DaLI: Deformation and Light Invariant Descriptor

International Journal of Computer Vision

Volumn 115, Issue 2, 2015, Pages 136-154

  Simo Serra, Edgar ^a   Torras, Carme ^a   Moreno Noguer, Francesc ^a  

^a CSIC   (Spain)

Author keywords

Deformation and illumination invariance; Diffusion equation; Heat kernel descriptors; Local image descriptors

Indexed keywords

DEFORMATION;

DESCRIPTORS; DIFFUSION EQUATIONS; EXPERIMENTAL VALIDATIONS; FEATURE POINT DESCRIPTOR; HEAT KERNEL SIGNATURES; ILLUMINATION INVARIANCE; LOCAL IMAGE DESCRIPTORS; LOW-DIMENSIONAL SUBSPACE;

PRINCIPAL COMPONENT ANALYSIS;

EID: 84942980371     PISSN: 09205691     EISSN: 15731405     Source Type: Journal    
DOI: 10.1007/s11263-015-0805-1     Document Type: Article

References (56)

1. Aflalo, Y., Bronstein, E. M., Bronstein, M. M., & Kimmel, R. (2011). Deformable shape retrieval by learning diffusion kernels. In In Proc. SSVM.
2. Aubry, M., Schlickewei, U., & Cremers, D. (2011). The wave kernel signature: A quantum mechanical approach to shape analysis. In Computer Vision Workshops (ICCV Workshops), 2011 IEEE International Conference on (pp. 1626–1633).
3. Bay, H., Tuytelaars, T., & Gool, L. V. (2006). SURF: Speeded up robust features. In European Conference on Computer Vision (pp. 404–417).
4. Belongie, S., Malik, J., & Puzicha, J. (2002). Shape matching and object recognition using shape contexts. IEEE Transactions Pattern Analysis and Machine Intelligence, 24(4), 509–522.
5. Berg, A., Berg, T., & Malik, J. (2005). Shape matching and object recognition using low distortion correspondences. In IEEE Conference on Computer Vision and Pattern Recognition (pp. 26–33).
6. Bronstein, A., Bronstein, M., Bruckstein, A., & Kimmel, R. (2007). Analysis of two-dimensional non-rigid shapes. International Journal of Computer Vision, 78(1), 67–88.
7. Bronstein, M., & Kokkinos, I. (2010). Scale-invariant heat kernel signatures for non-rigid shape recognition. In IEEE Conference on Computer Vision and Pattern Recognition (pp. 1704–1711).
8. Cai, H., Mikolajczyk, K., & Matas, J. (2011). Learning linear discriminant projections for dimensionality reduction of image descriptors. IEEE Transactions Pattern Analysis and Machine Intelligence, 33(2), 338–352.
9. Calonder, M., Lepetit, V., Ozuysa, M., Trzcinski, T., Strecha, C., & Fua, P. (2012). BRIEF: Computing a local binary descriptor very fast. IEEE Transactions Pattern Analysis and Machine Intelligence, 34(7), 1281–1298.
10. Chavel, I. (1984). Eigenvalues in Riemannian geometry. London: London Academic Press.
11. Cheng, H., Liu, Z., Zheng, N., & Yang, J. (2008). A deformable local image descriptor. In IEEE Conference on Computer Vision and Pattern Recognition.
12. Cho, M., Lee, J., & Lee, K. (2009). Feature correspondence and deformable object matching via agglomerative correspondence clustering. In International Conference on Computer Vision (pp. 1280–1287).
13. Fan, B., Wu, F., & Hu, Z. (2012). Rotationally invariant descriptors using intensity order pooling. IEEE Transactions Pattern Analysis and Machine Intelligence, 34(10), 2031–2045.
14. Gębal, K., Bærentzen, J. A., Aanæs, H., & Larsen, R. (2009). Shape analysis using the auto diffusion function. In Proceedings of the Symposium on Geometry Processing, SGP ’09 (pp. 1405–1413).
15. de Goes, F., Goldenstein, S., & Velho, L. (2008). A hierarchical segmentation of articulated bodies. In Proceedings of the Symposium on Geometry Processing, SGP ’08 (pp. 1349–1356).
16. Gupta, R., & Mittal, A. (2007). Illumination and Affine-Invariant Point Matching using an Ordinal Approach. In International Conference on Computer Vision.
17. Gupta, R., & Mittal, A. (2008). Smd: A locally stable monotonic change invariant feature descriptor. In European Conference on Computer Vision (pp. 265–277).
18. Gupta, R., Patil, H., & Mittal, A. (2010). Robust order-based methods for feature description. In IEEE Conference on Computer Vision and Pattern Recognition.
19. Heikkilä, M., Pietikäinen, M., & Schmid, C. (2009). Description of interest regions with local binary patterns. Pattern Recognition, 42(3), 425–436.
20. Ke, Y., & Sukthankar, R. (2004). PCA-SIFT: a more distinctive representation for local image descriptors. In IEEE Conference on Computer Vision and Pattern Recognition (pp. 506–513).
21. Kokkinos, I., Bronstein, M., & Yuille, A. (2012). Dense Scale Invariant Descriptors for Images and Surfaces. Research Report RR-7914, INRIA.
22. Kovnatsky, A., Bronstein, M., Bronstein, A., & Kimmel, R. (2011). Photometric heat kernel signatures. In International Conference on Scale Space and Variational Methods in Computer Vision (pp. 616–627).
23. Leordeanu, M., & Hebert, M. (2005). A spectral technique for correspondence problems using pairwise constraints. In International Conference on Computer Vision (pp. 1482–1489).
24. Lévy, B. (2006). Laplace-Beltrami Eigenfunctions: Towards an Algorithm that Understands Geometry. In IEEE International Conference on Shape Modeling and Applications - SMI 2006 (p. 13).
25. Ling, H., & Jacobs, D. (2005). Deformation invariant image matching. In International Conference on Computer Vision (pp. 1466–1473).
26. Ling, H., & Jacobs, D. (2007). Shape classification using the inner-distance. IEEE Transactions Pattern Analysis and Machine Intelligence, 29(2), 286–299.
27. Ling, H., Yang, X., & Latecki, L. (2010). Balancing deformability and discriminability for shape matching. In European Conference on Computer Vision.
28. Litman, R., & Bronstein, A. (2014). Learning spectral descriptors for deformable shape correspondence. Pattern Analysis and Machine Intelligence, IEEE Transactions on, 36(1), 171–180.
29. Lowe, D. (2004). Distinctive image features from scale-invariant keypoints. International Journal of Computer Vision, 60(2), 91–110.
30. Mikolajczyk, K., & Schmid, C. (2005). A performance evaluation of local descriptors. IEEE Transactions Pattern Analysis and Machine Intelligence, 10(27), 1615–1630.
31. Morel, J., & Yu, G. (2009). ASIFT: A new framework for fully affine invariant image comparison. SIAM Journal on Imaging Sciences, 2(2), 438–469.
32. Moreno-Noguer, F. (2011). Deformation and illumination invariant feature point descriptor. In IEEE Conference on Computer Vision and Pattern Recognition (pp. 1593–1600).
33. Moreno-Noguer, F., & Fua, P. (2013). Stochastic exploration of ambiguities for nonrigid shape recovery. Pattern Analysis and Machine Intelligence, IEEE Transactions on, 35(2), 463–475.
34. Philbin, J., Isard, M., Sivic, J., & Zisserman, A. (2010). Descriptor learning for efficient retrieval. In European Conference on Computer Vision (pp. 677–691).
35. Pinkall, U., & Polthier, K. (1993). Computing discrete minimal surfaces and their conjugates. Experimental Mathematics, 2(1), 15–36.
36. Raviv, D., Bronstein, M. M., Sochen, N., Bronstein, A. M., & Kimmel, R. (2011). Affine-invariant diffusion geometry for the analysis of deformable 3d shapes. In IEEE Conference on Computer Vision and Pattern Recognition.
37. Reuter, M., Wolter, F., & Peinecke, N. (2006). Laplace-beltrami spectra as ’shape-dna’ of surfaces and solids. Computer Aided Design, 38(4), 342–366.
38. Rustamov, R. (2007). Laplace-beltrami eigenfunctions for deformation invariant shape representation. In Eurographics Symposium on Geometry Processing (pp. 225–233).
39. Sanchez, J., Ostlund, J., Fua, P., & Moreno-Noguer, F. (2010). Simultaneous pose, correspondence and non-rigid shape. In IEEE Conference on Computer Vision and Pattern Recognition (pp. 1189–1196).
40. Serradell, E., Glowacki, P., Kybic, J., Moreno-Noguer, F., & Fua, P. (2012). Robust non-rigid registration of 2d and 3d graphs. In IEEE Conference on Computer Vision and Pattern Recognition.
41. Shi, L., Yu, Y., & Feng, N. B. W. W. (2006). A fast multigrid algorithm for mesh deformation. ACM SIGGRAPH, 25(3), 1108–1117.
42. Sochen, N., Kimmel, R., & Malladi, R. (1998). A general framework for low level vision. IEEE Transactions on Image Processing, 7(3), 310–318.
43. Strecha, C., Bronstein, A. M., Bronstein, M. M., & Fua, P. (2012). LDAHash: Improved matching with smaller descriptors. IEEE Transactions Pattern Analysis and Machine Intelligence, 34(1), 66–78.
44. Sun, J., Ovsjanikov, M., & Guibas, L. (2009). A concise and provably informative multi-scale signature based on heat diffusion. In Eurographics Symposium on Geometry Processing (pp. 1383–1392).
45. Tang, F., Lim, S.H., Chang, N., & Tao, H. (2009). A novel feature descriptor invariant to complex brightness changes. In IEEE Conference on Computer Vision and Pattern Recognition (pp. 2631–2638).
46. Tola, E., Lepetit, V., & Fua, P. (2010). Daisy: An efficient dense descriptor applied to wide-baseline stereo. IEEE Transactions Pattern Analysis and Machine Intelligence, 32(5), 815–830.
47. Torresani, L., Kolmogorov, V., & Rother, C. (2008). Feature correspondence via graph matching: Models and global optimization. In European Conference on Computer Vision (pp. 596–609).
48. Trulls, E., Kokkinos, I., Sanfeliu, A., & Moreno-Noguer, F. (2013). Dense segmentation-aware descriptors. In IEEE Conference on Computer Vision and Pattern Recognition.
49. Trulls, E., Tsogkas, S., Kokkinos, I., Sanfeliu, A., & Moreno-Noguer, F. (2014). Segmentation-aware deformable part models. In IEEE Conference on Computer Vision and Pattern Recognition.
50. Vaxman, A., Ben-Chen, M., & Gotsman, C. (2010). A multi-resolution approach to heat kernels on discrete surfaces. ACM SIGGRAPH, 29(4), 121.
51. Vedaldi, A., & Fulkerson, B. (2008). VLFeat: An open and portable library of computer vision algorithms. http://www.vlfeat.org/
52. Vedaldi, A., & Soatto, S. (2005). Features for recognition: Viewpoint invariance for non-planar scenes. In International Conference on Computer Vision (pp. 1474–1481).
53. Wang, Z., Fan, B., & Wu, F. (2011). Local intensity order pattern for feature description. In International Conference on Computer Vision (pp. 603–610).
54. Wesseling, P. (2004). An Introduction to multigrid methods. Chichester: Wiley.
55. Yanowitz, S., & Bruckstein, A. (1989). A new method for image segmentation. Computer Vision, Graphics, and Image Processing, 46(1), 82–95.
56. Yezzi, A. (1998). Modified curvature motion for image smoothing and enhancement. IEEE Transactions on Image Processing, 7(3), 345–352.