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Object detection

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+#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 &ndash; 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]]