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Reconstruction-Free Compressive Vision for Surveillance Applications Andreas Spanias

Reconstruction-Free Compressive Vision for Surveillance Applications

Medios de pago

    Reconstruction-Free Compressive Vision for Surveillance Applications

    Editorial: Morgan & Claypool Publishers

    Idioma: Inglés

    ISBN: 9781681735559

    Formatos: PDF (con DRM de Adobe)

    Compatibles con: Windows, Mac, iOS, Android & eReaders

    Medios de pago
      Reconstruction-Free Compressive Vision for Surveillance Applications Andreas Spanias

      Reconstruction-Free Compressive Vision for Surveillance Applications

      Medios de pago

        Reconstruction-Free Compressive Vision for Surveillance Applications

        Editorial: Morgan & Claypool Publishers

        Idioma: Inglés

        ISBN: 9781681735559

        Formatos: PDF (con DRM de Adobe)

        Compatibles con: Windows, Mac, iOS, Android & eReaders

        Medios de pago
          Sinopsis
          Compressed sensing (CS) allows signals and images to be reliably inferred from undersampled measurements.Exploiting CS allows the creation of new types of high-performance sensors including infrared cameras and magnetic resonance imaging systems. Advances in computer vision and deep learning have enabled new applications of automated systems. In this book, we introduce reconstruction-free compressive vision, where image processing and computer vision algorithms are embedded directly in the compressive domain, without the need for first reconstructing the measurements into images or video. Reconstruction of CS images is computationally expensive and adds to system complexity. Therefore, reconstruction-free compressive vision is an appealing alternative particularly for power-aware systems and bandwidth-limited applications that do not have on-board post-processing computational capabilities. Engineers must balance maintaining algorithm performance while minimizing both the number of measurements needed and the computational requirements of the algorithms. Our study explores the intersection of compressed sensing and computer vision, with the focus on applications in surveillance and autonomous navigation. Other applications are also discussed at the end and a comprehensive list of references including survey papers are given for further reading.
          Acerca de Andreas Spanias

          Arizona State University

          Acerca de Cihan Tepedelenlioglu

          Arizona State University

          Acerca de Pavan Turaga

          Arizona State University

          Acerca de Henry Braun

          Arizona State University

          Acerca de Sameeksha Katoch

          Arizona State University

          Acerca de Suren Jayasuriya

          Arizona State University

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