Hexagonal structure for intelligent vision

Using hexagonal grids to represent digital images have been studied for more than 40 years. Increased processing capabilities of graphic devices and recent improvements in CCD technology have made hexagonal sampling attractive for practical applications and brought new interests on this topic. The hexagonal structure is considered to be preferable to the rectangular structure due to its higher sampling efficiency, consistent connectivity and higher angular resolution and is even proved to be superior to square structure in many applications. Since there is no mature hardware for hexagonal-based image capture and display, square to hexagonal image conversion has to be done before hexagonal-based image processing. Although hexagonal image representation and storage has not yet come to a standard, experiments based on existing hexagonal coordinate systems have never ceased. In this paper, we firstly introduced general reasons that hexagonally sampled images are chosen for research. Then, typical hexagonal coordinates and addressing schemes, as well as hexagonal based image processing and applications, are fully reviewed. © 2005 IEEE

A triple-diagonal gradient-based edge detection

Gradient-based edge detection is a straightforward method to identity the edge points in the original grey-level image. It is consistent with the intuition that in the human vision system the edge points always appear where the change of grey-level is greatest within their neighbourhood. In this paper, triple-diagonal gradient-based edge detection is introduced. It is based on the features of Spiral Architecture and computes the gradients in three diagonal directions instead of approximating the gradient in one direction only as the traditional methods do. Essentially, it improves the accuracy for locating edge points. As a result, it does not need any supplementary processing to enhance the edge map

Review on Performance Analysis of Square Pixel and Hexagonal Pixel Structure in Image Processing

Image processing is very important in several applications and have been using in them very efficiently. In image processing most applications data is collected and displayed in square pixels but most of the time the feature extraction from an image like image segmentation, image detection, edge detection, texture recognition, etc becomes difficult to recognize in square pixel image. So, one new approach called hexagonal pixel structure has been designed to overcome the problems of square pixel structure. Hexagon pixel is advantageous because of its higher symmetry, higher sampling efficiency, equidistance, greater angular resolution, less aliasing effect, consistent connectivity. Out of the many advantages for the hexagonal pixel structure most important is its resemblance with the arrangement of photoreceptors in the human eye. Due to the arrangement the amount of pixels required is very less. So, in this work, quality representation of image which having square pixel structures are compared with images having hexagonal pixel structure

Edge Detection on Spiral Architecture: an Overview

Abstract Gradient-based edge detection is a straightforward method to identify the edge points in the origina