Color Research and Application最新文献

During the silent film era, various light sources for film projection were available on the market. The choice of illuminant was determined on the one hand by the screening context and its infrastructure (e.g., electrification of the venue, etc.), and on the other hand by its optical and esthetical properties—luminosity, luminance, color hue, and so on—which could be more or less ideal for film projection. These properties could be inherent to the light source but could also be related to factors associated with the projection environment (e.g., diligence of the projectionist, distance to the screen, etc.). The present article highlights the esthetic properties—and problems—of different light sources and their usage context in Germany between 1909 and 1929, a period in which light technology was constantly subject to a desire for development and improvement, and thus to change. By means of the technological discourse that accompanied these developments in projection technology—for example, specialist literature, publications and advertisements by the manufacturers, as well as trade journals—, the carbon arc lamp, gas lights, and incandescent light sources are analyzed regarding the characteristics attributed to them within the discourse. In doing so, we aim to identify objectives of quality that were or were not explicitly or implicitly regarded as critical by professionals, and the extent to which color played a role in these considerations. The findings show that there was a wide variety of factors determining the visual properties of any film projection, and that their interactions need to be taken into account when restoring films and recreating historical projection formats, thus relativizing restoration policies such as wanting “the film to look as it did when it was first shown in cinemas […]” (While this particular quote was taken from Jørgen Christiansen, project manager at the Danish Film Institute, in a documentary about their archive, his words are representative of a recurring discourse that can be found among many film archives/film restorers, for example on the occasion of the Restauration Talks at the annual festival Il Cinema Ritrovato in Bologna [IT]).

Social robots are designed to communicate and interact with humans in a socio-emotional capacity, including the expression of emotions. Among the various ways that can be employed by robots to convey emotion information, the current study investigates color as a useful tool for emotion expression in social robots. Color is a flexible, inexpensive, and practical channel to convey emotional meaning in social robots, likely due to a robust network of color-emotion associations existing in human cognition. The current study tests how perceptions of the emotions happiness, sadness, and anger are influenced by social robots displaying a range of colored lights. The results show that color can meaningfully influence the perceptions of these robot emotions. Further, these results show that independent color dimensions (i.e., lightness, chroma, hue) impact emotion evaluation to differing extents, depending on the particular emotion. Additionally, we tested the influence of color on emotion perception for robots that varied in their anthropomorphic appearance, finding some nuanced differences in how their appearance modified their emotion expressions. We discuss the implications with respect to designing emotion-communicative social robots, dimensional approaches to understanding color-emotion associations more broadly, and the role of context in color meaning. The current work aims to facilitate the design of social robots and promote more meaningful human-robot social interactions.

Simultaneous contrast is the phenomenon in which the background influences perceived color. Existing color appearance models (CAMs) are inadequate in fully capturing this effect. This research aims to provide a deeper understanding of the simultaneous contrast effect and to extend the state-of-the-art color appearance model, CIECAM16, to better account for this phenomenon. By conducting three experiments, we empirically assessed the effects of simultaneous contrast on chroma, hue, and lightness. The findings revealed that a stimulus's perceived color attributes changed significantly depending on the color properties of the background. Based on these results, we extended CIECAM16 to account for the simultaneous contrast effect. The results demonstrated that the extended CIECAM16 model significantly improved the ability to predict color appearance while accounting for simultaneous contrast and offered closer alignment with observed findings and visual mechanisms. This research enhances the understanding of simultaneous contrast and contributes to improving the CIECAM16 model.

In the present era, hyperspectral images have become a pervasive tool in a multitude of fields. In order to provide a feasible alternative for scenarios where hyperspectral imaging equipment is not accessible, numerous researchers have endeavored to reconstruct hyperspectral information from limited spectral measurements, leading to the development of spectral reconstruction (SR) algorithms that primarily focus on the visible spectrum. In light of the remarkable advancements achieved in many computer vision tasks through the application of deep learning, an increasing number of SR works aim to leverage deeper and wider convolutional neural networks (CNNs) to learn intricate mappings of SR. However, the majority of deep learning methods tend to neglect the design of initial up-sampling when constructing networks. While some methods introduce innovative attention mechanisms, their transferability is limited, impeding further improvement in SR accuracy. To address these issues, we propose a multi-attention interaction and multi-scale fusion network (MAMSN) for SR. It employs a shunt-confluence multi-branch architecture to learn multi-scale information in images. Furthermore, we have devised a separable enhanced up-sampling (SEU) module, situated at the network head, which processes spatial and channel information separately to produce more refined initial up-sampling results. To fully extract features at different scales for visible-spectrum spectral reconstruction, we introduce an adaptive enhanced channel attention (AECA) mechanism and a joint complementary multi-head self-attention (JCMS) mechanism, which are combined into a more powerful feature extraction module, the dual residual double attention block (DRDAB), through a dual residual structure. The experimental results show that the proposed MAMSN network outperforms other SR methods in overall performance, particularly in quantitative metrics and perceptual quality.