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    "abbrevTitle": "SMPTE Mot. Imag. J",
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    "abstract": "The proliferation of video source formats, delivery mechanisms, and display devices has significantly increased broadcasters' needs for aspect ratio conversion. As a result, the authors have developed aspect processing, a framework for producing an arbitrary shape intelligently and flexibly. Aspect processing extends aspect ratio conversion to the definition of optimum viewing for a given screen size and shape. This paper presents new aspect processing technologies, which introduce a set of efficient criteria to automatically identify regions of interest in the picture and to process the picture accordingly. The paper also introduces a unique and novel technology—video seam carving, which works by altering the perspective of the image sequence to change between aspect ratios. These new technologies allow broadcasters to access new platforms and audiences with maximum re-use of systems and source material, resulting in a simpler and cheaper infrastructure.",
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    "authors": [
      {
        "bio": "Mike Knee graduated with a degree in Mathematics from Cambridge University, England, in 1980. He has since worked continuously in the broadcast industry, at BBC Research Department, Thomson Consumer Electronics in France, and for the last 15 years at Snell (formerly Snell & Wilcox) in the U.K. He has carried out original research in the areas of compression coding, transcoding and analysis, motion compensated de-interlacing, standards conversion, and other temporal processing of image sequences. Knee has been working on automatic scene analysis, based on image segmentation for application in mobile video, and on other novel methods for resizing and re-purposing video material. Much of the inspiration comes while cycling 40 km each day in the country lanes of Hampshire.",
        "name": "Mike Knee"
      },
      {
        "bio": "Roberta Piroddi received a Laurea degree in electronic engineering from the University of Cagliari (Italy) in 1999. She was awarded a Ph.D. degree in electronic engineering from the University of Surrey (U.K.) in 2004 for her work on object-based video segmentation. From 2002 to 2006, she worked as a Research Fellow, on biologically-inspired image processing, researching the properties of the human visual system and their application to computer vision, first at the Centre for Vision, Speech, and Signal Processing at the University of Surrey and then in the Communications and Signal Processing Group at Imperial College London. Piroddi joined Snell & Wilcox in 2006, working in the company's R&D department. Her current work focuses on developing algorithms for image processing and automatic content analysis, especially for conversion and repurposing, motion estimation, and 3D TV. Her research interests include multiple-feature image analysis, perceptual image saliency, and image-quality assessment.",
        "name": "Roberta Piroddi"
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      "holder": "Society of Motion Picture and Television Engineers, Inc.",
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        "note": "Parsed from NLM article XML (_source/SMPTE/HIGHWIRE/Source Bak/smptej/10.5594_J14873.xml)",
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      "year": "2010",
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        "note": "Parsed from NLM article XML (_source/SMPTE/HIGHWIRE/Source Bak/smptej/10.5594_J14873.xml)",
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    "docLabel": "SMPTE Motion Imaging Journal ( Volume: 119, Issue: 1, 2010)",
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    "docTitle": "Aspect Ratio Problems in Television Today and Some New Solutions",
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    "issn": {
      "electronic": "2160-2492",
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        "note": "Parsed from NLM article XML (_source/SMPTE/HIGHWIRE/Source Bak/smptej/10.5594_J14873.xml)",
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      "confidence": "high",
      "note": "Parsed from NLM article XML (_source/SMPTE/HIGHWIRE/Source Bak/smptej/10.5594_J14873.xml)",
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      "note": "Era-accurate journal title from SMPTE canonical repository (periodical parent at time of publication) (smpte-canonical-audit/canonicalLibrary.*.json)",
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      "note": "Parsed from NLM article XML (_source/SMPTE/HIGHWIRE/Source Bak/smptej/10.5594_J14873.xml)",
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    "pages": "35–41",
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      "confidence": "high",
      "note": "Parsed from NLM article XML (_source/SMPTE/HIGHWIRE/Source Bak/smptej/10.5594_J14873.xml)",
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        "confidence": "high",
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        "note": "Parsed from journal_metadata XML (_source/SMPTE/APTARA/DL Project Files/08202015 BACKFILE JOURNAL FRESH/smptej_119_1/MIJR15Vol119No1.xml)",
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      "confidence": "high",
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