Multi-scale Kernel Operators for Reflection and Rotation Symmetry: Further Achievements

Kondra, Shripad; Petrosino, Alfredo; Iodice, Sara

doi:10.1109/cvprw.2013.39

Cited by 29 publications

(16 citation statements)

References 8 publications

(9 reference statements)

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“…Four different methods for mirror symmetry segment detection were compared in [8] -CVPR 2013 Competition: [7,12,11,9]. Figure 2, taken from the competition report, shows their performance.…”

Section: Previous Workmentioning

confidence: 99%

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A convolutional approach to reflection symmetry

Cicconet

Birodkar

Lund

et al. 2017

Pattern Recognition Letters

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Abstract. We present a convolutional approach to reflection symmetry detection in 2D. Our model, built on the products of complex-valued wavelet convolutions, simplifies previous edgebased pairwise methods. Being parameter-centered, as opposed to feature-centered, it has certain computational advantages when the object sizes are known a priori, as demonstrated in an ellipse detection application. The method outperforms the best-performing algorithm on the CVPR 2013 Symmetry Detection Competition Database in the single-symmetry case. Code and a new database for 2D symmetry detection is available.

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Section: Previous Workmentioning

confidence: 99%

“…In [7], the algorithm operates in three steps: (1) correlation measures (using the SIFT descriptor) are computed along discrete directions; (2) symmetrical regions are identified by looking for matches in the directions characterized by maximum correlations; steps (1) and (2) are performed at different scales.…”

Section: Previous Workmentioning

confidence: 99%

A convolutional approach to reflection symmetry

Cicconet

Birodkar

Lund

et al. 2017

Pattern Recognition Letters

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show abstract

“…Lee and Liu [21] generalized reflection symmetry detection to a glide reflection symmetry detection problem by estimating a set of contiguous local straight reflection axes. Kondra et al [22] presented a multi-scale strategy to detect symmetry. First, SIFT correlation measures are computed at different directions of the same scale.…”

Section: Introductionmentioning

confidence: 99%

Symmetric Face Normalization

Liu

Ban

2019

Symmetry

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Image registration is an important process in image processing which is used to improve the performance of computer vision related tasks. In this paper, a novel self-registration method, namely symmetric face normalization (SFN) algorithm, is proposed. There are three contributions in this paper. Firstly, a self-normalization algorithm for face images is proposed, which normalizes a face image to be reflection symmetric horizontally. It has the advantage that no face model needs to be built, which is always severely time-consuming. Moreover, it can be considered as a pre-processing procedure which greatly decreases the parameters needed to be adjusted. Secondly, an iterative algorithm is designed to solve the self-normalization algorithm. Finally, SFN is applied to the between-image alignment problem, which results in the symmetric face alignment (SFA) algorithm. Experiments performed on face databases show that the accuracy of SFN is higher than 0.95 when the translation on the x-axis is lower than 15 pixels, or the rotation angle is lower than 18°. Moreover, the proposed SFA outperforms the state-of-the-art between-image alignment algorithm in efficiency (about four times) without loss of accuracy.

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“…Such differences will be pointed out in the following Sections. Moreover a similar procedure, though acting on extracted interest points, entered the 2013 CVPR competition as well [4]. According to the survey paper for the 2013 workshop [5], that procedure was clearly outperformed by two other methods participating to the workshop [6] [7].…”

Section: Introduction and Related Workmentioning

confidence: 99%

InnerSpec: Technical Report

Guerrini

Gnutti

Leonardi

2017

2017 IEEE International Conference on Computer Vision Workshops (ICCVW)

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In this report we describe "InnerSpec", an approach for symmetric object detection that is based both on the computation of a symmetry measure for each pixel and on gradient information analysis. The symmetry value is obtained as the energy balance of the even-odd decomposition of an oriented square patch with respect to its central axis. Such an operation is akin to the computation of a row-wise convolution in the midpoint. The candidate symmetry axes are then identified through the localization of peaks along the direction perpendicular to each considered angle. These axes are finally evaluated by computing the image gradient in their neighborhood, in particular checking whether the gradient information displays specular characteristics.

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Multi-scale Kernel Operators for Reflection and Rotation Symmetry: Further Achievements

Cited by 29 publications

References 8 publications

A convolutional approach to reflection symmetry

A convolutional approach to reflection symmetry

Symmetric Face Normalization

InnerSpec: Technical Report

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