{"id":1977,"date":"2014-02-05T17:32:43","date_gmt":"2014-02-05T17:32:43","guid":{"rendered":"https:\/\/www.anagram.at\/en\/diplomarbeit\/correspondence-analysis-2\/"},"modified":"2014-02-05T17:32:43","modified_gmt":"2014-02-05T17:32:43","slug":"correspondence-analysis-2","status":"publish","type":"page","link":"https:\/\/www.anagram.at\/en\/diplomarbeit\/correspondence-analysis-2\/","title":{"rendered":"Correspondence Analysis"},"content":{"rendered":"


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\n Next:<\/b> Search for corresponding lines<\/a>
\n Up:<\/b> Methods<\/a>
\n Previous:<\/b> Line Extraction<\/a>
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Correspondence Analysis
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\nAccording to a recent taxonomy [SS02<\/a>], methods creating a dense disparity map can be roughly divided into two groups. <\/p>\n

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Global algorithms<\/strong><\/dt>\n
[KZ02<\/a>], which tries to assign disparities in order to minimize a global cost function. They yield very accurate and dense disparity maps but at the expense of highly computational efforts, thus are not applicable to real-time environments.\n<\/dd>\n
Local algorithms<\/strong><\/dt>\n
[HIG02<\/a>,DSMMN02<\/a>,KKK+<\/sup>95<\/a>], also referred to as area-based algorithms, compare the photometric properties of neighbouring pixels in order to determine disparity. They yield significantly less accurate results compared to global algorithms, but may run in real-time. This depends heavily on the maximum disparity allowed.\n<\/dd>\n<\/dl>\n
<\/p>\n\n\n
Figure 3.10:<\/strong>
\nThe football field of the FIRA MiroSOT football league<\/caption>\n
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\n \"Image<\/div>\n<\/td>\n<\/tr>\n<\/table>\n<\/div>\n

The fastest method to compute disparity is a feature-based<\/i> approach with the constraint that the feature extraction is fast – which is the first problem that has to be solved. To find a feature which is easy to extract and easy to compare. Features are striking parts of an image, thus have a strong relation to the environment and the objects that are recorded. In the case of a football playing robot, the football field and the robots. The size of the robots in the MiroSOT football league is limited to 8cm x 8xm x 8cm. Figure 3.10<\/a> shows the official football field of the MiroSOT football league. Viewed from the camera of the Tinyphoon robot (Figure 3.11<\/a>), the goal, the markers on the ground and the field are , when projected to the image plane, represented by lines. Thus, lines are chosen as features for the corresponding analysis. <\/p>\n

<\/p>\n\n\n
Figure 3.11:<\/strong>
\nThe Tinyphoon robot<\/caption>\n
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\n \"Image<\/div>\n<\/td>\n<\/tr>\n<\/table>\n<\/div>\n

The benefit of a straight line is that it has a memory efficient description and that a fast line extraction can be implemented. In Section 3.4<\/a> an iterative line detector has been presented. Besides the Canny edge detector it has a time complexity (TC) of \"$, where \"$ is the number of image points. To find corresponding lines, every line has to be compared with each other, thus the TC of the correspondence analysis is \"$. <\/p>\n\n

Subsections<\/strong><\/a><\/p>\n