Fast Multiscale Operator Development for Hexagonal Images

Gardiner, Bryan; Coleman, Sonya; Scotney, Bryan

doi:10.1007/978-3-642-03798-6_29

Cited by 5 publications

(4 citation statements)

References 12 publications

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“…In previous work [3] we have developed adaptive hexagonal image processing operators for direct use on hexagonal pixel-based images. In that work however, the operator adaptivity was achieved by creating an initial 7-point hexagonal mask, equivalent to the standard 9-point mask on a rectangular pixel-based image, and increasing the mask size at each stage by inclusion of a layer of immediately neighbouring values around the edge of the current mask.…”

Section: Laplacian Operatorsmentioning

confidence: 99%

Coarse Scale Feature Extraction Using the Spiral Architecture Structure

Coleman

Scotney

Gardiner

2010

2010 20th International Conference on Pattern Recognition

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The Spiral Architecture has been developed as a fast way of indexing a hexagonal pixel-based image. In combination with spiral addition and spiral multiplication, methods have been developed for hexagonal image processing operations such as translation and rotation. Using the Spiral Architecture as the basis for our operator structure, we present a general approach to the computation of adaptive coarse scale Laplacian operators for use on hexagonal pixelbased images. We evaluate the proposed operators using simulated hexagonal images and demonstrate improved performance when compared with rectangular Laplacian operators such as Marr-Hildreth.

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Section: Laplacian Operatorsmentioning

confidence: 99%

Coarse Scale Feature Extraction Using the Spiral Architecture Structure

Coleman

Scotney

Gardiner

2010

2010 20th International Conference on Pattern Recognition

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“…In this paper we extend the work in [8] to present procedure for the corner detection on hexagonal images. In Section 2, we present an overview of the hexagonal image representation followed by the hexagonal operator design in Section 3.…”

Section: Introductionmentioning

confidence: 98%

“…To date, work done using hexagonal images has focussed on the development of hexagonally structured architectures for image representation and addressing [3,4,5], and a theoretical framework for signal modelling and transforms [6]; however, much less research has been undertaken on the development and application of image processing techniques for direct use on such image structures. Some standard algorithms have been extended from rectangular to hexagonal arrays in simple cases and recently edge detection operators have been presented in [7,8] but to date corner detection has not been investigated using hexagonal pixel based images.…”

Section: Introductionmentioning

confidence: 99%

Corner detection on hexagonal pixel based images

Liu

Coleman

Kerr

et al. 2011

2011 18th IEEE International Conference on Image Processing

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Corner detection is used in many computer vision applications that require fast and efficient feature matching. In addition, hexagonal pixel based images have been recently investigated for image capture and processing due to their ability to represent curved structures that are common in real images better than traditional rectangular pixel based images. Therefore, we present an approach to corner detection on hexagonal images and demonstrate that accuracy is comparable to well-known existing corner detectors applied to rectangular pixel based images.

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“…As illustrated in Fig. 1, the past few years have witnessed the widespread application of hexagonal grids in digital image processing [1], [4], [5], geospatial information subdivision organization [6], combat environment modeling [7], new economic geography [8], and intelligent transportation system [9], etc.…”

Section: Introductionmentioning

confidence: 99%

Hexagonal Convolutional Neural Networks for Hexagonal Grids

Luo

Zhang

et al. 2019

IEEE Access

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Hexagonal grids use a hierarchical subdivision tessellation to cover the entire plane or sphere. Due to the 6-fold rotational symmetry, hexagonal grids have some advantages (e.g. isoperimetry, equidistant neighbors, and uniform connectivity) over quadrangular and triangular girds, which makes them suitable to tackle tasks of geospatial information processing and intelligent decision-making. In this paper, we first introduce some applications based on the hexagonal grids. Then, we introduce the planer and spherical hexagonal grids and analyze the group representations for them, we review geometric deep learning, some Convolutional Neural Networks (CNNs) for hexagonal grids, and group-based equivariant convolution. Next in importance, we propose the HexagonNet for hexagonal grids, and define a new convolution operator and pooling operator. Finally, in order to evaluate the effectiveness of the proposed HexagonNet, we perform experiments on two tasks: aerial scene classification on the aerial image dataset (AID), and 3D shape classification on the ModelNet40 dataset. The experimental results verify the practical applicability of the HexagonNet given some fixed parameter budgets.

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Fast Multiscale Operator Development for Hexagonal Images

Cited by 5 publications

References 12 publications

Coarse Scale Feature Extraction Using the Spiral Architecture Structure

Coarse Scale Feature Extraction Using the Spiral Architecture Structure

Corner detection on hexagonal pixel based images

Hexagonal Convolutional Neural Networks for Hexagonal Grids

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