en>BG19bot: WP:CHECKWIKI error fix for #64. Do general fixes if a problem exists. - using AWB

2014-06-05T07:15:51Z

WP:CHECKWIKI error fix for #64. Do general fixes if a problem exists. - using AWB

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	~~'''Haar-like features''' are [[digital image]] [[feature (computer vision)\|feature]]s used in [[object recognition]]. They owe their~~ name ~~to their intuitive similarity with [[Haar wavelet]]s~~ and ~~were used in the first real-time face detector{{citation needed\|date=October 2013}}~~.		Claude is her name and she completely digs that title. One of the things I love most is climbing and now I have time to consider on new things. Her husband and her selected to reside in Alabama. Bookkeeping is what she does.<br><br>Here is my blog post auto warranty ([http://prolab.97048.info/UserProfile/tabid/968/userId/12295/Default.aspx simply click the following internet site])

	~~Historically, working with only image intensities (i.e., the [[RGB]] [[pixel]] values at each and every pixel~~ of ~~image) made~~ the ~~task of feature calculation [[computationally expensive]]. A publication by Papageorgiou et al.<ref>Papageorgiou, Oren~~ and Poggio, "A general framework for object detection", International Conference on Computer Vision, 1998.</ref> discussed working with an alternate feature set based on Haar wavelets instead of the usual image intensities. Viola and Jones<ref>Viola and Jones, "Rapid object detection using a boosted cascade of simple features", Computer Vision and [[Pattern Recognition]], 2001</ref> adapted the idea of using Haar wavelets and developed the so-called Haar-like features. A Haar-like feature considers adjacent rectangular regions at a specific location in a detection window, sums up the pixel intensities in each region and calculates the difference between these sums. This difference is then used to [[categorization\|categorize]] subsections of an image.
	~~For example, let us say we~~ have an image database with human [[face]]s. It is a common observation that among all faces the region of the eyes is darker than the region of the cheeks. Therefore a common haar feature for face detection is a set of two adjacent rectangles that lie above the eye and the cheek region. The position of these rectangles is defined relative to ~~a detection window that acts like a bounding box to the target object (the face in this case)~~.

	~~In the detection phase of the [[Viola–Jones object detection framework]], a window of the target size is moved over the input image,~~ and ~~for each subsection of the image the Haar-like feature is calculated. This difference is then compared~~ to a learned threshold that separates non-objects from objects. Because such a Haar-like feature is only a weak learner or classifier (its detection quality is slightly better than random guessing) a large number of Haar-like features are necessary to describe an object with sufficient accuracy. In the Viola–Jones object detection framework, the Haar-like features are therefore organized in something called a ''classifier cascade'' to form a strong learner or classifier.

	~~The key advantage of a Haar-like feature over most other features is its calculation speed. Due to the use of ''[[integral image]]s'', a Haar-like feature of any size can be calculated~~ in ~~constant time (approximately 60 microprocessor instructions for a 2-rectangle feature).~~

	~~== Rectangular Haar-like features ==~~

	~~A simple rectangular Haar-like feature can be defined as the difference of the sum of pixels of areas inside the rectangle, which can be at any position and scale within the original image~~. ~~This modified feature set~~ is ~~called ''2-rectangle feature''. Viola and Jones also defined 3-rectangle features and 4-rectangle features. The values indicate certain characteristics of a particular area of the image~~. Each feature type can indicate the existence (or absence) of certain characteristics in the image, such as edges or changes in texture. For example, a 2-rectangle feature can indicate where the border lies between a dark region and a light region.

	~~== Fast computation of Haar-like features ==~~

	~~One of the contributions of Viola and Jones was to use [[summed area table]]s,~~<~~ref~~>~~Crow, F, "Summed-area tables for texture mapping", in Proceedings of [[SIGGRAPH]], 18(3):207–212, 1984~~<~~/ref~~> which they called ''[[integral image]]s''. Integral images can be defined as two-dimensional [[lookup table]]s in the form of a matrix with the same size of the original image. Each element of the integral image contains the sum of all pixels located on the up-left region of the original image (~~in relation to the element's position). This allows to compute sum of rectangular areas in the image, at any position or scale, using only four lookups:~~
	[[~~Image~~:~~Prm VJ fig3 computeRectangleWithAlpha~~.~~png\|thumb\|right\|Finding the sum of the shaded rectangular area]]~~

	~~: <math> \text{sum} = I(C) + I(A) - I(B) - I(D)~~. ~~\, <~~/~~math>~~

	~~where points <math>A, B, C, D<~~/~~math> belong to the integral image <math>I<~~/~~math>, as shown in the figure.~~

	Each Haar-like feature may need more than four lookups, depending on how it was defined. Viola and Jones's 2-rectangle features need six lookups, 3-rectangle features need eight lookups, and 4-rectangle features need nine lookups.

	~~== Tilted Haar-like features ==~~
	~~Lienhart and Maydt<ref>Lienhart, R. and Maydt, J., "An extended set of Haar-like features for rapid object detection", ICIP02, pp. I: 900–903, 2002<~~/~~ref> introduced the concept of a tilted (45°) Haar-like feature~~. ~~This was used to increase~~ the ~~[[dimensionality~~]] of the set of features in an attempt to improve the detection of objects in images. This was successful, as some of these features are able to describe the object in a better way. For example, a 2-rectangle tilted Haar-like feature can indicate the existence of an edge at 45°.

	~~Messom and Barczak<ref>Messom, C.H. and Barczak, A.L.C., "Fast and Efficient Rotated Haar-like Features Using Rotated Integral Images", Australian Conference on Robotics and Automation (ACRA2006~~), pp. 1–6, 2006</ref> extended the idea to a generic rotated Haar-like feature. Although the idea sounds mathematically sound, practical problems prevented the use of Haar-like features at any angle. In order to be fast, detection algorithms use low resolution images, causing [[rounding error]]s. For this reason, rotated Haar-like features are not commonly used.

	~~==References==~~
	~~<references/>~~
	* Haar A. ''Zur Theorie der orthogonalen Funktionensysteme'', Mathematische Annalen, '''69''', pp. 331–371, 1910.

	~~[[Category:Bioinformatics]]~~
	~~[[Category:Feature detection]]~~

en>Simonpratt: added lower bound on computation of greedy spanner, fixed greedy algorithm fastest run time

2013-11-26T04:24:22Z

added lower bound on computation of greedy spanner, fixed greedy algorithm fastest run time

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2012-05-05T23:59:22Z

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en>BG19bot: WP:CHECKWIKI error fix for #64. Do general fixes if a problem exists. - using AWB

en>Simonpratt: added lower bound on computation of greedy spanner, fixed greedy algorithm fastest run time

en>Helpful Pixie Bot: ISBNs (Build KC)