Affine invariant curve matching using normalization and curvature scale-space

Giannekou, V.; Tzouveli, Paraskevi; Avrithis, Yannis; Kollias, Stefanos

doi:10.1109/cbmi.2008.4564948

Cited by 3 publications

(4 citation statements)

References 13 publications

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“…Giannekou et al. have used affine invariant curve normalisation to overcome rotation and starting point issues [24]. The shallow convexity/concavity issues have already been successfully addressed by many other researchers [4, 9].…”

Section: Related Workmentioning

confidence: 99%

“…The limitations of basic CSS descriptor are the problem of shallow concavities, normalisation of rotation and starting point issues. Giannekou et al have used affine invariant curve normalisation to overcome rotation and starting point issues [24]. The shallow convexity/concavity issues have already been successfully addressed by many other researchers [4,9].…”

Section: Related Workmentioning

confidence: 99%

“…Many of the shape description methods have adapted contour normalisation for compact representation and fast matching of features [4,8,9,11,13,[24][25][26]. Further shape descriptors with affine invariance support is focused by many authors [24,[27][28][29][30][31]. Recently, owing to the availability of huge computational resources, many authors have used the feature fusion and multi-scale representation of shapes, which consequently increase the overall performances [2,10,17,[32][33][34][35][36].…”

Section: Related Workmentioning

confidence: 99%

“…Among various types of shape feature extraction, the concept of centroid distance plays significant role in literature, because it is easier to calculate and its representation is simple [12,[20][21][22][23]. Many of the shape description methods have adapted contour normalisation for compact representation and fast matching of features [4,8,9,11,13,[24][25][26]. Further shape descriptors with affine invariance support is focused by many authors [24,[27][28][29][30][31].…”

Section: Related Workmentioning

confidence: 99%

See 3 more Smart Citations

Extraction of compact boundary normalisation based geometric descriptors for affine invariant shape retrieval

Paramarthalingam¹,

Thankanadar²

2020

IET Image Processing

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Shape recognition and retrieval is a complex task on non-rigid objects and it can be effectively performed by using a set of compact shape descriptors. This paper presents a new technique for generating normalised contour points from shape silhouettes, which involves the identification of object contour from images and subsequently the object area normalisation (OAN) method is used to partition the object into equal part area segments with respect to shape centroid. Later, these contour points are used to derive six descriptors such as compact centroid distance (CCD), central angle (ANG), normalized points distance (NPD), centroid distance ratio (CDR), angular pattern descriptor (APD) and multi-triangle area representation (MTAR). These descriptors are a 1D shape feature vector which preserve contour and region information of the shapes. The performance of the proposed descriptors is evaluated on MPEG-7 Part-A, Part-B and multi-view curve dataset images. The present experiments are aimed to check proposed shape descriptor's robustness to affine invariance property and image retrieval performance. Comparative study has also been carried out for evaluating our approach with other state of the art approaches. The results show that image retrieval rate in OAN approach performs significantly better than that in other existing shape descriptors. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

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Section: Related Workmentioning

confidence: 99%