Object-class detection - Revision history

Klbrain: Merge to Object detection following August 2013 merge proposal; see consensus decision at Talk:Outline of object recognition#Merge?

2016-11-12T14:35:02Z

Merge to Object detection following August 2013 merge proposal; see consensus decision at Talk:Outline of object recognition#Merge?

← Previous revision		Revision as of 14:35, 12 November 2016
Line 1:		Line 1:
			#REDIRECT [[Object detection]] {{R from merge}}
	{{Merge\|Object detection\|target=Outline of object recognition\|discuss=Talk:Outline of object recognition#Merge?\|date=August 2013}}

	'''Object class detection''' is a computer technology that deals with detecting objects of a certain class (such as humans, buildings, or cars) in digital [[image]]s and [[Streaming video\|video]]s. Well-researched domains of object class detection include [[face detection]] and [[pedestrian detection]]. Object class detection has applications in many areas of [[computer vision]], including image retrieval and video surveillance.

	== Applications ==
	It is used in [[face detection]] and [[face recognition]]. It is also used in tracking objects, for example tracking a ball during a football match, tracking movement of a cricket bat, tracking a person in a video.

	== Concept ==
	Every [[Entity\|object]] class has its own special [[feature (computer vision)\|feature]]s that helps in classifying the class – for example all [[circles]] are round.
	Object class detection uses these special features. For example, when looking for circles, objects that are at a particular distance from a point (i.e. the center) are sought. Similarly, when looking for squares, objects that are [[perpendicular]] at corners and have equal side lengths are needed. A similar approach is used for [[face]] identification where eyes, nose, and lips can be found and [[feature (computer vision)\|feature]]s like skin color and distance between eyes can be found.

	== Techniques and algorithms ==
	The advantage we are having is, an [[image]] is made of pixels. So in most cases we know the location of next point, it will be connected to our current pixel.
	Starting with [[circles]], take an image, convert it to gray scale, and detect edges. Move along edges, draw normal, they will intersect at center. Do this for entire circle or find connected edges and calculate [[Euclidean distance]] between center and connected points.
	Another algorithm is move along connected edges rotation of tangent will be uniform, because of symmetry. So whenever there is an abrupt change in [[rotation]], you are out of circle.
	<br />
	For [[squares]], move along edges. First of all check if they are straight lines or not (check if pixels are having either same x or y co-ordinates). After that look for a 90 degree change in angle(if you were moving along a horizontal line then at corner y co-ordinate will stop changing and x will start changing).

	==References==
	*{{cite web\|url=http://vision.eecs.ucf.edu/projects/3D_Model_based_Object_Detection/ObjectDetection.html \|title=Object Class Detection \|publisher=Vision.eecs.ucf.edu \|date= \|accessdate=2013-10-09}}
	*{{cite web\|url=http://www.vision.ee.ethz.ch/publications/get_abstract.cgi?procs=560&mode=&lang=en \|title=ETHZ - Computer Vision Lab: Publications \|publisher=Vision.ee.ethz.ch \|date= \|accessdate=2013-10-09}}

	== External links ==
	* [http://www.mmp.rwth-aachen.de/publications/pdf/mikolajczyk-multiclass-cvpr06.pdf Multiple object class detection]

	== References ==
	<references />

	[[Category:Object recognition and categorization]]

Klbrain: Disambiguated: object → Entity, feature → feature (computer vision)

2016-01-14T15:32:58Z

Disambiguated: object → Entity, feature → feature (computer vision)

← Previous revision		Revision as of 15:32, 14 January 2016
Line 7:		Line 7:

	== Concept ==		== Concept ==
	Every [[object]] class has its own special [[feature (computer vision)\|feature]]s that helps in classifying the class – for example all [[circles]] are round.		Every [[Entity\|object]] class has its own special [[feature (computer vision)\|feature]]s that helps in classifying the class – for example all [[circles]] are round.
	Object class detection uses these special features. For example, when looking for circles, objects that are at a particular distance from a point (i.e. the center) are sought. Similarly, when looking for squares, objects that are [[perpendicular]] at corners and have equal side lengths are needed. A similar approach is used for [[face]] identification where eyes, nose, and lips can be found and [[feature]]s like skin color and distance between eyes can be found.		Object class detection uses these special features. For example, when looking for circles, objects that are at a particular distance from a point (i.e. the center) are sought. Similarly, when looking for squares, objects that are [[perpendicular]] at corners and have equal side lengths are needed. A similar approach is used for [[face]] identification where eyes, nose, and lips can be found and [[feature (computer vision)\|feature]]s like skin color and distance between eyes can be found.

	== Techniques and algorithms ==		== Techniques and algorithms ==

Qwertyus: Disambiguated: features → feature (computer vision)

2015-06-08T19:08:51Z

Disambiguated: features → feature (computer vision)

← Previous revision		Revision as of 19:08, 8 June 2015
Line 7:		Line 7:

	== Concept ==		== Concept ==
	Every [[object]] class has its own special [[~~features~~]] that helps in classifying the class – for example all [[circles]] are round.		Every [[object]] class has its own special [[feature (computer vision)\|feature]]s that helps in classifying the class – for example all [[circles]] are round.
	Object class detection uses these special features. For example, when looking for circles, objects that are at a particular distance from a point (i.e. the center) are sought. Similarly, when looking for squares, objects that are [[perpendicular]] at corners and have equal side lengths are needed. A similar approach is used for [[face]] identification where eyes, nose, and lips can be found and [[feature]]s like skin color and distance between eyes can be found.		Object class detection uses these special features. For example, when looking for circles, objects that are at a particular distance from a point (i.e. the center) are sought. Similarly, when looking for squares, objects that are [[perpendicular]] at corners and have equal side lengths are needed. A similar approach is used for [[face]] identification where eyes, nose, and lips can be found and [[feature]]s like skin color and distance between eyes can be found.

John of Reading: Typo/general fixing, replaced: Euclidian → Euclidean using AWB

2015-04-08T17:08:00Z

Typo/general fixing, replaced: Euclidian → Euclidean using AWB

← Previous revision		Revision as of 17:08, 8 April 2015
Line 12:		Line 12:
	== Techniques and algorithms ==		== Techniques and algorithms ==
	The advantage we are having is, an [[image]] is made of pixels. So in most cases we know the location of next point, it will be connected to our current pixel.		The advantage we are having is, an [[image]] is made of pixels. So in most cases we know the location of next point, it will be connected to our current pixel.
	Starting with [[circles]], take an image, convert it to gray scale, and detect edges. Move along edges, draw normal, they will intersect at center. Do this for entire circle or find connected edges and calculate [[~~Euclidian~~ distance]] between center and connected points.		Starting with [[circles]], take an image, convert it to gray scale, and detect edges. Move along edges, draw normal, they will intersect at center. Do this for entire circle or find connected edges and calculate [[Euclidean distance]] between center and connected points.
	Another algorithm is move along connected edges rotation of tangent will be uniform, because of symmetry. So whenever there is an abrupt change in [[rotation]], you are out of circle.		Another algorithm is move along connected edges rotation of tangent will be uniform, because of symmetry. So whenever there is an abrupt change in [[rotation]], you are out of circle.
	<br />		<br />
	For [[squares]], move along edges. First of all check if they are straight lines or not (check if pixels are having either same x or y co-ordinates). After that look for a 90 degree change in angle(if you were moving along a horizontal line then at corner y co-ordinate will stop changing and x will start changing).		For [[squares]], move along edges. First of all check if they are straight lines or not (check if pixels are having either same x or y co-ordinates). After that look for a 90 degree change in angle(if you were moving along a horizontal line then at corner y co-ordinate will stop changing and x will start changing).

⚫	== External links ==
⚫	* [http://www.mmp.rwth-aachen.de/publications/pdf/mikolajczyk-multiclass-cvpr06.pdf Multiple object class detection]

	==References==		==References==
Line 24:		Line 21:
	*{{cite web\|url=http://www.vision.ee.ethz.ch/publications/get_abstract.cgi?procs=560&mode=&lang=en \|title=ETHZ - Computer Vision Lab: Publications \|publisher=Vision.ee.ethz.ch \|date= \|accessdate=2013-10-09}}		*{{cite web\|url=http://www.vision.ee.ethz.ch/publications/get_abstract.cgi?procs=560&mode=&lang=en \|title=ETHZ - Computer Vision Lab: Publications \|publisher=Vision.ee.ethz.ch \|date= \|accessdate=2013-10-09}}

		⚫	== External links ==

		⚫	* [http://www.mmp.rwth-aachen.de/publications/pdf/mikolajczyk-multiclass-cvpr06.pdf Multiple object class detection]


	== References ==		== References ==
	<references />		<references />

	[[Category:Object recognition and categorization]]		[[Category:Object recognition and categorization]]

12.226.155.11: /* External links */ Fix link to "Multiple object class detection" pdf

2014-03-07T19:08:05Z

External links: Fix link to "Multiple object class detection" pdf

← Previous revision		Revision as of 19:08, 7 March 2014
Line 18:		Line 18:

	== External links ==		== External links ==
	* [http://www.mmp.rwth-aachen.de~~%2Fpublications%2Fpdf%2Fmikolajczyk~~-multiclass-cvpr06.pdf~~&ei=SFEHUrutLsSHrAe-rICQAg&usg=AFQjCNGBFnbUZa99nmlZEzfNStpcLwAPsg&bvm=bv.50500085,d.bmk~~ Multiple object class detection]		* [http://www.mmp.rwth-aachen.de/publications/pdf/mikolajczyk-multiclass-cvpr06.pdf Multiple object class detection]

	==References==		==References==

Lfstevens: Filling in 2 references using Reflinks

2013-10-09T22:12:42Z

Filling in 2 references using Reflinks

← Previous revision		Revision as of 22:12, 9 October 2013
Line 21:		Line 21:

	==References==		==References==
	*~~<ref>[~~http://vision.eecs.ucf.edu/projects/3D_Model_based_Object_Detection/ObjectDetection.html~~]</ref>~~		*{{cite web\|url=http://vision.eecs.ucf.edu/projects/3D_Model_based_Object_Detection/ObjectDetection.html \|title=Object Class Detection \|publisher=Vision.eecs.ucf.edu \|date= \|accessdate=2013-10-09}}
	*~~<ref>[~~http://www.vision.ee.ethz.ch/publications/get_abstract.cgi?procs=560&mode=&lang=en~~]</ref>~~		*{{cite web\|url=http://www.vision.ee.ethz.ch/publications/get_abstract.cgi?procs=560&mode=&lang=en \|title=ETHZ - Computer Vision Lab: Publications \|publisher=Vision.ee.ethz.ch \|date= \|accessdate=2013-10-09}}




			== References ==
			<references />
	[[Category:Object recognition and categorization]]		[[Category:Object recognition and categorization]]

Lfstevens at 22:12, 9 October 2013

2013-10-09T22:12:03Z

← Previous revision		Revision as of 22:12, 9 October 2013
Line 21:		Line 21:

	==References==		==References==
	*http://vision.eecs.ucf.edu/projects/3D_Model_based_Object_Detection/ObjectDetection.html		*<ref>[http://vision.eecs.ucf.edu/projects/3D_Model_based_Object_Detection/ObjectDetection.html]</ref>
	*http://www.vision.ee.ethz.ch/publications/get_abstract.cgi?procs=560&mode=&lang=en		*<ref>[http://www.vision.ee.ethz.ch/publications/get_abstract.cgi?procs=560&mode=&lang=en]</ref>

Malcolma: added Category:Object recognition and categorization; removed {{uncategorized}} using HotCat

2013-09-26T08:43:19Z

added Category:Object recognition and categorization; removed {{uncategorized}} using HotCat

← Previous revision		Revision as of 08:43, 26 September 2013
Line 24:		Line 24:
	*http://www.vision.ee.ethz.ch/publications/get_abstract.cgi?procs=560&mode=&lang=en		*http://www.vision.ee.ethz.ch/publications/get_abstract.cgi?procs=560&mode=&lang=en


	{{uncategorized\|date=August 2013}}

			[[Category:Object recognition and categorization]]

Yobot: WP:CHECKWIKI error fixes using AWB (9494)

2013-09-26T05:20:08Z

WP:CHECKWIKI error fixes using AWB (9494)

← Previous revision		Revision as of 05:20, 26 September 2013
Line 1:		Line 1:
	{{Merge\|Object detection\|target=Outline of object recognition\|discuss=Talk:Outline of object recognition#Merge?\|date=August 2013}}		{{Merge\|Object detection\|target=Outline of object recognition\|discuss=Talk:Outline of object recognition#Merge?\|date=August 2013}}

		⚫	'''Object class detection''' is a computer technology that deals with detecting objects of a certain class (such as humans, buildings, or cars) in digital [[image]]s and [[Streaming video\|video]]s. Well-researched domains of object class detection include [[face detection]] and [[pedestrian detection]]. Object class detection has applications in many areas of [[computer vision]], including image retrieval and video surveillance.

		⚫	== Applications ==
⚫	'''Object class detection''' is a computer technology that deals with detecting objects of a certain class (such as humans, buildings, or cars) in digital [[~~Image\|images~~]] and [[Streaming video\|video]]s. Well-researched domains of object class detection include [[face detection]] and [[pedestrian detection]]. Object class detection has applications in many areas of [[computer vision]], including image retrieval and video surveillance.

⚫	== Applications ==
	It is used in [[face detection]] and [[face recognition]]. It is also used in tracking objects, for example tracking a ball during a football match, tracking movement of a cricket bat, tracking a person in a video.		It is used in [[face detection]] and [[face recognition]]. It is also used in tracking objects, for example tracking a ball during a football match, tracking movement of a cricket bat, tracking a person in a video.

Line 19:		Line 18:

	== External links ==		== External links ==
	[http://www.mmp.rwth-aachen.de%2Fpublications%2Fpdf%2Fmikolajczyk-multiclass-cvpr06.pdf&ei=SFEHUrutLsSHrAe-rICQAg&usg=AFQjCNGBFnbUZa99nmlZEzfNStpcLwAPsg&bvm=bv.50500085,d.bmk Multiple object class detection]		* [http://www.mmp.rwth-aachen.de%2Fpublications%2Fpdf%2Fmikolajczyk-multiclass-cvpr06.pdf&ei=SFEHUrutLsSHrAe-rICQAg&usg=AFQjCNGBFnbUZa99nmlZEzfNStpcLwAPsg&bvm=bv.50500085,d.bmk Multiple object class detection]

	==References==		==References==
Line 25:		Line 24:
	*http://www.vision.ee.ethz.ch/publications/get_abstract.cgi?procs=560&mode=&lang=en		*http://www.vision.ee.ethz.ch/publications/get_abstract.cgi?procs=560&mode=&lang=en

	{{~~cat improve~~\|date=August 2013}}		{{uncategorized\|date=August 2013}}

Jeremy112233: Disambiguated: videos → Streaming video

2013-09-18T20:47:47Z

Disambiguated: videos → Streaming video

← Previous revision		Revision as of 20:47, 18 September 2013
Line 1:		Line 1:

	{{Merge\|Object detection\|target=Outline of object recognition\|discuss=Talk:Outline of object recognition#Merge?\|date=August 2013}}		{{Merge\|Object detection\|target=Outline of object recognition\|discuss=Talk:Outline of object recognition#Merge?\|date=August 2013}}


	'''Object class detection''' is a computer technology that deals with detecting objects of a certain class (such as humans, buildings, or cars) in digital [[Image\|images]] and [[~~videos~~]]. Well-researched domains of object class detection include [[face detection]] and [[pedestrian detection]]. Object class detection has applications in many areas of [[computer vision]], including image retrieval and video surveillance.		'''Object class detection''' is a computer technology that deals with detecting objects of a certain class (such as humans, buildings, or cars) in digital [[Image\|images]] and [[Streaming video\|video]]s. Well-researched domains of object class detection include [[face detection]] and [[pedestrian detection]]. Object class detection has applications in many areas of [[computer vision]], including image retrieval and video surveillance.

	== Applications ==		== Applications ==