Particle Swarm Image Matching on Epipolar Line
This paper presents a method for determining the 3D position of an image point on a reference image using particle swarm optimization (PSO) to search the height (Z value) that gives the biggest Normalized Cross Correlation (NCC) coefficient. The searching area is in the surrounding of the height of...
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| Format: | Article |
| Language: | English |
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Copernicus Publications
2014-04-01
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| Series: | The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences |
| Online Access: | http://www.int-arch-photogramm-remote-sens-spatial-inf-sci.net/XL-4/87/2014/isprsarchives-XL-4-87-2014.pdf |
| _version_ | 1811299702027583488 |
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| author | J.-S. Hsia |
| author_facet | J.-S. Hsia |
| author_sort | J.-S. Hsia |
| collection | DOAJ |
| description | This paper presents a method for determining the 3D position of an image point on a reference image using particle swarm
optimization (PSO) to search the height (Z value) that gives the biggest Normalized Cross Correlation (NCC) coefficient. The
searching area is in the surrounding of the height of the image point. The NCC coefficient evaluates the similarity with the image
point and a corresponding point on an epipolar line in the search image. The position of corresponding image point on the epipolar
line is determined by the height point on a sloping line locus. The PSO algorithm starts with a swarm of random particles. The
position of each particle is a potential solution in the problem space. Each particle is given a randomized velocity and attracted
toward the location of the best fitness. The position of each particle is iteratively modified by adding a newly computed velocity to
its current position. The velocity is updated by three factors which are two attractions from local best position and global best
position, two strengths of the attractions, and two uniform random numbers for each attraction. The iteration will stop when the
current solution is convergent. The time of computation is highly related to the range of height and the interval of height
enumeration when the approach to find a corresponding image point of an image point on a reference image is based on the height
enumeration along sloping line locus. The precision of results can be improved by decreasing the interval of height enumeration.
This shows the limitation of the enumeration method in the efficiency and accuracy. The issue is overcome by a method of using
PSO algorithm. The proposed method using different parameters such as the size of image window, the number of particles, and the
size of the height searching range has been applied to aerial stereo images. The accuracy of tested results is evaluated on the base of
the comparison to the reference data from the results of least-square matching being manually given initial points. The evaluation
result shows that tested results has given a solution to a level of less than 1 centimetre without using refined image matching method.
The same level of accuracy can reach even when the searching range is bigger than 90 meters. But the difference of image window
size may lead to the change of the matching result. And, without the procedures of both coarse-to-fine hierarchical solution and
refined image matching method, the algorithm still can give the same accuracy level of least-square image matching resulting. This
method also shows its ability to give reasonable matching results without manual assistance. |
| first_indexed | 2024-04-13T06:39:45Z |
| format | Article |
| id | doaj.art-e932189d3f434dc1b5f6723fe530298d |
| institution | Directory Open Access Journal |
| issn | 1682-1750 2194-9034 |
| language | English |
| last_indexed | 2024-04-13T06:39:45Z |
| publishDate | 2014-04-01 |
| publisher | Copernicus Publications |
| record_format | Article |
| series | The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences |
| spelling | doaj.art-e932189d3f434dc1b5f6723fe530298d2022-12-22T02:57:46ZengCopernicus PublicationsThe International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences1682-17502194-90342014-04-01XL-4879110.5194/isprsarchives-XL-4-87-2014Particle Swarm Image Matching on Epipolar LineJ.-S. Hsia0Dept. of Multimedia and Mobile Commerce, Kainan University, No.1 Kainan Road, Luchu, Taoyuan, TaiwanThis paper presents a method for determining the 3D position of an image point on a reference image using particle swarm optimization (PSO) to search the height (Z value) that gives the biggest Normalized Cross Correlation (NCC) coefficient. The searching area is in the surrounding of the height of the image point. The NCC coefficient evaluates the similarity with the image point and a corresponding point on an epipolar line in the search image. The position of corresponding image point on the epipolar line is determined by the height point on a sloping line locus. The PSO algorithm starts with a swarm of random particles. The position of each particle is a potential solution in the problem space. Each particle is given a randomized velocity and attracted toward the location of the best fitness. The position of each particle is iteratively modified by adding a newly computed velocity to its current position. The velocity is updated by three factors which are two attractions from local best position and global best position, two strengths of the attractions, and two uniform random numbers for each attraction. The iteration will stop when the current solution is convergent. The time of computation is highly related to the range of height and the interval of height enumeration when the approach to find a corresponding image point of an image point on a reference image is based on the height enumeration along sloping line locus. The precision of results can be improved by decreasing the interval of height enumeration. This shows the limitation of the enumeration method in the efficiency and accuracy. The issue is overcome by a method of using PSO algorithm. The proposed method using different parameters such as the size of image window, the number of particles, and the size of the height searching range has been applied to aerial stereo images. The accuracy of tested results is evaluated on the base of the comparison to the reference data from the results of least-square matching being manually given initial points. The evaluation result shows that tested results has given a solution to a level of less than 1 centimetre without using refined image matching method. The same level of accuracy can reach even when the searching range is bigger than 90 meters. But the difference of image window size may lead to the change of the matching result. And, without the procedures of both coarse-to-fine hierarchical solution and refined image matching method, the algorithm still can give the same accuracy level of least-square image matching resulting. This method also shows its ability to give reasonable matching results without manual assistance.http://www.int-arch-photogramm-remote-sens-spatial-inf-sci.net/XL-4/87/2014/isprsarchives-XL-4-87-2014.pdf |
| spellingShingle | J.-S. Hsia Particle Swarm Image Matching on Epipolar Line The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences |
| title | Particle Swarm Image Matching on Epipolar Line |
| title_full | Particle Swarm Image Matching on Epipolar Line |
| title_fullStr | Particle Swarm Image Matching on Epipolar Line |
| title_full_unstemmed | Particle Swarm Image Matching on Epipolar Line |
| title_short | Particle Swarm Image Matching on Epipolar Line |
| title_sort | particle swarm image matching on epipolar line |
| url | http://www.int-arch-photogramm-remote-sens-spatial-inf-sci.net/XL-4/87/2014/isprsarchives-XL-4-87-2014.pdf |
| work_keys_str_mv | AT jshsia particleswarmimagematchingonepipolarline |