CMRT09

Stilla, Uwe

Bibliographic data

Monograph

Persistent identifier:: 856955019

Author:: Stilla, Uwe

Title:: CMRT09

Sub title:: object extraction for 3D city models, road databases, and traffic monitoring ; concepts, algorithms and evaluation ; Paris, France, September 3 - 4, 2009 ; [joint conference of ISPRS working groups III/4 and III/5]

Scope:: X, 234 Seiten

Year of publication:: 2009

Place of publication:: Lemmer

Publisher of the original:: GITC

Identifier (digital):: 856955019

Illustration:: Illustrationen, Diagramme, Karten

Language:: English

Usage licence:: Attribution 4.0 International (CC BY 4.0)

Publisher of the digital copy:: Technische Informationsbibliothek Hannover

Place of publication of the digital copy:: Hannover

Year of publication of the original:: 2016

Document type:: Monograph

Collection:: Earth sciences

Chapter

Title:: STUDY OF SIFT DESCRIPTORS FOR IMAGE MATCHING BASED LOCALIZATION IN URBAN STREET VIEW CONTEXT David Picard, Matthieu Cord and Eduardo Valle

Document type:: Monograph

Structure type:: Chapter

CMRT09: Object Extraction for 3D City Models, Road Databases and Traffic Monitoring - Concepts, Algorithms, and Evaluation 198 Figure 13: Evolution of the precision against the number of images retrieved. strategy, but it is still under 5% most of the retrieval. In overall, all methods failed at finding the relevant scene in the database. 6 CONCLUSION In this paper, we have reviewed the use of keypoints based voting strategy for image matching in the context of the iTowns project. We have tested different strategies (pair wise comparison, k-NN search with brute voting, angle dif ferences refinement, and 2D affine transform estimation) on two subset of urban scene database. We have first found that there is no penalty in using an approximate k-NN search, which is a huge improvement on the retrieval speed. Even for small datasets like the first we used, a pair-wise comparison or a linear k-NN search is not feasible for interactive application. The second point we have found is that the post-processing of the voting strategies is essential to the success of the retrieval. The Ransac refinement is the only one able to retrieve at least one relevant image within the first five im ages, which is the main criterion for a user in this kind of task. A further improvement could be the estimation of more complexe transformation that are more robust to per spective changes. However, overall results largely depend on the database content. In the case of a small database (which can be obtained through geolocalization) with well taken pictures like the first we used, the results are good enough to be used in the intended application.For the second database, the quality of the results is very low, making them inade quate for our applications. This lack of quality might be an intrinsic characteristic of SIFT when confronted to images like ours, that contain many problematic features (complex shadows, trees, branches, etc), which spawn a huge amount of descriptors with low discriminant power. Those points increase dramatically the number of false matches, inflat ing the rank of of non relevant images (such as on Fig. 14, which has more matches than the relevant images). As im provement, we suggest a filtering of the database in order to remove points that are not informative. To conclude, we consider the extension of keypoints based method from copy detection to the matching of scene in difficult context as not successful. We think there is more work to do both on the descriptors and on the matching process. We intend to share our databases and groundtruth with the community in order to allow the benchmarking of those tasks on difficult images. Figure 14: False matching between two images after geo metric consistency check. REFERENCES Fischler, M. A. and Bolles, R. C., 1981. Random sample consen sus: a paradigm for model fitting with applications to image anal ysis and automated cartography. Commun. ACM 24(6), pp. 381 — 395. Friedman, J., Bentley, J. L. and Finkei, R. A., 1976. An algroithm for finding best matches in logarithmic expected time. Technical report, Stanford, CA, USA. Indyk, P. and Motwani, R., 1998. Approximate nearest neighbors: towards removing the curse of dimensionality. In: STOC ’98: Proceedings of the thirtieth annual ACM symposium on Theory of computing, ACM, New York, NY, USA, pp. 604-613. Jegou, H., Douze, M. and Schmid, C., 2008. Hamming em bedding and weak geometric consistency for large scale image search. In: A. Z. David Forsyth, Philip Torr (ed.), European Con ference on Computer Vision, LNCS, Vol. I, Springer, pp. 304- 317. Kleinberg, J. M., 1997. Two algorithms for nearest-neighbor search in high dimensions. In: STOC '91: Proceedings of the twenty-ninth annual ACM symposium on Theory of computing, ACM, New York, NY, USA, pp. 599-608. Lowe, D., 2003. Distinctive image features from scale-invariant keypoints. In: International Journal of Computer Vision, Vol. 20, pp. 91-110. Schmid, C. and Mohr, R., 1997. Local grayvalue invariants for image retrieval. IEEE Trans. Pattern Anal. Mach. Intell. 19(5), pp.530-535. Valle, E., 2008. Local-Descriptor Matching for Image Identifi cation Systems. PhD thesis, Univ. Cergy-Pontoise, ETIS, UMR CNRS 8051. Direction : S. Philipp-Foliguet, M. Cord. Valle, E., Cord, M. and Philipp-Foliguet, S., 2008. High dimensional descriptor indexing for large multimedia databases. In: CIKM '08: Proceeding of the 17th ACM conference on In formation and knowledge management, ACM, New York, NY, USA, pp. 739-748.

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