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Poster Session 1
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Initialization for Image Registration using Feature Matching
(a) |
(b) |
Figure 7. Segmented Polygons with IDs (a) Reference Image (b) Sensed Image
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Table 1. Corresponded
Polygons
| Sensed | Reference |
| s1 | r1 |
| s3 | r3 |
| s4 | r4 |
| s5 | r5 |
| s6 | r6 |
| s8 | r7 |
| s10 | r8 |
| s11 | r9 |
| s12 | r10 |
| s13 | r13 |
| s14 | r14 |
| s15 | r16 |
| s16 | r15 |
| s17 | r17 |
| s18 | r18 |
| s20 | r20 |
Table 2. Combined
Correspondence for FD & IM
| Sensed | Reference |
| s1 | r1 |
| s4 | r2 |
| s5 | r4 |
| s6 | r12 |
| s8 | r7 |
| s10 | r15 |
| s11 | r9 |
| s12 | r10 |
| s13 | r13 |
| s15 | r16 |
| s17 | r17 |
| s20 | r20 |
Table 3. Correspondence
by SM
| Sensed | Reference |
| s1 | r1 |
| s3 | r3 |
| s5 | r5 |
| s6 | r6 |
| s8 | r7 |
| s10 | r8 |
| s12 | r10 |
| s15 | r16 |
| s16 | r15 |
| s17 | r17 |
| s18 | r18 |
| s19 | r14 |
| s20 | r20 |
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Table 4. Matching Results
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Table 5. Robust Estimated Results |
| Sensed | Reference |
| s1 | r1 |
| s8 | r7 |
| s12 | r10 |
| s15 | r16 |
| s17 | r17 |
| s20 | r20 |
|
| Sensed | Reference |
| s1 | r1 |
| s8 | r7 |
| s12 | r10 |
| s15 | r16 |
| s17 | r17 |
| s20 | r20 |
|
It is observed that if only FD and IM are used, more polygon pairs will be selected. While some
erroneous correspondence (S4, S5, S6, and S10) are remained. On the other hand, if we use SM,
the situation is similar. The difference is that the remained erroneous corresponding polygon (S19) is different. When three descriptors are combined, the selected polygon pairs are less
while with highest reliability. It is the purpose of this investigation that we only need small
amount, 3 for instance, of RCPs for initialization of image registration. Thus, the proposed
scheme is validated for the time being.
Concluding Remarks
The experimental results indicate that the proposed scheme may select RCP pairs with very high
reliability. Although the number of selected pairs is less, the reliability is our concerned. Thus,
the test is successful. However, we still need further tests to ascertain the applicability. It should
be pointed out that the proposed scheme might only be used for the area of rolling terrain. For
those images with rugged terrain or large scale images with high-rises the scheme may result
unreliably.
References
- Chen, L.C., and Lee, L. H., 1992, Progressive Generation of Control Frameworks for Image
Registration, Photogrammetric Engineering and Remote Sensing, 58 (9), pp.1321-1328.
- Chen, L. C., and Rau, J. Y., 1993. “Geometric Correction of Airborne Scanner Imagery
Using Orthophotos and Triangulated Feature Points”, International Journal of Remote
Sensing. 14 (16), pp.3041-3059.
- Deok, J. P., Nam, K. M., and Park, R. H., 1995, Multiresoultion edge detection techniques,
Pattern Recognition, 28 (2), pp. 211-229.
- Flusser, J., 1992. Invariant shape description and measure of object similarity, Proc. 4th
Internat. Conf. Image Processing, Maastricht, The Netherlands, pp.139-142.
- Goshtasby, A., Stockman, G., and Page, C., 1986. A region-based approach to digital image
registration with subpixel accuracy, IEEE Trans. On Geoscience and Remote Sensing, 24
(3), pp. 390-399.
- Harwood, D., Subbarao, M., Hakalahti, H., and Davis, L. S., 1987, A new class of
edge-preserving smoothing filters, Pattern Recognition Letters, 6, pp. 155-162.
- Mayr, W. and Heipke, C., 1988, A contribution to digital orthophoto generation,
International Archives of Photogrammetry and Remote Sensing, Kyoto, Vol. XXVII, Part
B11, pp. 430-439.
- Nevatia, R., and Medioni, G., 1984, Matching images using linear features, IEEE Trans. on
PAMI, 6 (6), pp. 675-685.
- Saint-Marc, P., Chen, J. S., and Medioni, G., 1991, Adaptive smoothing: a general tool for
early vision, IEEE Pattern Analysis and Machine Intelligence, 13 (6), pp.514-529.
- Pratt, W. K., 1991, Digital Image Processing, 2nd edition, John Wiley & Sons, New York, pp.
563-648.
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