Synesthetic art through 3-D projection: The requirements of a computer-based supermedium

A computer-based form of multimedia art is proposed that uses the computer to fuse aspects of painting, sculpture, dance, music, film, and other media into a one-to-one synthesia of image and sound for spatially synchronous 3-D projection. Called synesthetic art, this conversion of many varied media into an aesthetically unitary experience determines the character and requirements of the system and its software. During the start-up phase, computer stereographic systems are unsuitable for software development. Eventually, a new type of illusory-projective supermedium will be required to achieve the needed combination of large-format projection and convincing real life presence, and to handle the vast amount of 3-D visual and acoustic information required. The influence of the concept on the author's research and creative work is illustrated through two examples

The Image – by any means

Art is inherently a reflection of the human condition. As I am working, my intention is to make images that, at the very least, capture the audience\u27s attention. By adding layers and fracturing the images, I want the audience to see through various visual relationships. These visual relationships can be interpreted as a metaphor of the often hectic and unstable qualities of daily life. As the human figure is my connection to the work, it also serves as the viewer\u27s connection. The blending of media and mixing up of figurative representation with abstraction is my attempt to challenge the viewer to search for their own humanity among the clamor of everyday existence. I want to bring a sense of balance and harmony to the disorder of being. This body of work is an attempt to blend abstract expressionistic painting, collage, and large-format, digital, inkjet printing. In the past I have always kept my life as a graphic designer separate from my life as an artist. I now feel comfortable with the idea of integrating these aspects of myself to create energetic, stimulating, and thought-provoking images. The finished pieces range in size from 36 x24 to 63 x45 . Some elements in the imagery are painterly in contrast to the mechanical and digital processes that I employ. Figurative elements are combined with abstract forms. Through training, education, and practice I have come to be a process-oriented artist. I don\u27t intend to make art that overtly makes any kind of personal, social or political statement. The two things most important to me are the act of making the image and the image itself. This does not mean that content does not play a role in my process. As I work, I let the content emerge intuitively. I use the figure as an element of form. I use bright, contrasting and complementary colors to stimulate the image. I integrate pattern and design to create depth and motion

Pictures in Your Mind: Using Interactive Gesture-Controlled Reliefs to Explore Art

Tactile reliefs offer many benefits over the more classic raised line drawings or tactile diagrams, as depth, 3D shape, and surface textures are directly perceivable. Although often created for blind and visually impaired (BVI) people, a wider range of people may benefit from such multimodal material. However, some reliefs are still difficult to understand without proper guidance or accompanying verbal descriptions, hindering autonomous exploration. In this work, we present a gesture-controlled interactive audio guide (IAG) based on recent low-cost depth cameras that can be operated directly with the hands on relief surfaces during tactile exploration. The interactively explorable, location-dependent verbal and captioned descriptions promise rapid tactile accessibility to 2.5D spatial information in a home or education setting, to online resources, or as a kiosk installation at public places. We present a working prototype, discuss design decisions, and present the results of two evaluation studies: the first with 13 BVI test users and the second follow-up study with 14 test users across a wide range of people with differences and difficulties associated with perception, memory, cognition, and communication. The participant-led research method of this latter study prompted new, significant and innovative developments

Statistical image properties predict aesthetic ratings in abstract paintings created by neural style transfer

Artificial intelligence has emerged as a powerful computational tool to create artworks. One application is Neural Style Transfer, which allows to transfer the style of one image, such as a painting, onto the content of another image, such as a photograph. In the present study, we ask how Neural Style Transfer affects objective image properties and how beholders perceive the novel (style-transferred) stimuli. In order to focus on the subjective perception of artistic style, we minimized the confounding effect of cognitive processing by eliminating all representational content from the input images. To this aim, we transferred the styles of 25 diverse abstract paintings onto 150 colored random-phase patterns with six different Fourier spectral slopes. This procedure resulted in 150 style-transferred stimuli. We then computed eight statistical image properties (complexity, self-similarity, edge-orientation entropy, variances of neural network features, and color statistics) for each image. In a rating study, we asked participants to evaluate the images along three aesthetic dimensions (Pleasing, Harmonious, and Interesting). Results demonstrate that not only objective image properties, but also subjective aesthetic preferences transferred from the original artworks onto the style-transferred images. The image properties of the style-transferred images explain 50 – 69% of the variance in the ratings. In the multidimensional space of statistical image properties, participants considered style-transferred images to be more Pleasing and Interesting if they were closer to a “sweet spot” where traditional Western paintings (JenAesthetics dataset) are represented. We conclude that NST is a useful tool to create novel artistic stimuli that preserve the image properties of the input style images. In the novel stimuli, we found a strong relationship between statistical image properties and subjective ratings, suggesting a prominent role of perceptual processing in the aesthetic evaluation of abstract images

Stroke Based Painterly Rendering

International audienceMany traditional art forms are produced by an artist sequentially placing a set of marks, such as brush strokes, on a canvas. Stroke based Rendering (SBR) is inspired by this process, and underpins many early and contemporary Artistic Stylization algorithms. This Chapter outlines the origins of SBR, and describes key algorithms for placement of brush strokes to create painterly renderings from source images. The chapter explores both local greedy, and global optimization based approaches to stroke placement. The issue of creative control in SBR is also briefly discussed

Perceptual 3D rendering based on principles of analytical cubism

Cataloged from PDF version of article.Cubism, pioneered by Pablo Picasso and Georges Braque, was a breakthrough in art, influencing artists to abandon existing traditions. In this paper, we present a novel approach for cubist rendering of 3D synthetic environments. Rather than merely imitating cubist paintings, we apply the main principles of analytical cubism to 3D graphics rendering. In this respect, we develop a new cubist camera providing an extended view, and a perceptually based spatial imprecision technique that keeps the important regions of the scene within a certain area of the output. Additionally, several methods to provide a painterly style are applied. We demonstrate the effectiveness of our extending view method by comparing the visible face counts in the images rendered by the cubist camera model and the traditional perspective camera. Besides, we give an overall discussion of final results and apply user tests in which users compare our results very well with analytical cubist paintings but not synthetic cubist paintings. (c) 2012 Elsevier Ltd. All rights reserved

COMPUTATIONAL MODELLING OF HUMAN AESTHETIC PREFERENCES IN THE VISUAL DOMAIN: A BRAIN-INSPIRED APPROACH

Following the rise of neuroaesthetics as a research domain, computational aesthetics has also known a regain in popularity over the past decade with many works using novel computer vision and machine learning techniques to evaluate the aesthetic value of visual information. This thesis presents a new approach where low-level features inspired from the human visual system are extracted from images to train a machine learning-based system to classify visual information depending on its aesthetics, regardless of the type of visual media. Extensive tests are developed to highlight strengths and weaknesses of such low-level features while establishing good practices in the domain of study of computational aesthetics. The aesthetic classification system is not only tested on the most widely used dataset of photographs, called AVA, on which it is trained initially, but also on other photographic datasets to evaluate the robustness of the learnt aesthetic preferences over other rating communities. The system is then assessed in terms of aesthetic classification on other types of visual media to investigate whether the learnt aesthetic preferences represent photography rules or more general aesthetic rules. The skill transfer from aesthetic classification of photos to videos demonstrates a satisfying correct classification rate of videos without any prior training on the test set created by Tzelepis et al. Moreover, the initial photograph classifier can also be used on feature films to investigate the classifier’s learnt visual preferences, due to films providing a large number of frames easily labellable. The study on aesthetic classification of videos concludes with a case study on the work by an online content creator. The classifier recognised a significantly greater percentage of aesthetically high frames in videos filmed in studios than on-the-go. The results obtained across datasets containing videos of diverse natures manifest the extent of the system’s aesthetic knowledge. To conclude, the evolution of low-level visual features is studied in popular culture such as in paintings and brand logos. The work attempts to link aesthetic preferences during contemplation tasks such as aesthetic rating of photographs with preferred low-level visual features in art creation. It questions whether favoured visual features usage varies over the life of a painter, implicitly showing a relationship with artistic expertise. Findings display significant changes in use of universally preferred features over influential vi abstract painters’ careers such an increase in cardinal lines and the colour blue; changes that were not observed in landscape painters. Regarding brand logos, only a few features evolved in a significant manner, most of them being colour-related features. Despite the incredible amount of data available online, phenomena developing over an entire life are still complicated to study. These computational experiments show that simple approaches focusing on the fundamentals instead of high-level measures allow to analyse artists’ visual preferences, as well as extract a community’s visual preferences from photos or videos while limiting impact from cultural and personal experiences

The Computer as Filter Machine: A Clustering Approach to Categorize Artworks Based on a Social Tagging Network

Image catalogs containing several million reproductions of artworks still pose a costly or computationally intensive challenge if one tries to categorize them adequately, either in a manual or automatic way. Using crowdsourced annotations assigned by laypersons, this article proposes the application of a clustering algorithm to segment artworks into groups. It is shown that the resulting clusters allow for a consistent reclassification extending the traditional categories (history, genre, portrait, still life, landscape), and thus enable a finely-grained differentiation which can be used to search in and filter image inventories, among other things