Tutorial Image Segmentation - Revision history

Peter at 04:02, 8 December 2016

2016-12-08T04:02:48Z

← Older revision		Revision as of 04:02, 8 December 2016
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	* [[Example_Binary_Image\|Example Binary Image]]		* [[Example_Binary_Image\|Example Binary Image]]
	* [[Applet_Binary_Operations\|Applet Binary Operations]]
	* [https://github.com/lessthanoptimal/BoofCV/blob/master/examples/src/boofcv/examples/segmentation/ExampleThresholding.java Example Thresholding]		* [https://github.com/lessthanoptimal/BoofCV/blob/master/examples/src/boofcv/examples/segmentation/ExampleThresholding.java Example Thresholding]

Peter at 13:14, 28 March 2016

2016-03-28T13:14:44Z

← Older revision		Revision as of 13:14, 28 March 2016
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	<syntaxhighlight lang="java">		<syntaxhighlight lang="java">
	ImageSuperpixels alg = FactoryImageSegmentation.fh04(new ConfigFh04(100,30), imageType);		ImageSuperpixels alg = FactoryImageSegmentation.fh04(new ConfigFh04(100,30), imageType);
	~~ImageSInt32~~ pixelToSegment = new ~~ImageSInt32~~(color.width,color.height);		GrayS32 pixelToSegment = new GrayS32(color.width,color.height);
	alg.segment(color,pixelToSegment);		alg.segment(color,pixelToSegment);
	</syntaxhighlight>		</syntaxhighlight>

Peter at 23:57, 15 September 2014

2014-09-15T23:57:50Z

← Older revision		Revision as of 23:57, 15 September 2014
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	=== Examples ===		=== Examples ===

	* [[~~Example_Superpixels~~\|Example ~~Superpixels~~]]		* [[Example_Color_Segmentation\|Example Color Segmentation]]

	= Superpixels =		= Superpixels =

Peter at 23:21, 14 September 2014

2014-09-14T23:21:02Z

← Older revision		Revision as of 23:21, 14 September 2014
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	</center>		</center>

	Next up is color based image segmentation. Color based segmentation assigns pixels to a region based ~~on their~~ color alone~~. Color based segmentation could be used to separate a road from glass~~. In the above example all the pixels which are approximately yellow are saved and the rest are set to black.		Next up is color based image segmentation. Color based segmentation assigns pixels to a region based color information alone. In the above example all the pixels which are approximately yellow are saved and the rest are set to black.

	BoofCV does not provide a highlevel interface for color based segmentation, but does provide ~~most of~~ the tools you will need implement it yourself. The color space in which segmentation is performed ~~and affect its effectiveness~~. ~~For example, using a~~ color space ~~which~~ is invariant to ~~intensity (such as~~ HSV) can allow for a ~~single threshold~~ to be used ~~as an object moves in and out of shadow~~. ~~An example~~ (see below) ~~is provided~~ which does this. ~~You~~ can click on the image and it will segment out all pixels which have a similar appearance.		BoofCV does not provide a highlevel interface for color based segmentation, but does provide the tools you will need implement it yourself. The color space in which segmentation is performed is important. The most common color space, RGB, is not invariant to changes in lighting, while HSV is. This can allow for a simpler function to be used. Example code is provided (see below) which does exactly this. When the example code is run, you can click on the image and it will segment out all pixels which have a similar appearance.

	In the video below, color segmentation was used to identify the road in a simulated environment and steer the car.		In the video below, color segmentation was used to identify the road in a simulated environment and steer the car.

Peter at 19:39, 12 September 2014

2014-09-12T19:39:45Z

← Older revision		Revision as of 19:39, 12 September 2014
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	</center>		</center>

	The most basic way to threshold a gray scale image is by thresholding. Thresholding works b ~~yby~~ assigning 1 to all pixels below/above an intensity value to 1 and the rest 0. Typically the pixels which are assigned a value of 1 are an object of interest. BoofCV provides both global and adaptive (local) thresholding capabilities. The images above demonstrate a global threshold being used on a TEM image with particles.		The most basic way to threshold a gray scale image is by thresholding. Thresholding works b by assigning 1 to all pixels below/above an intensity value to 1 and 0 to the rest. Typically the pixels which are assigned a value of 1 are an object of interest. BoofCV provides both global and adaptive (local) thresholding capabilities. The images above demonstrate a global threshold being used on a TEM image with particles.

	Global thresholding is extremely fast, but it can be unclear what a good threshold is. BoofCV provides several methods for automatically computing global thresholds based on different theories. Otsu works by minimizing the spread of background and foreground pixels. Entropy maximizes the entropy between the foreground and background regions. The image's mean can also be quickly computed. The code below demonstrates how to compute and apply different global thresholds.		Global thresholding is extremely fast, but it can be unclear what a good threshold is. BoofCV provides several methods for automatically computing global thresholds based on different theories. Otsu works by minimizing the spread of background and foreground pixels. Entropy maximizes the entropy between the foreground and background regions. The image's mean can also be quickly computed. The code below demonstrates how to compute and apply different global thresholds.
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	</center>		</center>

	Next up is color based image segmentation. ~~In this example you can click on a pixel and it will find all~~ pixels ~~with~~ a ~~similar~~ color. ~~This is done by convert~~ the ~~image into a color space~~ which ~~is independent of intensity, HSV,~~ and ~~then selecting pixels which~~ are ~~similar~~.		Next up is color based image segmentation. Color based segmentation assigns pixels to a region based on their color alone. Color based segmentation could be used to separate a road from glass. In the above example all the pixels which are approximately yellow are saved and the rest are set to black.

	~~One application includes identifying~~ a ~~road~~ for an ~~autonomous vehicle~~ to ~~drive along~~.		BoofCV does not provide a highlevel interface for color based segmentation, but does provide most of the tools you will need implement it yourself. The color space in which segmentation is performed and affect its effectiveness. For example, using a color space which is invariant to intensity (such as HSV) can allow for a single threshold to be used as an object moves in and out of shadow. An example (see below) is provided which does this. You can click on the image and it will segment out all pixels which have a similar appearance.

			In the video below, color segmentation was used to identify the road in a simulated environment and steer the car.
			* [https://www.youtube.com/watch?v=CTX-Aty1rcA Virtual Road Segmentation]

			=== Examples ===

			* [[Example_Superpixels\|Example Superpixels]]

	= Superpixels =		= Superpixels =

Peter at 17:28, 12 September 2014

2014-09-12T17:28:48Z

← Older revision		Revision as of 17:28, 12 September 2014
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	= Color Histogram =		= Color Histogram =

			<center>
			<gallery heights=150 widths=400 >
			File:Sunflowers.jpg\| Input sunflower image.
			File:Example_color_segment_yellow.jpg\|Image segmented using the color yellow.
			</gallery>
			</center>

	Next up is color based image segmentation. In this example you can click on a pixel and it will find all pixels with a similar color. This is done by convert the image into a color space which is independent of intensity, HSV, and then selecting pixels which are similar.		Next up is color based image segmentation. In this example you can click on a pixel and it will find all pixels with a similar color. This is done by convert the image into a color space which is independent of intensity, HSV, and then selecting pixels which are similar.
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	=== Superpixel API ===		=== Superpixel API ===

	* [https://github.com/lessthanoptimal/BoofCV/blob/master/main/ip/src/boofcv/~~alg~~/~~filter~~/~~binary~~/~~GThresholdImageOps~~.java ~~FactoryImageSegmentation~~]		* [https://github.com/lessthanoptimal/BoofCV/blob/master/main/feature/src/boofcv/factory/segmentation/FactoryImageSegmentation.java FactoryImageSegmentation]
	* [https://github.com/lessthanoptimal/BoofCV/blob/master/main/ip/src/boofcv/alg/~~filter~~/~~binary~~/~~ThresholdImageOps~~.java ~~ImageSuperpixels~~]		* [https://github.com/lessthanoptimal/BoofCV/blob/master/main/feature/src/boofcv/abst/segmentation/ImageSuperpixels.java ImageSuperpixels]
			* [https://github.com/lessthanoptimal/BoofCV/blob/master/main/feature/src/boofcv/alg/segmentation/ImageSegmentationOps.java ImageSegmentationOps]
			* [https://github.com/lessthanoptimal/BoofCV/blob/master/main/visualize/src/boofcv/gui/feature/VisualizeRegions.java VisualizeRegions]

Peter at 17:13, 12 September 2014

2014-09-12T17:13:51Z

← Older revision		Revision as of 17:13, 12 September 2014
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	= Image Segmentation and Superpixels in BoofCV =		= Image Segmentation and Superpixels in BoofCV =

	This article provides an overview of image segmentation and superpixels in BoofCV. Image segmentation is a problem in which an image is partitioned into groups of related pixels. These pixel groups can then be used to identify objects and reduce the complexity of image processing. Superpixels are a more specific type of segmentation where the partitions ~~form~~ connected clusters.		This article provides an overview of image segmentation and superpixels in BoofCV. Image segmentation is a problem in which an image is partitioned into groups of related pixels. These pixel groups can then be used to identify objects and reduce the complexity of image processing. Superpixels are a more specific type of segmentation where the partitions are connected clusters.

	The following topics will be covered:		The following topics will be covered:
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	[[File:tutorial_superpixel_border.jpg\|frame\|center\|Superpixel Borders A) SLIC B) Felzenszwalb-Huttenlocher (FH04) C) Mean-Shift D) Watershed]]		[[File:tutorial_superpixel_border.jpg\|frame\|center\|Superpixel Borders A) SLIC B) Felzenszwalb-Huttenlocher (FH04) C) Mean-Shift D) Watershed]]

	Superpixels have ~~been gaining popularity recently~~. ~~BoofCV contains implementations~~ of ~~several recently developed~~ algorithms.		Superpixels are sets of connected pixels which have similar features. The border region between superpixels tends to lie along the edges of shapes. Different algorithms will produce superpixels of different shapes, see figure above. For example, SLIC produces regions which are approximately the same size while Felzenzwalb-Huttenlocker (FH) and Mean-Shift produce a variable number of regions depent on the scene's complexity.

	~~Mean-Shift~~		One of the earlier superpixel algorithms is watershed. The idea behind watershed is that the image is viewed as a topological map where pixel intensity is the pixel's height. If you placed a waterdroplet in any of pixels it would fall down towards a local minimum. All pixels which fall inside the same local minimum belong to the same superpixel. While very fast, it tends to over segment images.
	~~SLIC~~
	~~Felzenszwalb-Huttenlocher~~
	~~Watershed~~

	~~Which one~~ is the ~~best is highly~~ application ~~dependent~~. ~~Here is application included with BoofCV which allows you~~ to ~~experiment and play with different parameters~~.		To overcome watershed's oversegmentation issue you can provide a seed to initialize each region. How you generate the seed is dependent on the application. In the image below seeds were found using a blob detector. If seeds were not used then there would be thousands more regions due to the amount of noise in the image.

	(~~go through~~ a ~~few examples)~~		[[File:Example_Watershed_with_Seeds.jpg\|thumb\|400px\|center\|Watersheed with seeds prevents oversegmentation]]

			A code example is shown below demonstrating superpixel segmentation using the ImageSuperpixels highlevel interface. FactoryImageSegmentation should be used to create new instances of ImageSuperpixels.
			<syntaxhighlight lang="java">
			ImageSuperpixels alg = FactoryImageSegmentation.fh04(new ConfigFh04(100,30), imageType);
			ImageSInt32 pixelToSegment = new ImageSInt32(color.width,color.height);
			alg.segment(color,pixelToSegment);
			</syntaxhighlight>


			<center>
			'''Superpixel Summary Table'''
			{\| class="wikitable"
			! Name !! Count !! Color !! FPS
			\|-
			\|Mean-Shift \|\| Variable \|\| Color \|\| 0.5
			\|-
			\| \|\| \|\| Gray \|\| 0.8
			\|-
			\|SLIC \|\| Fixed \|\| Color \|\| 1.8
			\|-
			\| \|\| \|\| Gray \|\| 2.4
			\|-
			\|Felzenszwalb-Huttenlocher \|\| Variable \|\| Color \|\| 5.1
			\|-
			\| \|\| \|\| Gray \|\| 5.2
			\|-
			\|Watershed \|\| Both \|\| Gray \|\| 43
			\|}
			''FPS'' (Frames Per Second)was found using a 481x321 image on a Intel Core i7-2600 3.4 Ghz, single thread. ''Count'' is the number of regions it will find in an image. ''Color'' indicates if a color or gray image was processed.
			</center>

			=== Examples ===

			* [[Example_Superpixels\|Example Superpixels]]

			=== Superpixel API ===

			* [https://github.com/lessthanoptimal/BoofCV/blob/master/main/ip/src/boofcv/alg/filter/binary/GThresholdImageOps.java FactoryImageSegmentation]
			* [https://github.com/lessthanoptimal/BoofCV/blob/master/main/ip/src/boofcv/alg/filter/binary/ThresholdImageOps.java ImageSuperpixels]

Peter at 02:22, 26 August 2014

2014-08-26T02:22:51Z

← Older revision		Revision as of 02:22, 26 August 2014
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	= Superpixels =		= Superpixels =

			[[File:tutorial_superpixel_border.jpg\|frame\|center\|Superpixel Borders A) SLIC B) Felzenszwalb-Huttenlocher (FH04) C) Mean-Shift D) Watershed]]

	Superpixels have been gaining popularity recently. BoofCV contains implementations of several recently developed algorithms.		Superpixels have been gaining popularity recently. BoofCV contains implementations of several recently developed algorithms.

Peter at 15:37, 24 August 2014

2014-08-24T15:37:04Z

← Older revision		Revision as of 15:37, 24 August 2014
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	<center>		<center>
	<gallery heights=200 widths=600>		<gallery caption="Variable Lighting" heights=200 widths=600>
	File:VariableLight_Otsu_Square.jpg\|Calibration grid ~~with variable lighting~~. Left: original, Middle: Otsu, Right: Adaptive Square		File:VariableLight_Otsu_Square.jpg\|Calibration grid. ''Left:'' original, ''Middle:'' Global Otsu, ''Right:'' Adaptive Square
	</gallery>		</gallery>
	</center>		</center>
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	<center>		<center>
	<gallery heights=200 widths=600>		<gallery caption="Difficult Text Example" heights=200 widths=600>
	File:Text_square_sauvola.jpg\|~~Complex text sample.~~ Left: Original, Middle: Adaptive Square, Right: Sauvola		File:Text_square_sauvola.jpg\|''Left:'' Original, ''Middle:'' Adaptive Square, ''Right:'' Sauvola
	</gallery>		</gallery>
	</center>		</center>
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	<center>		<center>
	<gallery caption="~~Binary Object~~ Extraction" heights=150 widths=200 >		<gallery caption="Blob Extraction" heights=150 widths=200 >
	Image:example_binary_labeled.png\|Labeled Binary Image		Image:example_binary_labeled.png\|Labeled Binary Image
	</gallery>		</gallery>

Peter at 08:08, 23 August 2014

2014-08-23T08:08:20Z

← Older revision		Revision as of 08:08, 23 August 2014
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	* [[Example_Binary_Image\|Example Binary Image]]		* [[Example_Binary_Image\|Example Binary Image]]
	* [[Applet_Binary_Operations Applet Binary Operations]]		* [[Applet_Binary_Operations\|Applet Binary Operations]]
	* [https://github.com/lessthanoptimal/BoofCV/blob/master/examples/src/boofcv/examples/segmentation/ExampleThresholding.java Example Thresholding]		* [https://github.com/lessthanoptimal/BoofCV/blob/master/examples/src/boofcv/examples/segmentation/ExampleThresholding.java Example Thresholding]