This review presents a comprehensive and systematic overview of image-processing techniques that are used in automated feature-detection algorithms applied to solar data: i) image pre-processing procedures, ii) automated detection of spatial features, iii) automated detection and tracking of temporal features (events), and iv) post-processing tasks, such as visualization of solar imagery, cataloguing, statistics, theoretical modeling, prediction, and forecasting. For each aspect the most recent developments and science results are highlighted. We conclude with an outlook on future trends.

Image Processing Techniques and Feature Recognition in Solar Physics

IBM Research Report Proceedings of the First European Workshop on Object Orientation and Web Services Editors

IMAGE ENHANCEMENT TECHNIQUES APPLIED TO SOLAR FEATURE DETECTION by Artur J. Kowalski This dissertation presents the development of automatic image enhancement techniques for solar feature detection. The new method allows for detection and tracking of the evolution of filaments in solar images. Series of H-alpha full-disk images are taken in regular time intervals to observe the changes of the solar disk features. In each picture, the solar chromosphere filaments are identified for further evolution examination. The initial preprocessing step involves local thresholding to convert grayscale images into black-and-white pictures with chromosphere granularity enhanced. An alternative preprocessing method, based on image normalization and global thresholding is presented. The next step employs morphological closing operations with multi-directional linear structuring elements to extract elongated shapes in the image. After logical union of directional filtering results, the remaining noise is removed from the final outcome using morphological dilation and erosion with a circular structuring element. Experimental results show that the developed techniques can achieve excellent results in detecting large filaments and good detection rates for small filaments. The final chapter discusses proposed directions of the future research and applications to other areas of solar image processing, in particular to detection of solar flares, plages and sunspots. IMAGE ENHANCEMENT TECHNIQUES APPLIED TO SOLAR FEATURE DETECTION by Artur J. Kowalski A Dissertation Submitted to the Faculty of New Jersey Institute of Technology in Partial Fulfillment of the Requirements for the Degree of Doctor of Philosophy in Computer Science Department of Computer Science May 2003 Copyright CO 2003 by Artur J. Kowalski ALL RIGHTS RESERVED

Image enhancement techniques applied to solar feature detection

The formal representation of designed events allows the proposed system to be verified. The basic analysis operation of LTSA detects deadlocks, and may therefore demonstrate overall progress. The state transition models generated also indicate processes that can incorrectly make indepen- dent progress when interaction events are missing. It is possible to demonstrate that the system satisfies its behavioral requirements by stepping through event sequences. Scenarios and stories derived from the specific user information and general system requirements were used to deter- mine the test sequences.

Using Event Models in Grid Design

Scientific research and practical applications of solar physics require data and computational services to be integrated seamlessly and efficiently. The European Grid for Solar Observations (EGSO) leverages grid-oriented concepts and technology to provide a high-performance infrastructure for solar applications. In this paper, we describe the active brokerage technique adopted in EGSO to improve the access to and flow of solar data. The technique is based on a multi-tier management of metadata.

Improving Communication Through Active Brokerage: the EGSO Data Grid

We present three categories of sites from which solar data can be retrieved. We describe the retrieval software systems associated with these sites and then present software systems used for analyzing solar data. We show that both retrieval and analysis systems provide only limited global organization and/or standard access of data. We thus also discuss extensions to these systems, including data combination (or mapping) software, reusable software components, and visualization software. We argue that such extensions could eventually provide die level of software support expected by users.

D.M. Zarro

Papers

Steps towards a virtual solar observatory