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Temperament and perception of tooth bleaching results

100%

Mehr K. , Maciejewska-Szaniec Z. , Stanglewicz T. , Maciejewska B. , Pilarska A. , Lira J. , Piotrowski P.

nr 3

294-297

Subfocal Color Categorization and Naming: The Role of Exposure to Language and Professional Experience

88%

Malinowska M. , Haman M.

nr 4

15-20

The current state of the debate on the linguistic factors in color perception and categorization is reviewed. Developmental and learning studies were hitherto almost ignored in this debate. A simple experiment is reported in which 20 Academy of Fine Arts, Faculty of Painting students’ performance in color discrimination and naming tasks was compared to the performance of 20 Technical University students. Subfocal colors (different hues of red and blue) were used. While there was no difference in overall discrimination ability, AFA students had a much richer and specialized color vocabulary. Both groups also applied different strategies of discrimination and naming. However, naming system in neither group was coherent. This suggests that naming played primarily the role of markers for control processes rather than names for categories. It is concluded that up-to-date debate is too simplified and a complex model of interrelations between perceptual categorization and naming framed in the developmental context is needed rather than the search for a simple answer “language”, “environment”, or “perceptual universals”.

Depth map color constancy

88%

Ebner M. , Hansen J.

tom Vol. 9, no. 4

167--177

A human observer is able to determine the color of objects independent of the light illuminating these objects. This ability is known as color constancy. In the first stages of visual information processing, data are analyzed with respect to wavelength composition, orientation, motion, and depth. With this contribution, we investigate whether depth information can help in estimating the color of the objects. We assume that local space average color is computed in V4 through resistively coupled neurons to estimate the color of the illuminant. We show how this computational model can be extended to incorporate depth information.