Comparison of Image Conversions Between Square Structure and Hexagonal Structure

He, Xiangjian; Li, Jianmin; Hintz, Tom

doi:10.1007/978-3-540-74607-2_24

Cited by 6 publications

(8 citation statements)

References 4 publications

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“…The light intensity of any given square pixel is assigned using the light intensity of the sub-pixel that is located at the centre of these 7×7 sub-pixels that were separated from the square pixel. The light intensities of these central subpixels are re-assigned through a tri-linear interpolation process as shown in [8]. …”

Section: Conversion From Virtual Hexagonal Structure To Square Structurementioning

confidence: 99%

“…This sub-pixel is called the reference sub-pixel of the corresponding virtual hexagonal pixel close to the centre of the hexagonal pixel like the sub-pixels P i (i=0,1,2,…,6) shown in Figure 3. The light intensity of each virtual hexagonal pixel is approximated through bi-linear interpolation process as shown in [8].…”

Section: Construction Of a Virtual Hexagonal Structurementioning

confidence: 99%

“…Because there has been no hardware available for image display and capture on hexagonal structure, in this section, we review a software approach to the construction of virtual hexagonal structure [7,8]. To construct hexagonal pixels, in [7], each square pixel was first separated into 7×7 small pixels, called subpixels.…”

Section: Construction Of a Virtual Hexagonal Structurementioning

confidence: 99%

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Local Binary Patterns on Hexagonal Image Structure

Jia

et al. 2007

7th IEEE International Conference on Computer and Information Technology (CIT 2007)

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Local Binary Pattern (LBP) was designed and widely used for efficient texture classification. It has been used for face recognition and has potential applications in many other research areas such as human detection. LBP provides a simple and effective way to represent patterns. Uniform LBPs play an important role for LBP-based pattern /object recognition as they include majority of LBPs. In this paper, we present LBP codes on hexagonal image structure. We show that LBPs defined on hexagonal structure have higher percentages of uniform LBPs that will lead to a more efficient and accurate recognition scheme for image classification.

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Section: Conversion From Virtual Hexagonal Structure To Square Structurementioning

confidence: 99%

Section: Construction Of a Virtual Hexagonal Structurementioning

confidence: 99%

Section: Construction Of a Virtual Hexagonal Structurementioning

confidence: 99%

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Local Binary Patterns on Hexagonal Image Structure

Jia

et al. 2007

7th IEEE International Conference on Computer and Information Technology (CIT 2007)

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“…Because there has been no hardware available for image display and capture on hexagonal structure, a software approach to the construction of virtual hexagonal structure as shown in [6] is used in this paper. To construct hexagonal pixels, each square pixel was first separated into 7x7 small pixels, called sub-pixels.…”

Section: Conversion Between Hexagonal and Square Structuresmentioning

confidence: 99%

“…1. A cluster of seven hexagonal pixels [6] To define the size of the virtual hexagonal structure, let me assume the size of the original square structure is 8Mx8N. Then, it is easy to compute that the hexagonal structure has (14M-I) rows and 8N columns.…”

Section: Conversion Between Hexagonal and Square Structuresmentioning

confidence: 99%

Canny edge detection on a virtual hexagonal image structure

Wei

et al. 2009

2009 Joint Conferences on Pervasive Computing (JCPC)

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Canny edge detector is the most popular tool for edge detection and has many applications in the areas of image processing, multimedia and computer vision. The Canny algorithm optimizes the edge detection through noise filtering using an optimal function approximated by the first derivative of a Gaussian. It identifies the edge points by computing the gradients of light intensity function based on the fact that the edge points likely appear where the gradient magnitudes are large. Hexagonal structure is an image structure alternative to traditional square image structure. Because all the existing hardware for capturing image and for displaying image are produced based on square structure, an approach that uses linear interpolation described for conversion between square and hexagonal structures. Gaussian filtering together with gradient computation is performed on the hexagonal structure. The pixel edge strengths on the square structure are then estimated before the thresholds of Canny algorithm are applied to determine the final edge map. The experimental results show the edge detection on hexagonal structure using static and video images, and the comparison with the results using Canny algorithm on square structure.

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Linear interpolation for image conversion between square structure and hexagonal structure

2007

Proc Appl Math and Mech

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Spiral Architecture (SA) is a relatively new and powerful image structure consisting of hexagonal pixels arranged in a onedimensional coordinate system. However, all the existing hardware for capturing image and for displaying image are produced based on traditional square image structure. In this paper, we present a software approach to represent an image on SA based on linear interpolation method. We perform edge detection on SA to demonstrate the advantages of image processing on SA.

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Comparison of Image Conversions Between Square Structure and Hexagonal Structure

Cited by 6 publications

References 4 publications

Local Binary Patterns on Hexagonal Image Structure

Local Binary Patterns on Hexagonal Image Structure

Canny edge detection on a virtual hexagonal image structure

Linear interpolation for image conversion between square structure and hexagonal structure

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