Detection of interest points using symmetry

Reisfeld, Daniel; Wolfson, Haim J.; Yeshurun, Yehezkel

doi:10.1109/iccv.1990.139494

Cited by 71 publications

(43 citation statements)

References 22 publications

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“…which searches for the best-"tting ellipse in the parameter space [x , y , a, b, ]. E Position optimization and face veri"cation through the detection of a symmetry axis [44]; a symmetry axis Fig. 6.…”

Section: Flfv -5ne Location and Face Veri5cationmentioning

confidence: 99%

Real-time face location on gray-scale static images

Maio

Maltoni

2000

Pattern Recognition

109

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Section: Flfv -5ne Location and Face Veri5cationmentioning

confidence: 99%

Real-time face location on gray-scale static images

Maio

Maltoni

2000

Pattern Recognition

109

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“…4 evaluates these features on a dataset of real world images. Moreover, the resulting symmetry detection is compared on two different data sets to the classic method by Reisfeld et al (Reisfeld et al, 1995) (Reisfeld et al, 1990) and to the newer method by Loy & Eklundh (Loy and Eklundh, 2006), which was reported as the best method in (Park et al, 2008) and (Rauschert et al, 2011). In the final Sec.…”

Section: Introductionmentioning

confidence: 99%

Detection of Symmetry Points in Images

Dalitz¹,

Pohle-Fröhlich²,

Bolten³

2013

Proceedings of the International Conference on Computer Vision Theory and Applications

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Abstract:This article proposes a new method for detecting symmetry points in images. Like other symmetry detection algorithms, it assigns a "symmetry score" to each image point. Our symmetry measure is only based on scalar products between gradients and is therefore both easy to implement and of low runtime complexity. Moreover, our approach also yields the size of the symmetry region without additional computational effort. As both axial symmetries as well as some rotational symmetries can result in a point symmetry, we propose and evaluate different methods for identifying the rotational symmetries. We evaluate our method on two different test sets of real world images and compare it to several other rotational symmetry detection methods.

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“…These algorithms use different techniques such as corner detection [8], local symmetry detection [9,10], convexity estimation [11,12] or blob detection [7,13,14,15] in order to detect interest points in an image.…”

Section: Introductionmentioning

confidence: 99%

Reducing Keypoint Database Size

Jamshy

Krupka

Yeshurun

2009

Image Analysis and Processing – ICIAP 2009

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Abstract. Keypoints are high dimensional descriptors for local features of an image or an object. Keypoint extraction is the first task in various computer vision algorithms, where the keypoints are then stored in a database used as the basis for comparing images or image features. Keypoints may be based on image features extracted by feature detection operators or on a dense grid of features. Both ways produce a large number of features per image, causing both time and space performance challenges when upscaling the problem.We propose a novel framework for reducing the size of the keypoint database by learning which keypoints are beneficial for a specific application and using this knowledge to filter out a large portion of the keypoints. We demonstrate this approach on an object recognition application that uses a keypoint database. By using leave one out K nearest neighbor regression we significantly reduce the number of keypoints with relatively small reduction in performance.

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Detection of interest points using symmetry

Cited by 71 publications

References 22 publications

Real-time face location on gray-scale static images

Real-time face location on gray-scale static images

Detection of Symmetry Points in Images

Reducing Keypoint Database Size

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