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The video content analyzer includes programs for automatic extraction of visual features from MPEG videos in the compressed domain.
This paper presents an architecture for the extraction of visual primitives on chip: energy, orientation, disparity, and optical flow.
For the extraction of visual features, our visual saliency model uses biologically plausible spatio-temporal 3D Gabor filters.
However, most existing image/videos based methods for capturing, retrieving, and modeling 3D as-built building information heavily relied on the extraction of visual features from images or video frames.
If, as suggested by Vachon et al. (2012), the extraction of visual information can be momentarily degraded by the high cognitive demands of the situation, it is likely that the extraction of information logged in CHEX suffers the same complications.
These data indicate that interference associated with visual HUD-based distraction is broad, affecting not only selective attention mechanisms but also processes associated with the perceptual extraction of visual features.
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Visual perception of art broadly consists of three stages [25]: (i) extraction of basic visual features, (ii) organization of these features into coherent and fundamental forms, followed by (iii) addition of meaning onto these forms through associations stored in long-term memory.
In the GP these methods have mostly included visual inspection of the recorded data followed by extraction of visually salient parameters [9].
For the extraction of basic visual cues, independent and complementary modules are envisaged.
In this respect, our method involves extraction of several visual features from the facial regions, as well as incorporation of scene characteristics and deep features to the recognition.
However, extraction of such visual speech information from full-face videos is computationally expensive, as it requires tracking faces and facial features.
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