Drums, curve descriptors and affine invariant region matching

Zuliani, M.; Bertelli, Luca; Kenney, Charles; Chandrasekaran, Shivkumar; Manjunath, B.S.

doi:10.1016/j.imavis.2006.12.001

Cited by 9 publications

(4 citation statements)

References 37 publications

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“…The proof of Theorem 1 is also given in the Appendix 1. Theorem 1 illustrates that the whitening matrices used in [36, 37], in fact, are equivalent to each other. They both are specific forms of Theorem 1.…”

Section: Affine‐invariant Shape Representationmentioning

confidence: 98%

“…In [37], the covariance matrix is decomposed into the following form

C = {bold-italicC}^{1 / 2} {bold-italicC}^{1 / 2}

…”

Section: Affine‐invariant Shape Representationmentioning

confidence: 99%

“…They both are specific forms of Theorem 1. Hence, we do not develop other forms of whitening matrices, and that applied in [37] is selected in this paper.…”

Section: Affine‐invariant Shape Representationmentioning

confidence: 99%

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Whitening central projection descriptor for affine‐invariant shape description

Lan

Yang

Jiang

et al. 2013

IET Image Processing

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Section: Affine‐invariant Shape Representationmentioning

confidence: 98%

“…In [37], the covariance matrix is decomposed into the following form

C = {bold-italicC}^{1 / 2} {bold-italicC}^{1 / 2}

…”

Section: Affine‐invariant Shape Representationmentioning

confidence: 99%

See 1 more Smart Citation

Whitening central projection descriptor for affine‐invariant shape description

Lan

Yang

Jiang

et al. 2013

IET Image Processing

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“…After long-term research, many scholars proposed numerous image matching algorithms focused on the above two problems. In terms of computation methods of similarity measurement, there are mainly two types of similarity measurement as measurement based on grey level [13][14][15][16] and measurement based on features [17][18][19][20]. Regarding confirmation methods of searching range of homologous point, there are mainly following matching strategies like hierarchy matching strategy based on wavelet pyramid or Gauss pyramid [21,22], matching strategy based on epipolar-line constraint [23][24][25] matching strategy based on parallax continuity constraint [26][27][28], and so forth.…”

Section: Introductionmentioning

confidence: 99%

Stereo Image Matching for Vehicle-Borne Mobile Mapping System Based on Digital Parallax Model

Sheng

2011

International Journal of Vehicular Technology

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Considering automatic and effective stereo image matching for vehicle-borne mobile mapping system (VMMS), a new stereo image matching algorithm based on digital parallax model (DPM) is proposed in this paper. The new matching propagation strategy is designed in this algorithm, which includes two processes as DPM construction and parallax prediction. With some known matched points, the DPM of stereo image pairs is firstly constructed, and parameters for confirming conjugate epipolar line is also worked out. Then searching range during dense matching can be confirmed under constraints of DPM and epipolar line, which can improve matching speed and accuracy. Furthermore, to improve matching robustness, the computation model of similarity measurement combined with local structure feature and global color feature is designed. The new algorithm is applied to actual stereo images taken by VMMS to verify its validity. Experimental results show that the proposed approach has higher reliability and accuracy.

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