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By Newman, Moskowitz, Chang, Brahmadesam

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1. Linear representations We will refer to representations of data based on symbols (language or knowledge representation) as linear representations. In situations in which such representations are available for the domain of the Team LRN 18 CHAPTER 1 Introduction desired image, they may be processed to obtain information on how to choose the artifacts. As a simple example, an algorithm for NPR might determine the subject of a sentence accompanying the rendition and highlight the corresponding object in the image using certain graphical methods.

NPR will play an important role here because of its flexibility and large repertoire of possible nuances that can be associated with an expression. A criterion for measuring the success of NPR under these circumstances is the uniformity with which a complex message can be conveyed to users: a test might be to show subjects an extensive graphical presentation and ask them to write down in a natural language what they ascertain. Variables pertaining to the similarity of the accounts of several subjects and how well these match the intended meaning represent possible variables of assessment.

The lines are drawn with different slopes, lengths, and densities: slope 30◦, lines approximately 3 pixels long (a), more dense hatching as in (a) by changing the error compensation (b), slope 45◦, lines 5 pixels long (c). 2 This causes the surrounding pixel values to become much brighter and thus not to contribute to the set of black pixels or lines. 10. Even though error diffusion approaches generally reduce the number of artifacts left in the image, there are still a few portions that show some kind 2.

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