1. Solar Image Recognition Workshop, Brussels, 23 & 24 Oct. The Detection of Filaments in Solar Images Dr. Rami Qahwaji Department of Electronic Imaging and Media Communications School of Informatics, Bradford University Richmond Road, Bradford BD7 1DP, UK Tel. +44(0)1274 236078 Fax. +44(0)1274 23372

2. Solar Image Recognition Workshop, Brussels, 23 & 24 Oct. Organisation of Talk Objective Objective Hybrid System Hybrid System Detection of central solar region Detection of central solar region Image enhancement and filtering Image enhancement and filtering Detection of filament regions Detection of filament regions Evaluating the performance Evaluating the performance Future research directions and related topics Future research directions and related topics

3. Solar Image Recognition Workshop, Brussels, 23 & 24 Oct. A fast detection system that provides automatic detection for the filaments in solar images that are observed from ground-based instruments, is presented. A fast detection system that provides automatic detection for the filaments in solar images that are observed from ground-based instruments, is presented. The aim is to design a robust and efficient detection system that can extract the positions of filaments regardless of their shapes, positions or sizes. The aim is to design a robust and efficient detection system that can extract the positions of filaments regardless of their shapes, positions or sizes. The automatic detection is carried out using hybrid techniques that detects the central solar region, filter the image and then determine the position and size of the The automatic detection is carried out using hybrid techniques that detects the central solar region, filter the image and then determine the position and size of the filament regions. filament regions.

4. Solar Image Recognition Workshop, Brussels, 23 & 24 Oct. The input to the detection system is a raw uncleaned solar image. The input to the detection system is a raw uncleaned solar image. The detection process is divided to three major steps. The detection process is divided to three major steps. Firstly, the central region of the solar disk is located within the image. Firstly, the central region of the solar disk is located within the image. Secondly, the detected central region is enhanced and filtered. Secondly, the detected central region is enhanced and filtered. Finally, the regions containing filaments within the central solar region are highlighted. Finally, the regions containing filaments within the central solar region are highlighted.

5. Solar Image Recognition Workshop, Brussels, 23 & 24 Oct. Stage 1: The detection of the central solar region The central region in the solar disk is detected in order to avoid the limb darkening effect and the detection of incomplete filaments that extend to or around the solar limb. The central region in the solar disk is detected in order to avoid the limb darkening effect and the detection of incomplete filaments that extend to or around the solar limb. The morphological Filling algorithm is the corner stone for the detection. The morphological Filling algorithm is the corner stone for the detection.

6. Solar Image Recognition Workshop, Brussels, 23 & 24 Oct. The Modified HMT Algorithm Step 1. Converting the input image to a binary image: The splitting technique converts the input image to the binary image X. Step 2. Implementing the hit-filter: X is correlated hexagonally with a hit filter that detects the horizontal and vertical edges to produce image H. Step 3. Implementing the miss-filter: X is correlated with the negative of the hit filter to produce M. The detected edges are represented by white pixels M. Step 4. Final AND operation: H is ANDed with M to produce the output image.

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8. Determining the Density of Objects using the Watershed Transform (WST) The digital image is represented in the 3-D space by considering its brightness as a height coming out from the page toward the observer. Hence, each binary image consists of mountains and valleys. The valleys are assumed to be covered by rising water. At the point of intersection a WST edge is found.

9. Solar Image Recognition Workshop, Brussels, 23 & 24 Oct. Detecting the central solar region Step 1. Apply HMT to an input image in order to obtain image Z. Step 2. Apply WST to image Z to obtain image W. Step 3. For every horizontal edge in W, if a WST pixel emerges from it then its position is highlighted. Step 4. Each WST line detected is processed as follows: W is scanned to determine if the WST line is vertically continuous. The WST line does not suffer from large horizontal displacements. The WST line goes downward until it meets a horizontal edge. If all true, then then the WST line is given a false colour. Step 5. For every false – coloured WST line, the black pixels that separate it from its surrounding edge pixels are converted to white to produce image F. Step 6. Output image is obtained by ANDing F with Input image.

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11. Stage 2: Image Filtering and Enhancement Solar image enhancement aims to increase the grey- scale differences between the active regions and filaments. Solar image enhancement aims to increase the grey- scale differences between the active regions and filaments. This is implemented by finding the mean of the central region and finding the maximum and minimum grey scale values. The values of the pixels are re- calculated in order to maximise the differences with the mean without changing the mean's value. This is implemented by finding the mean of the central region and finding the maximum and minimum grey scale values. The values of the pixels are re- calculated in order to maximise the differences with the mean without changing the mean's value. Intensity filtering using low thresholds is applied afterwards. The filaments and filaments-like regions are darker in colour which enables an intensity filter with a low threshold to indicate their positions and to eliminate background and active regions. Intensity filtering using low thresholds is applied afterwards. The filaments and filaments-like regions are darker in colour which enables an intensity filter with a low threshold to indicate their positions and to eliminate background and active regions.

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13. Stage 3: The detection of Filament Regions This stage aims to detect the exact locations of filaments with accuracy and speed. This stage aims to detect the exact locations of filaments with accuracy and speed. The mosaic technique is implemented to enhance the quality of the filtered regions. The mosaic technique is implemented to enhance the quality of the filtered regions. The actual detection of filaments is carried out using a sliding window that searches through the mosaic image The actual detection of filaments is carried out using a sliding window that searches through the mosaic image

14. Solar Image Recognition Workshop, Brussels, 23 & 24 Oct. The Mosaic Images A mosaic image is constructed by decreasing the resolution of the input image by a certain factor (n). A mosaic image is constructed by decreasing the resolution of the input image by a certain factor (n). The mosaic principle is used to enhance the detection performance. The mosaic principle is used to enhance the detection performance.

15. Solar Image Recognition Workshop, Brussels, 23 & 24 Oct. Detecting the Filaments in the Mosaic Image A sliding window is moved along the mosaic image. Every time the window reaches the upper edge of a mosaic filament, a search for the lower, left and right boundaries for the filament region is carried out. A sliding window is moved along the mosaic image. Every time the window reaches the upper edge of a mosaic filament, a search for the lower, left and right boundaries for the filament region is carried out. Once the boundaries of the filament are determined, the filament pixels are painted in a false colour to prevent them from being detected again by another moving window. Once the boundaries of the filament are determined, the filament pixels are painted in a false colour to prevent them from being detected again by another moving window. At the end of this process, variable size windows are defined and stored in multiple size arrays and represented later in a data file that can be input to a neural network or region growing code to be verified as a filament region. At the end of this process, variable size windows are defined and stored in multiple size arrays and represented later in a data file that can be input to a neural network or region growing code to be verified as a filament region.

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17. The detection algorithm detects the filaments in a 1024 × 1024 image in about 0.8 seconds using P4-2.0 G Hz PC with 512 M Byte RAM.

18. Solar Image Recognition Workshop, Brussels, 23 & 24 Oct. Evaluating the performance Defining error metrics In order to evaluate the performance of the filament detector, the following two error rates are introduced: In order to evaluate the performance of the filament detector, the following two error rates are introduced:  The false acceptance rate (FAR): probability of a non- filament object being detected as a filament.  The false rejection rate (FRR): the probability of a filament not being detected because it is considered to be a non-filament object.

19. Solar Image Recognition Workshop, Brussels, 23 & 24 Oct. The detection algorithm was applied to Meudon H- Alpha solar images for the period from 1/ 7 / 2001 till 5/ 8/ 2001. The detection algorithm was applied to Meudon H- Alpha solar images for the period from 1/ 7 / 2001 till 5/ 8/ 2001. For every H-alpha image used, a corresponding manually constructed synoptic map that contains the locations of filaments exists. For every H-alpha image used, a corresponding manually constructed synoptic map that contains the locations of filaments exists. These filament maps are obtained manually using the subjective analysis of solar observers. These filament maps are obtained manually using the subjective analysis of solar observers. The FAR and FRR error parameters are established by comparing the detected filaments, which are generated using the current detection algorithm, with those detected manually and recorded in the synoptic maps. The FAR and FRR error parameters are established by comparing the detected filaments, which are generated using the current detection algorithm, with those detected manually and recorded in the synoptic maps.

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22. The way Forward In the future, a neural network verification stage will be added to the current detection technique for verification purposes. In the future, a neural network verification stage will be added to the current detection technique for verification purposes. The neural network could be also used to provide understanding of the relation between the filament regions that are detected over successive days. The neural network could be also used to provide understanding of the relation between the filament regions that are detected over successive days. Elimination of sun spots using regional morphological filters. Elimination of sun spots using regional morphological filters. Detection of neutral lines in MDI images and comparing these results with the automatic detection with filaments and Active regions. Detection of neutral lines in MDI images and comparing these results with the automatic detection with filaments and Active regions.

23. Solar Image Recognition Workshop, Brussels, 23 & 24 Oct. Enhancing the Solar image slope=(float)(i-j)/(float)(pos_right[i]-pos_left[j]); new_y=i-slope*(pos_right[i]-x);

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25. Detection of Neutral lines The neutral lines in the MDI images are detected by applying morphological edge detection to the smoothed image. The neutral lines in the MDI images are detected by applying morphological edge detection to the smoothed image. Multi-level smoothing is designed. Multi-level smoothing is designed. The hexagonal sampling is applied to find the average energy for every pixel in the image. The hexagonal sampling is applied to find the average energy for every pixel in the image. The average energy is then compared with a local region threshold that is determined based on the smoothing level. The average energy is then compared with a local region threshold that is determined based on the smoothing level.

26. Solar Image Recognition Workshop, Brussels, 23 & 24 Oct. The smoothing is carried out using the following steps: The smoothing is carried out using the following steps: 1. the energy surrounding every pixel is calculated in a hexagonal manner. 2. The output energy is compared with an energy threshold. According to the comparison outcome, three regions will be obtained: The blue regions are obtained when the energy exceeds the detection threshold. The blue regions are obtained when the energy exceeds the detection threshold. The red regions are obtained when the energy is less than the detection threshold. The red regions are obtained when the energy is less than the detection threshold. White regions are obtained when the energy lies within the detection threshold region. White regions are obtained when the energy lies within the detection threshold region.

27. Solar Image Recognition Workshop, Brussels, 23 & 24 Oct. Additional step is added to handle the white regions. Additional step is added to handle the white regions. The energy surrounding every white pixel is again calculated using a larger hexagonal filter and again the calculated energy is compared. If no decision can be made then a larger window is used and the process is repeated. The energy surrounding every white pixel is again calculated using a larger hexagonal filter and again the calculated energy is compared. If no decision can be made then a larger window is used and the process is repeated.

28. Solar Image Recognition Workshop, Brussels, 23 & 24 Oct. The input image, Taken on the 23 rd July, 2002

29. Solar Image Recognition Workshop, Brussels, 23 & 24 Oct. Different smoothing levels

30. Solar Image Recognition Workshop, Brussels, 23 & 24 Oct. Current work Comparing the neutral lines with active regions and with filaments Comparing the neutral lines with active regions and with filaments The statistical distribution of the neutral lines within the automatically detected active regions and filaments is studied. The aim is to verify whether a relation exist or can be established. The statistical distribution of the neutral lines within the automatically detected active regions and filaments is studied. The aim is to verify whether a relation exist or can be established.

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32. Thanks for listening