TY - GEN
T1 - Multi-level dynamic programming for axial motion stereo line matching
AU - Yip, Raymond K. K.
N1 - Publication details (e.g. title, author(s), publication statuses and dates) are captured on an “AS IS” and “AS AVAILABLE” basis at the time of record harvesting from the data source. Suggestions for further amendments or supplementary information can be sent to [email protected].
PY - 1997
Y1 - 1997
N2 - In this paper, a multi-level dynamic programming approach is used to solve the line segment based correspondence problem in axial motion stereo. In this method, a Local Similarity Measure is calculated for each line segment pair between the Front and Back images. In level 1, the matching probability between line segments is represented by their Local Similarity Measure (LSM). Line segment pair that have a matching probability larger than a threshold T1 is selected as potential matching pair. T1 is set to a relative high value so that the probability of correct match of level 1 is very high. Dynamic programming is then used to search for their best match. Based on the geometric properties between the matched and the unmatched line segments, a Global Similarity Measure (GSM)is calculated for each unmatched line segment pair. An overall Similarity Measure (matching probability) is then obtained by the LSM and the GSM. Then, the algorithm begin the second match but with a slightly lower threshold T2. The new matched results are then used to modify the GSM and the overall Similarity Measure. These processes are repeated until a predefined level nstop (or a predefined condition) is reached. By using the GSM and multi-level searching technique, the proposed technique increases the matching accuracy and reduce the number of unmatched line segment due to misordering when dynamic programming is used for axial motion stereo matching. © Springer-Verlag Berlin Heidelberg 1997.
AB - In this paper, a multi-level dynamic programming approach is used to solve the line segment based correspondence problem in axial motion stereo. In this method, a Local Similarity Measure is calculated for each line segment pair between the Front and Back images. In level 1, the matching probability between line segments is represented by their Local Similarity Measure (LSM). Line segment pair that have a matching probability larger than a threshold T1 is selected as potential matching pair. T1 is set to a relative high value so that the probability of correct match of level 1 is very high. Dynamic programming is then used to search for their best match. Based on the geometric properties between the matched and the unmatched line segments, a Global Similarity Measure (GSM)is calculated for each unmatched line segment pair. An overall Similarity Measure (matching probability) is then obtained by the LSM and the GSM. Then, the algorithm begin the second match but with a slightly lower threshold T2. The new matched results are then used to modify the GSM and the overall Similarity Measure. These processes are repeated until a predefined level nstop (or a predefined condition) is reached. By using the GSM and multi-level searching technique, the proposed technique increases the matching accuracy and reduce the number of unmatched line segment due to misordering when dynamic programming is used for axial motion stereo matching. © Springer-Verlag Berlin Heidelberg 1997.
UR - https://www.scopus.com/pages/publications/84957708536
UR - https://www.scopus.com/record/pubmetrics.uri?eid=2-s2.0-84957708536&origin=recordpage
U2 - 10.1007/3-540-63507-6_252
DO - 10.1007/3-540-63507-6_252
M3 - RGC 32 - Refereed conference paper (with host publication)
SN - 3540635076
SN - 9783540635079
VL - 1310
T3 - Lecture Notes in Computer Science
SP - 612
EP - 619
BT - Image Analysis and Processing - 9th International Conference, ICIAP 1997, Proceedings
PB - Springer Verlag
T2 - 9th International Conference on Image Analysis and Processing, ICIAP 1997
Y2 - 17 September 1997 through 19 September 1997
ER -