	Field and Description
static int	INIT
static int	MARKER_PIXEL
static int	MASK
static int	WSHED

Constructor Summary

Constructors

Constructor and Description
WatershedVincentSoille1991()

Method Summary

Modifier and Type	Method and Description
GrayS32	getOutput() Segmented output image with watersheds.
GrayS32	getOutputBorder() The entire segmented image used internally.
int	getTotalRegions() Returns the total number of regions labeled.
void	process(GrayU8 input) Perform watershed segmentation on the provided input image.
void	process(GrayU8 input, GrayS32 seeds) Segments the image using initial seeds for each region.
void	removeWatersheds() Removes watershed pixels from the output image by merging them into an arbitrary neighbor.

Methods inherited from class java.lang.Object

equals, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait

Field Detail

WSHED

public static final int WSHED

See Also:

Constant Field Values

INIT

public static final int INIT

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Constant Field Values

MASK

public static final int MASK

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Constant Field Values

MARKER_PIXEL

public static final int MARKER_PIXEL

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Constant Field Values

Constructor Detail

WatershedVincentSoille1991

public WatershedVincentSoille1991()

Method Detail

process

public void process(GrayU8 input)

Perform watershed segmentation on the provided input image. New basins are created at each local minima.

Parameters:

input - Input gray-scale image.

process

public void process(GrayU8 input,
                    GrayS32 seeds)

Segments the image using initial seeds for each region. This is often done to avoid over segmentation but requires additional preprocessing and/or knowledge on the image structure. Initial seeds are specified in the input image 'seeds'. A seed is any pixel with a value > 0. New new regions will be created beyond those seeds. The final segmented image is provided by getOutput().

NOTE: If seeds are used then getTotalRegions() will not return a correct solution.

Parameters:

input - (Input) Input image

seeds - (Output) Segmented image containing seeds. Note that all seeds should have a value > 0 and have a value ≤ numRegions.

getOutput

public GrayS32 getOutput()

Segmented output image with watersheds. This is a sub-image of getOutputBorder() to remove the outside border of -1 valued pixels.

getOutputBorder

public GrayS32 getOutputBorder()

The entire segmented image used internally. This contains a 1-pixel border around the entire image filled with pixels of value -1.

removeWatersheds

public void removeWatersheds()

Removes watershed pixels from the output image by merging them into an arbitrary neighbor.

getTotalRegions

public int getTotalRegions()

Returns the total number of regions labeled. If watersheds have not been removed then this will including the watershed.

THIS IS NOT VALID IF SEEDS ARE USED!!!

Returns:

number of regions.