Different from existing graphic saliency detection methods, which estimate saliency based on pixel-level contrast, the proposed method detects salient objects by computing object-level contrast.
THERE is, to be sure, an element of art to designing the look and feel of computing objects that intuitively please users.
This distributed computing environment is a middleware whose dynamic properties replicate the behaviour of large data flows, i.e. computing objects migrating between the different computing nodes of a local area network.
While computing object-based spatial frequencies is relatively straightforward with stationary objects, such as static images of human faces, one challenge in estimating object-based spatial frequencies in the current study is due to variability in body size and body postures over time in the action videos.
To be able to accommodate meso-scale objects, we need to extend the computational limits of the DDA method such that it could compute object sizes of 10λ 30λ characteristic dimension (i.e. volumes of 103 104 cubic wavelengths).
The primate brain contains a hierarchy of visual areas, dubbed the ventral stream, which rapidly computes object representations that are both specific for object identity and relatively robust against identity-preserving transformations like depth-rotations [ 33, 32, 23, 13 ].
The primate brain contains a hierarchy of visual areas, dubbed the ventral stream, which rapidly computes object representations that are both specific for object identity and robust against identity-preserving transformations, like depth rotations [1, 2].
In contrast to AIP, left LO in the ventral stream appears to compute object properties when the task is purely perceptual, showing different levels of activation for size versus pattern discrimination only.
The first algorithm aimed to find correspondences and the second aimed to compute object pose by assuming a full-perspective camera model.
The study then computed object-level spectral, texture, and three-dimensional information for image object content representation using NDVI-based spectral, wavelet transform-based texture, variogram -based texture, and canopy surface height information.
Improvements in communication and processing technologies have opened the doors to exploit on-board cameras to compute objects' spatial attitude using only the visual information from sequences of remote sensed images.
