The mechanisms by which the brain represents colors are largely unknown. In addition, the large number of color phenomena in the natural world has made understanding color rather difficult. Color transparency perception, which is studied in this thesis, is precisely one of these interesting phenomena: when a surface is seen both in plain view and through a transparent overlay, the visual system still identifies it as a single surface. Processes of the visual system have widely inspired researchers in many domains such as neurosciences, psychology, as well as computer vision. The progress of digital imaging technologies requires research engineers to deal with issues that demand knowledge of human visual processing. To humans, an image is not a random collection of pixels, but a meaningful arrangement of regions and objects. One thus can be inspired by the human visual system to investigate color representation and its applicability to digital image processing. Finding a model of perception is still a challenging matter for researchers among multidisciplinary fields. This thesis discusses the problem of defining an accurate model of transparency perception. Despite the large number of studies on this topic, the underlying mechanisms are still not well understood. Investigating perceptual transparency is challenging due to its interactions with different visual phenomena, but the most intensively studied conditions for perceptual transparency are those involving achromatic luminance and chromatic constraints. Although these models differ in many aspects, a broad distinction can be drawn between models of additive and subtractive transparency. The General Convergence Model (GCM) combines both additive and subtractive color mixtures in showing that systematic chromatic changes in a linear color space, such as translation and convergence (or a combination of both), lead to perceptual transparency. However, while this model seems to be a necessary condition, it is not a sufficient one for transparency perception. A first motivation of this thesis was to evaluate and define situations more general than the GCM. Several chromatic changes consistent or not with the GCM were generated. Additional parameters, such as configural complexity, luminance level, magnitude of the chromatic change and shift direction were tested. The main results showed that observers' responses are influenced by each of the above cited parameters. Convergences appear significantly more transparent when motion is added for bipartite configurations, or when they are generated in a checkerboard configuration. Translations are influenced by both configuration and motion. Shears are described as opaque, except when short vector lengths are combined with motion: the overlay tends to be transparent. Divergences are strongly affected by motion and vector lengths, and rotations by a combination of checkerboard configuration with luminance level and vector length. These results question the generality of the GCM. We also investigated the effects of shadows on the perception of a transparent filter. An attempt to extend these models to handle transparency perception in complex scenes involving surfaces varying in shape and depth, change in conditions of illumination and shadow, is described. A lightness-matching task was performed to evaluate how much constancy is shown by the subject among six experimental conditions, in which shadow position, shadow blur, shadow and filter blending values were varied. The results showed that lightness constancy is very high even if surfaces were seen under both filter and shadow. A systematic deviation from perfect constancy in a manner consistent with a perceived additive shift was also observed. Because the GCM includes additive mixture and is related to color and lightness constancy, these results are promising and may be explained ultimately by this model.