Overview of image processing tools to extract physical information from JET videos

In magnetic confinement nuclear fusion devices such as JET, the last few years have witnessed a significant increase in the use of digital imagery, not only for the surveying and control of experiments, but also for the physical interpretation of results. More than 25 cameras are routinely used for imaging on JET in the infrared (IR) and visible spectral regions. These cameras can produce up to tens of Gbytes per shot and their information content can be very different, depending on the experimental conditions. However, the relevant information about the underlying physical processes is generally of much reduced dimensionality compared to the recorded data. The extraction of this information, which allows full exploitation of these diagnostics, is a challenging task. The image analysis consists, in most cases, of inverse problems which are typically ill-posed mathematically. The typology of objects to be analysed is very wide, and usually the images are affected by noise, low levels of contrast, low grey-level in-depth resolution, reshaping of moving objects, etc. Moreover, the plasma events have time constants of ms or tens of ms, which imposes tough conditions for real-time applications. On JET, in the last few years new tools and methods have been developed for physical information retrieval. The methodology of optical flow has allowed, under certain assumptions, the derivation of information about the dynamics of video objects associated with different physical phenomena, such as instabilities, pellets and filaments. The approach has been extended in order to approximate the optical flow within the MPEG compressed domain, allowing the manipulation of the large JET video databases and, in specific cases, even real-time data processing. The fast visible camera may provide new information that is potentially useful for disruption prediction. A set of methods, based on the extraction of structural information from the visual scene, have been developed for the automatic detection of MARFE (multifaceted asymmetric radiation from the edge) occurrences, which precede disruptions in density limit discharges. An original spot detection method has been developed for large surveys of videos in JET, and for the assessment of the long term trends in their evolution. The analysis of JET IR videos, recorded during JET operation with the ITER-like wall, allows the retrieval of data and hence correlation of the evolution of spots properties with macroscopic events, in particular series of intentional disruptions.

[1]  A Murari,et al.  A universal support vector machines based method for automatic event location in waveforms and video-movies: applications to massive nuclear fusion databases. , 2010, The Review of scientific instruments.

[2]  E. Gauthier,et al.  ITER-like wide-angle infrared thermography and visible observation diagnostic using reflective optics , 2007 .

[3]  Neville C. Luhmann,et al.  Investigating pellet ablation dynamics at ASDEX Upgrade , 2012 .

[4]  K. P. Chow,et al.  Efficient Block-based Motion Segmentation Method using Motion Vector Consistency , 2005, MVA.

[5]  T. Strohmer,et al.  Gabor Analysis and Algorithms , 2012 .

[6]  Daniel Lemire,et al.  Streaming Maximum-Minimum Filter Using No More than Three Comparisons per Element , 2006, Nord. J. Comput..

[7]  Berthold K. P. Horn,et al.  Determining Optical Flow , 1981, Other Conferences.

[8]  Rachid Deriche,et al.  Symmetrical Dense Optical Flow Estimation with Occlusions Detection , 2002, ECCV.

[9]  J. Vega,et al.  Phase Congruency Image Classification for MARFE Detection on JET with a Carbon Wall , 2012 .

[10]  J. Contributors,et al.  Scrape-off layer properties of ITER-like limiter start-up plasmas in JET , 2013 .

[11]  M. Davies,et al.  Approximating optical flow within the MPEG-2 compressed domain , 2005 .

[12]  Michael Elad,et al.  Image Denoising Via Sparse and Redundant Representations Over Learned Dictionaries , 2006, IEEE Transactions on Image Processing.

[13]  Angelos Vourlidas,et al.  Analysis of the Velocity Field of CMEs Using Optical Flow Methods , 2006 .

[14]  J. Weickert,et al.  Lucas/Kanade meets Horn/Schunck: combining local and global optic flow methods , 2005 .

[15]  Guillermo Sapiro,et al.  Discriminative learned dictionaries for local image analysis , 2008, 2008 IEEE Conference on Computer Vision and Pattern Recognition.

[16]  Determination of the pellet parameters by image processing methods , 2011 .

[17]  Ming-Kuei Hu,et al.  Visual pattern recognition by moment invariants , 1962, IRE Trans. Inf. Theory.

[18]  Mei-Chen Yeh,et al.  Fast Human Detection Using a Cascade of Histograms of Oriented Gradients , 2006, 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06).

[19]  J. Vega,et al.  Supervised Image Processing Learning for Wall MARFE Detection Prior to Disruption in JET With a Carbon Wall , 2014 .

[20]  A. Murari,et al.  Application of optical flow method for imaging diagnostic in JET , 2010 .

[21]  J. Contributors,et al.  A new radiation-hard endoscope for divertor spectroscopy on JET , 2013 .

[22]  Vladimir N. Vapnik,et al.  The Nature of Statistical Learning Theory , 2000, Statistics for Engineering and Information Science.

[23]  A. Murari,et al.  Motion estimation within the MPEG video compressed domain for JET plasma diagnostics , 2011 .

[24]  M. Elad,et al.  $rm K$-SVD: An Algorithm for Designing Overcomplete Dictionaries for Sparse Representation , 2006, IEEE Transactions on Signal Processing.

[25]  Status of the JET high frequency pellet injector , 2013 .

[26]  Takeo Kanade,et al.  An Iterative Image Registration Technique with an Application to Stereo Vision , 1981, IJCAI.

[27]  N. Balshaw,et al.  A wide angle view imaging diagnostic with all reflective, in-vessel optics at JET , 2013 .

[28]  S. Wolfe,et al.  Marfe: an edge plasma phenomenon , 1984 .

[29]  Yan-ping Chen,et al.  Fast hog feature computation based on CUDA , 2011, 2011 IEEE International Conference on Computer Science and Automation Engineering.

[30]  P. Thomas,et al.  Upgrade of the infrared camera diagnostics for the JET ITER-like wall divertor. , 2012, The Review of scientific instruments.

[31]  A. Murari,et al.  An Original Method for Spot Detection and Analysis for Large Surveys of Videos in JET , 2014, IEEE Transactions on Plasma Science.

[32]  Eero P. Simoncelli,et al.  Image quality assessment: from error visibility to structural similarity , 2004, IEEE Transactions on Image Processing.

[33]  P. Lang,et al.  A fast framing camera system for observation of acceleration and ablation of cryogenic hydrogen pellet in ASDEX Upgrade plasmas , 2004 .

[34]  Bo Shen,et al.  Direct feature extraction from compressed images , 1996, Electronic Imaging.

[35]  M. N. A. Beurskens,et al.  JET ITER-like wall—overview and experimental programme , 2011 .

[36]  Richard Szeliski,et al.  A Database and Evaluation Methodology for Optical Flow , 2007, 2007 IEEE 11th International Conference on Computer Vision.

[37]  Carsten Rother,et al.  Discrete-Continuous Optimization for Optical Flow Estimation , 2009, Statistical and Geometrical Approaches to Visual Motion Analysis.

[38]  Nigel J. B. McFarlane,et al.  Segmentation and tracking of piglets in images , 1995, Machine Vision and Applications.

[40]  Carlos Silva,et al.  Fast visible camera installation and operation in JET , 2008 .

[41]  Tony F. Chan,et al.  Active contours without edges , 2001, IEEE Trans. Image Process..

[42]  J Vega,et al.  Intelligent technique to search for patterns within images in massive databases. , 2008, The Review of scientific instruments.

[43]  M. Lehnen,et al.  Disruption heat loads and their mitigation in JET with the ITER-like wall , 2013 .

[44]  Patrick Pérez,et al.  Dense estimation and object-based segmentation of the optical flow with robust techniques , 1998, IEEE Trans. Image Process..

[45]  S. Mallat,et al.  Adaptive greedy approximations , 1997 .

[46]  A. Murari,et al.  A 10 000-Image-per-Second Parallel Algorithm for Real-Time Detection of MARFEs on JET , 2013, IEEE Transactions on Plasma Science.

[47]  V. Moncada,et al.  Integrated software for imaging data analysis applied to edge plasma physic and operational safety , 2011 .

[48]  P Arena,et al.  Image processing with cellular nonlinear networks implemented on field-programmable gate arrays for real-time applications in nuclear fusion. , 2010, The Review of scientific instruments.

[49]  J. Vega,et al.  Overview of intelligent data retrieval methods for waveforms and images in massive fusion databases , 2009 .

[50]  J. F. Delmond,et al.  Algorithms for the Automatic Identification of MARFEs and UFOs in JET Database of Visible Camera Videos , 2010, IEEE Transactions on Plasma Science.

[51]  Robyn A. Owens,et al.  Feature detection from local energy , 1987, Pattern Recognit. Lett..

[52]  K. McCormick,et al.  Major results from the stellarator Wendelstein 7-AS , 2008 .

[53]  H. Zohm,et al.  CORRIGENDUM: Pellet fuelling of ELMy H mode discharges on ASDEX upgrade , 1996 .

[54]  Bill Triggs,et al.  Histograms of oriented gradients for human detection , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).