Fast Accurate and Automatic Brushstroke Extraction

Brushstrokes are viewed as the artist’s “handwriting” in a painting. In many applications such as style learning and transfer, mimicking painting, and painting authentication, it is highly desired to quantitatively and accurately identify brushstroke characteristics from old masters’ pieces using computer programs. However, due to the nature of hundreds or thousands of intermingling brushstrokes in the painting, it still remains challenging. This article proposes an efficient algorithm for brush Stroke extraction based on a Deep neural network, i.e., DStroke. Compared to the state-of-the-art research, the main merit of the proposed DStroke is to automatically and rapidly extract brushstrokes from a painting without manual annotation, while accurately approximating the real brushstrokes with high reliability. Herein, recovering the faithful soft transitions between brushstrokes is often ignored by the other methods. In fact, the details of brushstrokes in a master piece of painting (e.g., shapes, colors, texture, overlaps) are highly desired by artists since they hold promise to enhance and extend the artists’ powers, just like microscopes extend biologists’ powers. To demonstrate the high efficiency of the proposed DStroke, we perform it on a set of real scans of paintings and a set of synthetic paintings, respectively. Experiments show that the proposed DStroke is noticeably faster and more accurate at identifying and extracting brushstrokes, outperforming the other methods

PaletteNeRF: Palette-based Color Editing for NeRFs

Neural Radiance Field (NeRF) is a powerful tool to faithfully generate novel views for scenes with only sparse captured images. Despite its strong capability for representing 3D scenes and their appearance, its editing ability is very limited. In this paper, we propose a simple but effective extension of vanilla NeRF, named PaletteNeRF, to enable efficient color editing on NeRF-represented scenes. Motivated by recent palette-based image decomposition works, we approximate each pixel color as a sum of palette colors modulated by additive weights. Instead of predicting pixel colors as in vanilla NeRFs, our method predicts additive weights. The underlying NeRF backbone could also be replaced with more recent NeRF models such as KiloNeRF to achieve real-time editing. Experimental results demonstrate that our method achieves efficient, view-consistent, and artifact-free color editing on a wide range of NeRF-represented scenes.Comment: 12 pages, 10 figure

Method and madness at the Isabella Stewart Gardner museum

The Isabella Stewart Gardner museum in Boston, Massachusetts is unique in history and design. Originating as a privately held collection, the Gardner Museum reflects its namesake’s eccentricities and stands in stark contrast to the backdrop of contemporary Boston. Although much has been written about the individual masterpieces held within the Gardner collection and there are numerous biographies of “Mrs. Jack,” as Gardner was sometimes called, little work has been done to investigate the museum in light of contemporary research in museology and the practices of collecting and display. Understanding collecting and curating as modes of knowledge production, this study seeks to discover the types of knowledge produced by and within the Gardner Museum. Because the museum highlights forms of knowledge other than that associated with textual criticism, I focus on the affective and historical material transfers at work in museum practice. As such, this study offers an opportunity to explore the nature of a performance-based method or orientation to scholarship. I both make use of and question “performative writing” as a mode of presentation, so that what emerges is an understanding of a method that, like the Gardner Museum, seeks to discover ways of knowing beyond (but not in lieu of) processes of representation and signification. In a sense then, performance methodology becomes both an object of study and my method. In bringing the Isabella Stewart Gardner Museum into relationship with the disciplinary problem of performative writing, I have conceived of my research and writing practices as processes of collecting and curating

The use of nonwoven support materials for the conservation of three-dimensional painted silk

This article presents research on the effectiveness of nonwoven support treatment for stabilizing splits on silk, with a specific focus on its possible use on three-dimensional painted silk. A literature review established that Tengujō, a Japanese paper made from the kōzo plant, was the most commonly used nonwoven support material in paper and textile conservation. Available paper conservation literature has also indicated that cellulose nanofibers (CNF) are a promising cellulose-based nonwoven support. The effectiveness of Tengujō and CNF supports, coated with adhesive and applied to silk using a nonaqueous method, was evaluated for strength, removability, and flexibility. The strength of the samples was determined through tensile and comparative shear tests. Results were interpreted using stress-strain graphs and visual analysis of the tested samples. Removability of the material was assessed by characterizing the adhesive residues left on the supported silk substrate using optical microscopy. Sensory evaluation was used to establish the flexibility of the material by assessing the physical attributes of supported samples. The results showed that while Tengujō and CNF are both effective supports, the characteristics of each make them suitable for different applications