, and geometric and photometric variances of objects. For example, in a live video of a skating competition, the skater moves rapidly across the rink, and the camera also moves to follow the skater. With moving camera, non-stationary background, and moving target, few vision algorithms could identify, categorize and

. The model and subjects showed good agreement in the objects judged to be most and least similar to the target categories. Our data also suggest that color, texture, and shape features are all useful for classification, and that the specific weighting of these features depends on the target object class

An implicit association test (IAT) measures differential association of 2 target concepts with an attribute. The 2 concepts appear in a 2-choice task (e.g., flower vs. insect names), and the attribute in a 2nd task (e.g., pleasant vs. unpleasant words for an evaluation attribute). When instructions

into useful categories of relevance. We present definitions for irrelevance and for two degrees of relevance. These definitions improve our understanding of the behavior of previous subset selection algorithms, and help define the subset of features that should be sought. The features selected should depend

Past research on brand extension evaluation does not incorporate the effects of the target category structure and competition from the existing brand. This paper reports the findings of an exploratory experimental study that shows the effects of competition on the evaluation of brand extensions

The file name of this memo is draft-alvestrand-content-language-

;best" image possible within the target frame time. Tests show that the algorithm generates more uniform frame rates than other previously described detail elision algorithms with little noticeable difference in image quality during visualization of complex models. CR Categories and Subject Descriptors

variables that comprise the IAT, not least the IAT categories and the exemplars that are instances of those categories. This study investigated the effects of manipulating the IAT's target categories that define the attitudinal domain that the IAT measures. Experiments were devised to determine the IAT

This paper describes an end-to-end method for extracting moving targets from a real-time video stream, classifying them into predefined categories according to imagebased properties, and then robustly tracking them. Moving targets are detected using the pixel wise difference between consecutive

for functional miRNA–target duplexes in vivo and distinguish classes of target sites with different functional properties. Target sites can be grouped into two broad categories. 59 dominant sites have sufficient complementarity to the miRNA 59 end to function with little or no support from pairing to the mi