ANALISIS VISUAL GAMBAR ANAK PADA MASA PRA-SKEMATIK

<em>This article aims to read visual signs and verbal signs by utilizing the children's drawings during the pre-schematic stage. The reading of the signs will be used to describe the elements of fine arts in the children's drawing. Four works of kindergarten children were chosen and observed. The four works are entitled "Mother", "Brother", "Zoo" and "Tree". These have been selected as the subjects of the study. These four works were chosen because these works were created by using different media, such as pencil on paper, watercolors on paper, and digital media. The object of this study is focused on visual analysis of the children's drawings on the elements of art, such as points, lines, shape, spaces, and colors. The pre-schematic stage is characterized by the appearance of circular images with lines that seem to indicate human or animal figures. During this stage, the scheme (the visual idea) is developed. The drawing shows what the child considers most important about the subject. There is a little understanding of space - objects are placed randomly throughout the image. The use of color is more emotional than logical. The elements of fine art are shown in the drawings can be described as the result of the observation 1) The lines have been controlled so has formed the image. 2) The shape of the object described has been identified as representing the object that the child wants to describe. 3) The colors used to fill the shapes are based on the child's imagination and desires except the color used to fill the shape of the tree. The colors of nature are the same as in real life, such as green leaves, blue sky, and green grass. In general, the image or drawing presented by the child is an illustration</em>

Editorial

This third issue of Airea presents a second round of articles in response to our call for contributions 'Revisiting interdisciplinarity within collaborative and participatory creative practice', announced in June 2019. Following the second issue that showcased contributions from sound-related areas, the present collection focuses on the breadth of practices in art and design. The contributions in this issue surface knowledge about the way interdisciplinary methodologies and approaches influence and shape spaces and bodies within collaborative and participatory works.

Transporting Deformations of Face Emotions in the Shape Spaces: A Comparison of Different Approaches

Studying the changes of shape is a common concern in many scientific fields. We address here two problems: (1) quantifying the deformation between two given shapes and (2) transporting this deformation to morph a third shape. These operations can be done with or without point correspondence, depending on the availability of a surface matching algorithm, and on the type of mathematical procedure adopted. In computer vision, the re-targeting of emotions mapped on faces is a common application. We contrast here four different methods used for transporting the deformation toward a target once it was estimated upon the matching of two shapes. These methods come from very different fields such as computational anatomy, computer vision and biology. We used the large diffeomorphic deformation metric mapping and thin plate spline, in order to estimate deformations in a deformational trajectory of a human face experiencing different emotions. Then we use naive transport (NT), linear shift (LS), direct transport (DT) and fanning scheme (FS) to transport the estimated deformations toward four alien faces constituted by 240 homologous points and identifying a triangulation structure of 416 triangles. We used both local and global criteria for evaluating the performance of the 4 methods, e.g., the maintenance of the original deformation. We found DT, LS and FS very effective in recovering the original deformation while NT fails under several aspects in transporting the shape change. As the best method may differ depending on the application, we recommend carefully testing different methods in order to choose the best one for any specific application.

Automated morphological phenotyping using learned shape descriptors and functional maps: A novel approach to geometric morphometrics

The methods of geometric morphometrics are commonly used to quantify morphology in a broad range of biological sciences. The application of these methods to large datasets is constrained by manual landmark placement limiting the number of landmarks and introducing observer bias. To move the field forward, we need to automate morphological phenotyping in ways that capture comprehensive representations of morphological variation with minimal observer bias. Here, we present Morphological Variation Quantifier (morphVQ), a shape analysis pipeline for quantifying, analyzing, and exploring shape variation in the functional domain. morphVQ uses descriptor learning to estimate the functional correspondence between whole triangular meshes in lieu of landmark configurations. With functional maps between pairs of specimens in a dataset we can analyze and explore shape variation. morphVQ uses Consistent ZoomOut refinement to improve these functional maps and produce a new representation of shape variation, area-based and conformal (angular) latent shape space differences (LSSDs). We compare this new representation of shape variation to shape variables obtained via manual digitization and auto3DGM, an existing approach to automated morphological phenotyping. We find that LSSDs compare favorably to modern 3DGM and auto3DGM while being more computationally efficient. By characterizing whole surfaces, our method incorporates more morphological detail in shape analysis. We can classify known biological groupings, such as Genus affiliation with comparable accuracy. The shape spaces produced by our method are similar to those produced by modern 3DGM and to auto3DGM, and distinctiveness functions derived from LSSDs show us how shape variation differs between groups. morphVQ can capture shape in an automated fashion while avoiding the limitations of manually digitized landmarks, and thus represents a novel and computationally efficient addition to the geometric morphometrics toolkit.

A shape optimization algorithm for cellular composites

We propose and investigate a mesh deformation technique for PDE constrained shape optimization. Introducing a gradient penalization to the inner product for linearized shape spaces, mesh degeneration can be prevented within the optimization iteration allowing for the scalability of employed solvers. We illustrate the approach by a shape optimization for cellular composites with respect to linear elastic energy under tension. The influence of the gradient penalization is evaluated and the parallel scalability of the approach demonstrated employing a geometric multigrid solver on hierarchically distributed meshes.

Consistent curvature approximation on Riemannian shape spaces

We describe how to approximate the Riemann curvature tensor as well as sectional curvatures on possibly infinite-dimensional shape spaces that can be thought of as Riemannian manifolds. To this end we extend the variational time discretization of geodesic calculus presented in Rumpf & Wirth (2015, Variational time discretization of geodesic calculus. IMA J. Numer. Anal., 35, 1011–1046), which just requires an approximation of the squared Riemannian distance that is typically easy to compute. First we obtain first-order discrete covariant derivatives via Schild’s ladder-type discretization of parallel transport. Second-order discrete covariant derivatives are then computed as nested first-order discrete covariant derivatives. These finally give rise to an approximation of the curvature tensor. First- and second-order consistency are proven for the approximations of the covariant derivative and the curvature tensor. The findings are experimentally validated on two-dimensional surfaces embedded in ${\mathbb{R}}^3$. Furthermore, as a proof of concept, the method is applied to a space of parametrized curves as well as to a space of shell surfaces, and discrete sectional curvature confusion matrices are computed on low-dimensional vector bundles.