Gradient-based Edge Detection Algorithms Research Articles

3 kHz PIV/OH-PLIF measurements in a gas turbine combustor at elevated pressure

This study was designed to test the feasibility of acquiring simultaneous PIV/OH-PLIF measurements at multi-kHz rates in a turbulent swirl flame at pressures relevant to modern industrial gas turbine combustors. To accomplish this, particle image velocimetry (PIV) and planar laser-induced fluorescence of the hydroxyl radical (OH-PLIF) were applied simultaneously at 3kHz to study the dynamics of a lean partially-premixed turbulent swirl-stabilized flame of natural gas in an optically accessible, high-pressure combustion test rig at 5bars. With 0.25mJ/pulse at 283nm for the OH-PLIF measurements, an average signal-to-noise ratio (SNR) of 4.1 was achieved over a region measuring 20×80mm. Absorption of the excitation laser proved to be the greatest challenge in this study, resulting in a significant variation in SNR from one side of the OH-PLIF images to the other. A procedure based on modeling the absorption according to the mean OH-distribution was used to semi-quantitatively correct for this effect. A gradient-based edge-detection algorithm was used to identify reaction zone locations in the resulting images. These were used to compute mean distributions of the flame surface density.With 2.5mJ/pulse at 532nm for the PIV system, velocity fields measuring 20×80mm were measured at a resolution of 1.25mm. Consistent with prior measurements in the burner, the flame shows strong thermo-acoustic pulsation, with a peak frequency of 388Hz. Phase-averages of the PIV and OH∗ data indicate these pulsations are driven by the same resonant feedback mechanism responsible for thermo-acoustic pulsation in the burner at atmospheric pressure. No evidence of a precessing vortex core, known to dominate the flow-field of the burner at atmospheric-pressure conditions, was observed.

Multi-scale edge detection on range and intensity images

Multi-scale feature extraction has become prominent in recent years. Additionally, processing images containing sparse or irregularly distributed data has become increasingly important, in particular with respect to the use of range image data. We present a family of multi-scale gradient-based edge detection algorithms that are suitable for use on either regularly or irregularly distributed image data; these algorithms can be applied directly to the range and intensity images without any image pre-processing. We quantitatively evaluate our algorithms on synthetic intensity and range images and also provide comparative visual output, using real images. The results demonstrate that this approach can be successfully applied to both range and intensity images, providing results that for intensity images are more accurate than from traditional gradient operators and for range images are more accurate than from the scan-line approximation.

Edge Detection on Hexagonal Structure

Gradient-based edge detection is a straightforward method to identify the edge points in the original grey-level image. It is intuitive that in the human vision system the edge points always appear where the grey-level value is greatly changed. Spiral Architecture is a relatively new image data structure that is inspired from anatomical considerations of the primate's vision. In Spiral Architecture, each image is represented as a collection of hexagonal pixels. Edge detection on Spiral Architecture has features of fast computation and accurate localization. In this paper, we present and compare gradient-based edge detection algorithms on Spiral Architecture. The experimental results show that the edge detection on Spiral Architecture outperforms that on traditional square image structure.

Gradient-based edge detection on a hexagonal structure

Gradient-based edge detection is a straightforward method to identify the edge points in the original grey-level image. It is intuitive that in the human vision system the edge points always appear where the grey-level value is greatly changed. Spiral Architecture is a relatively new image data structure that is inspired from anatomical considerations of the primate’s vision. In Spiral Architecture, each image is represented as a collection of hexagonal pixels. Edge detection on Spiral Architecture has features of fast computation and accurate localization. In this paper, we review the gradient-based edge detection algorithms on Spiral Architecture. An edge point is defined as a hexagonal pixel at which the magnitude of the gradient of brightness function assumes a local maximum.

Restorations of Aging Images Using Morphological Filters for Archiving

Old text images show many common signs of aging. These artifacts include noise, blurring, and fading. We will enhance these images to make these old texts readable so that we can archive the images and preserve our history.Morphological filters have been successfully employed in edge and object detection algorithms. These filters possess computational advantages over traditional edge detection methods such as gradient-based edge detection algorithms, as they are less computationally complex and are less influenced by extreme data values.In this paper, we propose new techniques for an improved morphological filter based object detection algorithm and image enhancement algorithm. We utilize components of image enhancement measures previously introduced by Agaian, Silver, and Panetta to focus on areas within an image that contain the most edge information. These algorithms are incorporated into our morphological filters as well as more complex pre- and post-processing methods.

Point-source suppression for atmospheric wave extraction from airglow imaging measurements

Spectroscopic images of atmospheric chemiluminescent airglow contain fundamental diagnostic information about the structure and dynamics of the upper atmosphere. However, westward movement of stars causes non-negligible contamination in the two-dimensional horizontal wavenumber spectra extracted from all-sky airglow images. The conventional star-removing scheme is not effective in attenuating the star artifacts in the spectra of emission intensities of gravity wave perturbation, hindering the subsequent calculation of the angular spectra and the momentum fluxes of the profiles. To address this problem, a gradient-based edge detection algorithm for locating and attenuating the undesired star signal is developed. Comparatively, this algorithm is more effective than the conventional star-removing methods. The algorithm is validated in the context of experimentally obtained spectra.