This study focuses on the properties of reflection and absorption of light in the visible and near infrared range of acrylic dyes and related materials for coloring. The research aims to understand the use of acrylic dyes in fine arts and to simulate these dyes for printing purposes. Two groups of acrylic dyes were studied using spectroscopy to determine their light absorption properties in the visible (VIS) and near infrared (NIR) regions. The measurements were performed with forensic cameras equipped with 24 light filters in the range of 400–1000 nm. The results of the digital recordings were compared with video surveillance cameras that operate and record during the day and night. The same color had at least two formulations of dyes for print reproduction to simulate what the naked eye sees and to simulate light absorption in the NIR spectrum. The study aimed to gain a deeper understanding of the properties of acrylic paints and related materials, considering how they can be used in fine arts and how to simulate them in printing.

Visual perception of screen elements depends on their color, font, and position in the user interface design. Objects in the central part of the screen are perceived more easily than those in the peripheral areas. However, the peripheral space is valuable for applications like advertising and promotion and should not be overlooked. Optimizing the design of elements in this area can improve user attention to peripheral visual stimuli during focused tasks. This study aims to evaluate how different combinations of text and background color affect the visibility of moving textual stimuli in the peripheral areas of the screen, while attention is focused on a central task. This study investigates how background color, combined with white or black text, affects the attention of participants. It also identifies which background color makes a specific word most noticeable in the peripheral part of the screen. We designed quizzes to present stimuli with black or white text on various background colors in the peripheral regions of the screen. The background colors tested were blue, red, yellow, green, white, and black. While saturation and brightness were kept constant, the color tone was varied. Among ten combinations of background and text color, we aimed to determine the most noticeable combination in the peripheral part of the screen. The combination of white text on a blue background resulted in the shortest detection time (1.376 s), while black text on a white background achieved the highest accuracy rate at 79%. The results offer valuable insights for improving peripheral text visibility in user interfaces across various visual communication domains such as video games, television content, and websites, where peripheral information must remain noticeable despite centrally focused user attention and complex viewing conditions.

Understanding abstract concepts of aesthetics and semantics is crucial in developing reliable and verifiable systems for information retrieval or content generation. In this study we explore the space of use-cases where neural networks can aid in deepening the understanding of aesthetic and semantic theoretical principles, their relations, their practical relevance, and possibilities for automation of theory-aligned, and thus explainable, semantic or aesthetic evaluation. We start with pretrained object-recognition vision models, refitted and fine-tuned to discriminate between classes of fine art paintings along two core-semantic axes: abstract-concrete and iconic-symbolic. We then refit the models one more time using the CBM method to make models more reliable and their outputs interpretable in the frame of design- and art-theoretic principles. Finally, we apply a curated collection of xAI methods to analyze, visualize, and contrast the decision-making criteria of four developed models. Our findings show that - for the task of mapping sets of highly distributed, complex and intertwined features - the introduction of narrow concept bottlenecks at the output end of the black-box networks affords for interpretation of models’ outputs, while leaving no negative impact on their classification performance. The methodology developed for this paper presents a transferable framework for amplifying black-box vision-models’ usability and verifiability of their outputs, and it is especially applicable in specialized fields with well-formed theoretical understanding of their respective imaging problem-space. All code developed for constructing, refitting and analyzing models, alongside the datasets created for this study, have been made publicly available under the GPLv3 license.