"Epipolar geometry"의 두 판 사이의 차이
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* In this section we will deal with epipolar geometry.<ref name="ref_52f5">[https://docs.opencv.org/master/da/de9/tutorial_py_epipolar_geometry.html OpenCV: Epipolar Geometry]</ref> | * In this section we will deal with epipolar geometry.<ref name="ref_52f5">[https://docs.opencv.org/master/da/de9/tutorial_py_epipolar_geometry.html OpenCV: Epipolar Geometry]</ref> | ||
* The epipolar geometry then describes the relation between the two resulting views.<ref name="ref_3f33">[https://en.wikipedia.org/wiki/Epipolar_geometry Epipolar geometry]</ref> | * The epipolar geometry then describes the relation between the two resulting views.<ref name="ref_3f33">[https://en.wikipedia.org/wiki/Epipolar_geometry Epipolar geometry]</ref> | ||
| + | ===소스=== | ||
| + | <references /> | ||
| + | |||
| + | == 노트 == | ||
| + | |||
| + | ===위키데이터=== | ||
| + | * ID : [https://www.wikidata.org/wiki/Q200904 Q200904] | ||
| + | ===말뭉치=== | ||
| + | # Epipolar geometry describes the geometric relationship between two camera systems.<ref name="ref_183e0d45">[https://link.springer.com/10.1007/978-0-387-31439-6_128 Epipolar Geometry]</ref> | ||
| + | # In this section we will deal with epipolar geometry.<ref name="ref_52f527b1">[http://amroamroamro.github.io/mexopencv/opencv/epipolar_geometry_demo.html Epipolar Geometry]</ref> | ||
| + | # This paper gives a comparison of SAR imaging and camera imaging from the viewpoint of epipolar geometry.<ref name="ref_f831a665">[https://www.spiedigitallibrary.org/conference-proceedings-of-spie/9901/99010V/Epipolar-geometry-comparison-of-SAR-and-optical-camera/10.1117/12.2234943.full Epipolar geometry comparison of SAR and optical camera]</ref> | ||
| + | # The imaging model and epipolar geometry of the two sensors are analyzed in detail.<ref name="ref_f831a665" /> | ||
| + | # The standard epipolar geometry setup involves two cameras observing the same 3D point P, whose projection in each of the image planes is located at p and p’ respectively.<ref name="ref_c2fdbeca">[https://www.geeksforgeeks.org/python-opencv-epipolar-geometry/ Python OpenCV: Epipolar Geometry]</ref> | ||
| + | # An interesting case of epipolar geometry is shown in Figure 4, which occurs when the image planes are parallel to each other.<ref name="ref_c2fdbeca" /> | ||
| + | # The application of projective geometry to this situation results in the now popular epipolar geometry approach.<ref name="ref_8bedaa2d">[http://www1.cs.columbia.edu/~jebara/htmlpapers/SFM/node8.html Epipolar Geometry]</ref> | ||
| + | # Due to the linearity of the above equation, the epipolar geometry approach maintains a clean elegance in its manipulations.<ref name="ref_8bedaa2d" /> | ||
| + | # The result is that it is not possible to determine the epipolar geometry between close consecutive frames and it cannot be determined from image correspondences alone.<ref name="ref_8bedaa2d" /> | ||
| + | # The linearization in epipolar geometry creates these degeneracies and numerical ill-conditioning near them.<ref name="ref_8bedaa2d" /> | ||
| + | # To calculate depth information from a pair of images we need to compute the epipolar geometry.<ref name="ref_6007913c">[http://homepages.inf.ed.ac.uk/rbf/CVonline/LOCAL_COPIES/OWENS/LECT10/node3.html Epipolar geometry]</ref> | ||
| + | # We first describe properties of the epipolar geometry of two affine cameras, and its optimal computation from point correspondences.<ref name="ref_435efd61">[https://www.cambridge.org/core/books/multiple-view-geometry-in-computer-vision/affine-epipolar-geometry/F4D4DAE080C01DA8330A8BE038E28945 Multiple View Geometry in Computer Vision]</ref> | ||
| + | # The epipolar geometry then describes the relation between the two resulting views.<ref name="ref_3f33d8fe">[https://en.wikipedia.org/wiki/Epipolar_geometry Epipolar geometry]</ref> | ||
===소스=== | ===소스=== | ||
<references /> | <references /> | ||
2020년 12월 21일 (월) 00:27 판
노트
- The linearization in epipolar geometry creates these degeneracies and numerical ill-conditioning near them.[1]
- To calculate depth information from a pair of images we need to compute the epipolar geometry.[2]
- We first describe properties of the epipolar geometry of two affine cameras, and its optimal computation from point correspondences.[3]
- In this section we will deal with epipolar geometry.[4]
- The epipolar geometry then describes the relation between the two resulting views.[5]
소스
노트
위키데이터
- ID : Q200904
말뭉치
- Epipolar geometry describes the geometric relationship between two camera systems.[1]
- In this section we will deal with epipolar geometry.[2]
- This paper gives a comparison of SAR imaging and camera imaging from the viewpoint of epipolar geometry.[3]
- The imaging model and epipolar geometry of the two sensors are analyzed in detail.[3]
- The standard epipolar geometry setup involves two cameras observing the same 3D point P, whose projection in each of the image planes is located at p and p’ respectively.[4]
- An interesting case of epipolar geometry is shown in Figure 4, which occurs when the image planes are parallel to each other.[4]
- The application of projective geometry to this situation results in the now popular epipolar geometry approach.[5]
- Due to the linearity of the above equation, the epipolar geometry approach maintains a clean elegance in its manipulations.[5]
- The result is that it is not possible to determine the epipolar geometry between close consecutive frames and it cannot be determined from image correspondences alone.[5]
- The linearization in epipolar geometry creates these degeneracies and numerical ill-conditioning near them.[5]
- To calculate depth information from a pair of images we need to compute the epipolar geometry.[6]
- We first describe properties of the epipolar geometry of two affine cameras, and its optimal computation from point correspondences.[7]
- The epipolar geometry then describes the relation between the two resulting views.[8]