Three Dimensional Image Processing in Hexagonal Prism Lattice of Z3 Grid
Journal Title: Advances in Image and Video Processing - Year 2017, Vol 5, Issue 3
Abstract
A 2D hexagonal image is an array of what are called pixels which are the coordinates of the hexagonal lattice points decided by the linear horizontal rows and the nonlinear vertical zig-zags. A 2D hexagonal image could also be informally called as image slice or a matrix of pixel values arranged in a hexagonal array. A 3D hexagonal image is viewed as an ordered sequence of 2D hexagonal image slices arranged in the z-direction and the 3D arrangement of voxel values is called as a prism of voxel values. Most of the 3D hexagonal image processing operations are similar to those of 2D hexagonal image processing. 3D hexagonal images are processed with the help of 3D hexagonal scanning windows, whereas 2D hexagonal images are processed with the help of 2D hexagonal scanning windows. For instance, a 3D hexagonal image processing operation like 3D surface detection is carried out using analogous 2D edge detection algorithm on every image slice and the processed slices assembled to visualize 3D surface detected image. In fact, 2D contours of an image slice are called superficial features and closed surfaces of a 3D image are called volumetric features. One can always obtain surface detected version of a 3D hexagonal image by processing the 2D hexagonal image slices using 2D edge detection operation, and consequently the 3D surface detection operation is termed as 2.5D hexagonal image processing. One could also process the 3D hexagonal image data using a 3D surface detection algorithm, in which case it is termed as 3D hexagonal image processing. This is not the case with the operation of skeltonization. One cannot make use of 2.5D skeletonization operation of 2D image slices in order to get skeltonized version of the corresponding 3D image. In fact, one would come across discrepancies and differences when 2.5D skeletonization of 2D hexagonal image slices of a 3D hexagonal image is carried out instead of the direct 3D skeletonization of the 3D hexagonal image. This paper highlights certain 3D algorithms for processing 3D hexagonal images.
Authors and Affiliations
Mohd. Sherfuddin Khan, E. G. Rajan, Vijay H. Mankar
Keywords
Related Articles
- EP ID EP303184
- DOI 10.14738/aivp.53.3241
- Views 59
- Downloads 0
Design of DTCWT-DWT Image Compressor-Decompressor with Companding Algorithm
Discrete Wavelet Transform (DWT) has demonstrated advantages in image compression due to its time-frequency resolution property. Dual Tree Complex Wavelet Transform (DTCWT) in addition to the advantages of DWT supports a...
Gait Recognition System Using Gabor Wavelet and Active Gait Differential Image
Gait is a behavioural biometric process that serves to identify persons using their walking style. It is un-obstructive, not easy to conceal and offers distance recognition. Various approaches have been employed to impro...
Three Dimensional Image Processing in Hexagonal Prism Lattice of Z3 Grid
A 2D hexagonal image is an array of what are called pixels which are the coordinates of the hexagonal lattice points decided by the linear horizontal rows and the nonlinear vertical zig-zags. A 2D hexagonal image could a...
People Detection in Complex Scenes By Using An Improved and Robust HOG Descriptor
The detection of moving people in a complex scene filmed with a single camera is among the most difficult fields of research in vision by computer. In this work, we suggest improving the quality of detection methods base...
Denoising the Underwater Images by using Adaptive Filters
The Sound Navigation And Ranging and synthetic aperture radar images are perturbed by the multiplicative noise called speckle noise . The presence of speckle noise leads to incorrect analysis and has to be handled carefu...