The stated goals of fusion traditionally surround some improvement of human perception and/or computer processing, aiming to produce stimuli that are more informative and more suitable to visual perception, maximize relevant information particular to a task, increase perceptibility, and provide other such advancing effects (e.g., McCarley & Krebs, 2006; Toet et al., 2010).
In musically related task, this logarithmic form of decomposition has been proved to be more suitable for human perception and pitch-based features are more discriminative.
It includes data collection, resolving similarities found in different sources, modification data to a type acceptable for the analysis, the analysis itself and output representation, i.e. the results visualization in a form most suitable for human perception.
Human tactile perception is robust and suitable for multimodal sensing [ 59].
There is now evidence towards the validity and reliability of the IPQ-R as suitable measure of illness perceptions in the context of ESRD [ 35, 36].
From our perspective, one of the more promising methods for improving upon current Big Data visualization techniques is in its correlation with Augmented Reality (AR) and Virtual Reality (VR) that are suitable for the limited perception capabilities of humans.
The main goal of image fusion is to extract all the important features from all input images and integrate them to form a fused image which is more informative and suitable for human visual perception or computer processing.
In previous studies, perception of suitable costs and satisfaction of MMT are predictors of retention [ 12, 25].
A recent study on 2,728 MMT patients in China [ 25] indicated that perception of suitable MMT cost is a significant predictor of retention.
Furthermore, the ratio of costs over per unit increment of quality of life may provide a useful index to further evaluate patient's perception of suitable costs and treatment satisfaction and form a basis of policy decision making.
