Figure 1: The proposed method can rate a given color palette with any number of colors relative to human aesthetic preferences. The proposed method suggests a compatible color for the given palette, which allows us to expand the palette while retaining color harmony to support user exploration of color design.
AbstractA model to rate color combinations that considers human aesthetic preferences is proposed. The proposed method does not assume that a color palette has a specific number of colors, i.e., input is not restricted to a two-, three-, or five-color palettes. We extract features from a color palette whose size does not depend on the number of colors in the palette. The proposed rating prediction model is trained using a human color preference dataset. The model allows a user to extend a color palette, e.g., from three colors to five or seven colors, while retaining color harmony. In addition, we present a color search scheme for a given palette and a customized version of the proposed model for a specific color tone. We demonstrate that the proposed model can also be applied to various palette-based applications.
Visual cryptography (VC) is an encryption technique for hiding a secret image in distributed and shared images (referred to as shares). VC schemes are employed to encrypt multiple images as meaningless, noisy patterns or meaningful images. However, decrypting multiple secret images using a unique share is difficult with traditional VC. We propose an approach to hide multiple images in meaningful shares. We can decrypt multiple images simultaneously using a common share, which we refer to as a magic sheet.The magic sheet decrypts multiple secret images depending on a given share. The shares are printed on transparencies, and decryption is performed by physically superimposing the transparencies. We evaluate the proposed method using binary, grayscale, and color images.
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