Semi-automatic modifications of video object trajectories for video compositing applications

Nicolas, Henri; Denoual, Franck

doi:10.1016/j.sigpro.2005.02.019

Cited by 2 publications

(2 citation statements)

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“…In some cases, these points can be automatically detected using a Harris corner detector ([ 13 , 14 ] or through edge alignment and correspondence-based approaches [ 15 , 16 ]. An estimation method based on a direct comparison between the current original image and the previously calculated mosaic was also proposed [ 17 , 18 ]. The analytical minimization criterion was designed to optimize the determination of the blending coefficient.…”

Section: The Image Mosaicing Algorithmmentioning

confidence: 99%

Mosaicing of Hyperspectral Images: The Application of a Spectrograph Imaging Device

Moroni

Dacquino

Cenedese

2012

Sensors

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Hyperspectral monitoring of large areas (more than 10 km2) can be achieved via the use of a system employing spectrometers and CMOS cameras. A robust and efficient algorithm for automatically combining multiple, overlapping images of a scene to form a single composition (i.e., for the estimation of the point-to-point mapping between views), which uses only the information contained within the images themselves is described here. The algorithm, together with the 2D fast Fourier transform, provides an estimate of the displacement between pairs of images by accounting for rotations and changes of scale. The resulting mosaic was successively georeferenced within the WGS-84 geographic coordinate system. This paper also addresses how this information can be transferred to a push broom type spectral imaging device to build the hyperspectral cube of the area prior to land classification. The performances of the algorithm were evaluated using sample images and image sequences acquired during a proximal sensing field campaign conducted in San Teodoro (Olbia-Tempio—Sardinia). The hyperspectral cube closely corresponds to the mosaic. Mapping allows for the identification of objects within the image and agrees well with ground-truth measurements.

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Section: The Image Mosaicing Algorithmmentioning

confidence: 99%

Mosaicing of Hyperspectral Images: The Application of a Spectrograph Imaging Device

Moroni

Dacquino

Cenedese

2012

Sensors

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show abstract

“…Although the industry has made use of various technologies such as blue screen photography, rotoscoping, polyfill (polygon filling) method, etc., very little effort has gone into automating the entire process of compositing except those cited earlier. Some recent trends include separation of object layers [28] or video object planes from different input videos for matte preparation [18]. However such methods would require explicit computation of structure from motion and the accuracy of the matte depends on the accuracy with which the layers can be separated.…”

Section: Introductionmentioning

confidence: 99%