Thus, when potential contributions of associations driven by factors such as onomatopoeic content or mediated associations between the nonword and the definition are removed, the congruency between sound symbolic properties of nonwords and object descriptions seems to be limited to enhanced memory for the size of the object only, and not for other characteristics of the object.
Rather, this modification results from a pragmatic system that codes for the graspable characteristics of the object.
Two novel algorithms for estimating the motion characteristics of the object in dynamic scene by processing a limited sequence of images are presented in this paper.
When objects are in motion in the real world, then variations in observer-object distance (as produced by self-motion towards the object) change the two-dimensional spatial characteristics of the object.
One of the main difficulties for the visual sensor-based object tracking is that distinguishable characteristics of the objects are nontrivial to be constructed for all the detected targets due to the objects' similarity in color, size, and shape.
These authors used a detection task to determine the interference of changing an irrelevant characteristic of an object, for example, its spatial location, on the recognition of a relevant characteristic, for example, the object's shape or color.
Nodes can be treated as characteristic points of the object for modeling.
Therefore, this study proposes an evolutionary approach that applies the characteristics of software reuse, the metrics for the object-oriented concept, and the genetic algorithm to effectively manage and optimize the embedded system.
