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Kinematic analysis is the first task that needs to be solved for further real-life problems such as machine dynamics and durability, machine balancing, and optimization, chemistry, etc. Currently, there are many methods to analyze the kinematic problem, for instance: vector graphics method, vector-analytic method, or Dennavit-Hartenberg-Craig matrix method. Although each method has its advantages, they have mostly encountered difficulties in establishing equations associated with complicated spatial structures. This research proposed a novel method, entitled "'perpendicular projection method" to solve the problem of spatial structural kinematic analysis. This is an approximate solution method that combines the use of generalized coordinates and direction cosine matrices. With this method, the problem of kinematic analysis of spatial structures will be solved simply and easily by establishing connection equations as well as simulating the motion of spatial structures. This method was then applied in conducting the kinematic analysis of a spatial mechanism with 1 rotating joint (R), 1 ball joint (S), 1 translational joint (P), and one cylindrical joint (C). The calculated results, including the velocity, acceleration of generalized coordinates, angular velocity, angular acceleration of the links, velocity, and acceleration of the center of mass of the links, showed the efficiency of the proposed method. [ABSTRACT FROM AUTHOR]

Mesh-based image vectorization techniques have been studied for a long time, mostly owing to their compactness and flexibility in capturing image features. However, existing methods often lead to relatively dense meshes, especially when applied to images with high-frequency details or textures. We present a novel method that automatically vectorizes an image into a sparse collection of Coons patches whose size adapts to image features. To balance the number of patches and the accuracy of feature alignment, we generate the layout based on a harmonic cross field constrained by image features. We support T-junctions, which keeps the number of patches low and ensures local adaptation to feature density, naturally complemented by varying mesh-color resolution over the patches. Our experimental results demonstrate the utility, accuracy, and sparsity of our method.

This article discusses a universal way to create animation using Asymptote the language for vector graphics. The Asymptote language itself has a built-in library for creating animations, but its practical use is complicated by an extremely brief description in the official documentation and unstable execution of existing examples. The purpose of this article is to eliminate this gap. The method we describe is based on creating a PDF-file with frames using Asymptote, with further converting it into a set of PNG images and merging them into a video using FFmpeg. All stages are described in detail, which allows the reader to use the described method without being familiar with the used utilities.

Motion graphics videos are widely used in Web design, digital advertising, animated logos and film title sequences, to capture a viewer's attention. But editing such video is challenging because the video provides a low-level sequence of pixels and frames rather than higher-level structure such as the objects in the video with their corresponding motions and occlusions. We present a motion vectorization pipeline for converting motion graphics video into an SVG motion program that provides such structure. The resulting SVG program can be rendered using any SVG renderer (e.g. most Web browsers) and edited using any SVG editor. We also introduce a program transformation API that facilitates editing of a SVG motion program to create variations that adjust the timing, motions and/or appearances of objects. We show how the API can be used to create a variety of effects including retiming object motion to match a music beat, adding motion textures to objects, and collision preserving appearance changes. [ABSTRACT FROM AUTHOR]

Interface icons are prevalent in various digital applications. Due to limited time and budgets, many designers rely on informal evaluation, which often results in poor usability icons. In this paper, we propose a unique human‐in‐the‐loop framework that allows our target users, that is novice and professional user interface (UI) designers, to improve the usability of interface icons efficiently. We formulate several usability criteria into a perceptual usability function and enable users to iteratively revise an icon set with an interactive design tool, EvIcon. We take a large‐scale pre‐trained joint image‐text embedding (CLIP) and fine‐tune it to embed icon visuals with icon tags in the same embedding space (IconCLIP). During the revision process, our design tool provides two types of instant perceptual usability feedback. First, we provide perceptual usability feedback modelled by deep learning models trained on IconCLIP embeddings and crowdsourced perceptual ratings. Second, we use the embedding space of IconCLIP to assist users in improving icons' visual distinguishability among icons within the user‐prepared icon set. To provide the perceptual prediction, we compiled IconCEPT10K, the first large‐scale dataset of perceptual usability ratings over 10,000 interface icons, by conducting a crowdsourcing study. We demonstrated that our framework could benefit UI designers' interface icon revision process with a wide range of professional experience. Moreover, the interface icons designed using our framework achieved better semantic distance and familiarity, verified by an additional online user study. [ABSTRACT FROM AUTHOR]

Given that the meaning of an image is rarely self-evident using traditional keyword and/or content-based descriptions, the general goal of this study is to convert, with minimal human intervention, a stream of web vector graphics into a searchable knowledge graph structure that encodes semantically relevant image contents. To do so, we introduce an original framework titled SSG which automatically converts a stream of SVG images and objects into a semantic graph. We introduce an incremental clustering approach to semantically annotate SVG images and their constituent objects in a fast and efficient manner, using an aggregation of shape, area, color, and location similarity measures. We then produce an RDF graph representation of the input image and integrate it in a reference knowledge graph, incrementally extending its semantic expressiveness to improve future annotation tasks. This achieves semantization of vector image contents with minimum human effort and training data, while complying with native Web standards (i.e., SVG and RDF) to preserve transparency in representing and searching images using Semantic Web stack technologies. Our solution is of linear complexity in the number of images and clusters used. We have conducted a large battery of experiments to test and evaluate our approach. We have created a labelled SVG dataset consisting of 22,553 objects from 750 images based on panoramic dental X-ray images. To our knowledge, it is the first significant dataset of labelled SVG objects and images, which we make available online as a benchmark for future research in this area. Results underline our approach's effectiveness, and its applicability in a practical application domain. [ABSTRACT FROM AUTHOR]

A vector sketch is a popular and natural geometry representation depicting a 2D shape. When viewed from afar, the disconnected vector strokes of a sketch and the empty space around them visually merge into positive space and negative space, respectively. Positive and negative spaces are the key elements in the composition of a sketch and define what we perceive as the shape. Nevertheless, the notion of positive or negative space is mathematically ambiguous: While the strokes unambiguously indicate the interior or boundary of a 2D shape, the empty space may or may not belong to the shape's exterior. For standard discrete geometry representations, such as meshes or point clouds, some of the most robust pipelines rely on discretizations of differential operators, such as Laplace-Beltrami. Such discretizations are not available for vector sketches; defining them may enable numerous applications of classical methods on vector sketches. However, to do so, one needs to define the positive space of a vector sketch, or the sketch shape. Even though extracting this 2D sketch shape is mathematically ambiguous, we propose a robust algorithm, Alpha Contours, constructing its conservative estimate: a 2D shape containing all the input strokes, which lie in its interior or on its boundary, and aligning tightly to a sketch. This allows us to define popular differential operators on vector sketches, such as Laplacian and Steklov operators. We demonstrate that our construction enables robust tools for vector sketches, such as As-Rigid-As-Possible sketch deformation and functional maps between sketches, as well as solving partial differential equations on a vector sketch. [ABSTRACT FROM AUTHOR]

Artist sketches often use multiple overdrawn strokes to depict a single intended curve. Humans effortlessly mentally consolidate such sketches by detecting groups of overdrawn strokes and replacing them with the corresponding intended curves. While this mental process is near instantaneous, manually annotating or retracing sketches to communicate this intended mental image is highly time consuming; yet most sketch applications are not designed to handle overdrawing and can only operate on overdrawing-free, consolidated sketches. We propose StripMaker, a new and robust learning based method for automatic consolidation of raw vector sketches. We avoid the need for an unsustainably large manually annotated learning corpus by leveraging observations about artist workflow and perceptual cues viewers employ when mentally consolidating sketches. We train two perception-aware classifiers that assess the likelihood that a pair of stroke groups jointly depicts the same intended curve: our first classifier is purely local and only accounts for the properties of the evaluated strokes; our second classifier incorporates global context and is designed to operate on approximately consolidated sketches. We embed these classifiers within a consolidation framework that leverages artist workflow: we first process strokes in the order they were drawn and use our local classifier to arrive at an approximate consolidation output, then use the contextual classifier to refine this output and finalize the consolidated result. We validate StripMaker by comparing its results to manual consolidation outputs and algorithmic alternatives. StripMaker achieves comparable performance to manual consolidation. In a comparative study participants preferred our results by a 53% margin over those of the closest algorithmic alternative (67% versus 14%, other/neither 19%). [ABSTRACT FROM AUTHOR]

Today, the objective of applying shadow theory to architectural drawing is usually to obtain one (or several) bitmap images. This a priori innocuous fact leads to vector images being conspicuous by their absence when lighting factors come into play during the modelling and parametric design of architecture and its environment. The present article sets out a methodology to calculate shadows by placing vector drawing not only at the heart of the process but also of the obtained results. We found that in today's context of university excellence, it is essential to apply knowledge, control, as well as possible editing and postproduction of the effects generated by rendering process lighting calculations. Such interventions are also generally useful and effective in our profession. [ABSTRACT FROM AUTHOR]

This article proposes a method for computing the visible occluding contours of subdivision surfaces. The article first introduces new theory for contour visibility of smooth surfaces. Necessary and sufficient conditions are introduced for when a sampled occluding contour is valid, that is, when it may be assigned consistent visibility. Previous methods do not guarantee these conditions, which helps explain why smooth contour visibility has been such a challenging problem in the past. The article then proposes an algorithm that, given a subdivision surface, finds sampled contours satisfying these conditions, and then generates a new triangle mesh matching the given occluding contours. The contours of the output triangle mesh may then be rendered with standard non-photorealistic rendering algorithms, using the mesh for visibility computation. The method can be applied to any triangle mesh, by treating it as the base mesh of a subdivision surface. [ABSTRACT FROM AUTHOR]

This article presents a novel deep learning-based approach for automatically vectorizing and synthesizing the clipart of man-made objects. Given a raster clipart image and its corresponding object category (e.g., airplanes), the proposed method sequentially generates new layers, each of which is composed of a new closed path filled with a single color. The final result is obtained by compositing all layers together into a vector clipart image that falls into the target category. The proposed approach is based on an iterative generative model that (i) decides whether to continue synthesizing a new layer and (ii) determines the geometry and appearance of the new layer. We formulated a joint loss function for training our generative model, including the shape similarity, symmetry, and local curve smoothness losses, as well as vector graphics rendering accuracy loss for synthesizing clipart recognizable by humans. We also introduced a collection of man-made object clipart, ClipNet, which is composed of closed-path layers, and two designed preprocessing tasks to clean up and enrich the original raw clipart. To validate the proposed approach, we conducted several experiments and demonstrated its ability to vectorize and synthesize various clipart categories. We envision that our generative model can facilitate efficient and intuitive clipart designs for novice users and graphic designers. [ABSTRACT FROM AUTHOR]

The description of the steganographic method for embedding the digital watermark into image vector files of the SVG format is given. Vector images in SVG format can include elements based on Bezier curves. The proposed steganographic method is based on the splitting of cubic Bezier curves. Embedding hidden information involves splitting cubic Bezier curves according to the digital watermark given as numerical sequence. Algorithms of direct and reverse steganographic transformation are considered for proving the authenticity and integrity of a digital vector image. The StegoSVG library has been developed to implement forward and reverse steganographic transformations. The developed desktop application that implements the method is briefly described.

Camera-based wide-area self-posture estimation is an effective method to understand and learn about human motion, especially in sports. However, although rapid spatial tracking typically requires markers, prepositioned markers require extensive preparation in advance, and area projection markers exhibit problems in bright environments. In this study, we propose a system for spatial tracking and graphics display using vector-based laser projection embedded with M-sequence dashed line markers. The proposed approach is fast, wide-area, and can operate in bright environments. The system enables embedding and calibration of M-sequence codes in non-circular vector shapes, as well as rapid image processing recognition. We verified that the accuracy and speed of the proposed approach sufficed through static and dynamic tracking evaluations. We also demonstrate a practical application. [ABSTRACT FROM AUTHOR]

Many sketch processing applications target precise vector drawings with accurately specified stroke intersections, yet free-form artist drawn sketches are typically inexact: strokes that are intended to intersect often stop short of doing so. While human observers easily perceive the artist intended stroke connectivity, manually, or even semi-manually, correcting drawings to generate correctly connected outputs is tedious and highly time consuming. We propose a novel, robust algorithm that extracts viewer-perceived stroke connectivity from inexact free-form vector drawings by leveraging observations about local and global factors that impact human perception of inter-stroke connectivity. We employ the identified local cues to train classifiers that assess the likelihood that pairs of strokes are perceived as forming end-to-end or T- junctions based on local context. We then use these classifiers within an incremental framework that combines classifier provided likelihoods with a more global, contextual and closure-based, analysis. We demonstrate our method on over 95 diversely sourced inputs, and validate it via a series of perceptual studies; participants prefer our outputs over the closest alternative by a factor of 9 to 1. [ABSTRACT FROM AUTHOR]

We propose the use of curved triangles and mesh colours as a vector primitive for image vectorisation. We show that our representation has clear benefits for rendering performance, texture detail, as well as further editing of the resulting vector images. The proposed method focuses on efficiency, but it still leads to results that compare favourably with those from previous work. We show results over a variety of input images ranging from photos, drawings, paintings, all the way to designs and cartoons. We implemented several editing workflows facilitated by our representation: interactive user-guided vectorisation, and novel raster-style feature-aware brushing capabilities. [Display omitted] [ABSTRACT FROM AUTHOR]

Phylogenetic trees are useful tools to infer evolutionary relationships between genetic entities. Phylogenetics enables not only evolution-based gene clustering but also the assignment of gene duplication and deletion events to the nodes when coupled with statistical approaches such as bootstrapping. However, extensive gene duplication and deletion events bring along a challenge in interpreting phylogenetic trees and require manual inference. In particular, there has been no robust method of determining whether one of the paralog clades systematically shows higher divergence following the gene duplication event as a sign of functional divergence. Here, we provide Phylostat, a graphical user interface that enables clade divergence analysis, visually and statistically. Phylostat is a web-based tool built on phylo.io to allow comparative clade divergence analysis, which is available at https://phylostat.adebalilab.org under an MIT open-source licence. [ABSTRACT FROM AUTHOR]

Due to the risk of data leakage while training deep learning models in a shared environment, we propose a new privacy-preserving deep learning (PPDL) method using a structural image de-identification approach for object classification. The proposed structural image de-identification approach is designed based on the fact that the degree of structural distortion of an image object has the greatest impact on human's perceptual system. Thus, by modifying only the structural parts of the original one using order preserving encryption(OPE), the proposed structural image de-identification approach decreases only the recognition rate by human. From the experimental results using different standard datasets, we show that the object classification accuracy of the proposed structural image de-identification method is almost the same as the deep learning performance for non-encrypted images, without revealing the original image contents including sensitive information. Also, by handling the trade-off between object classification accuracy and privacy protection for the de-identified image, we experimentally find the optimal size of input image for the proposed structural image de-identification approach.

Diffusion curves image (DCI) is a kind of vector graphics with its geometric primitives defined by a set of Bézier curves. As one of the vector graphics, editability is an important property for diffusion curves. However, most of the current DCI shape manipulation methods are typically limited to single curve. It is tedious for users to edit the shape of DCI by deforming each curve individually, especially with the increasing number of curves. In addition, the image contents cannot well preserved because there is no constraints among curves. We present an efficient and flexible DCI manipulation method by combining global and local deformations. The overall shape is easily changed by our global manipulation tool with a few user interactions, while the local manipulation tool is used to modify details. These two tools can be combined to obtain desired effects. In both cases, the constraints during deformation preserve the quality of the editing results. Our experimental results demonstrate that the proposed method provides more convenient control for DCI shape editing than existing techniques based on diffusion curves.

We present an assistive system for clipart design by providing visual scaffolds from the unseen viewpoints. Inspired by the artists' creation process, our system constructs the visual scaffold by first synthesizing the reference 3D shape of the input clipart and rendering it from the desired viewpoint. The critical challenge of constructing this visual scaffold is to generate a reference 3D shape that matches the user's expectations in terms of object sizing and positioning while preserving the geometric style of the input clipart. To address this challenge, we propose a user‐assisted curve extrusion method to obtain the reference 3D shape. We render the synthesized reference 3D shape with a consistent style into the visual scaffold. By following the generated visual scaffold, the users can efficiently design clipart with their desired viewpoints. The user study conducted by an intuitive user interface and our generated visual scaffold suggests that our system is especially useful for estimating the ratio and scale between object parts and can save on average 57% of drawing time. [ABSTRACT FROM AUTHOR]

Introduction. The article discusses the positive aspects of the introduction of digital simulators of pedagogical activity into the process of professional training of teachers. The subject of the research is the most significant technical factors affecting the design of a digital simulator for professional training of mathematics teachers. Methodological approaches to simulating the process of learning to find solutions to problems are considered.Materials and Methods. In the article there was conducted theoretical analysis of the complex domestic and foreign scientific sources (articles and publications on the research topic, the results of dissertational researches in pedagogics and psychology), which generalize and systematize international and Russian experience in relation to teacher training, and in terms of the characteristics of different virtual simulators. There were applied a method of theoretical generalization, content analysis, technical algorithm for using computer programs in the designing of a simulator.Results. As a result of the authors’ analysis and interpretation of available sources, the data of international and Russian studies are summarized and systematized. Various platforms and technical characteristics that allow implementing simulators are analyzed. The technical data obtained during the analysis allowed us to identify and justify the process of creating the simulator platform. The visual concept of a simulator, which provides for the use of vector and raster graphics packages, is presented.Discussion and Conclusions. The article presents the stages of development and implementation of simulators in the professional training of teachers. The new digital educational environment with the use of computer modeling capabilities will allow future teachers to master professional competencies by training virtual students.

This paper presents a vector painting system for digital artworks. We first propose Temporal Diffusion Curve (TDC), a new form of vector graphics, and a novel random-access solver for modeling the evolution of strokes. With the help of a procedural stroke processing function, the TDC strokes can achieve various shapes and effects for multiple art styles. Based on these, we build a painting system of great potential. Thanks to the random-access solver, our method has real-time performance regardless of the rendering resolution, provides straightforward editing possibilities on strokes both at runtime and afterward, and is effective and straightforward for art production. Compared with the previous Diffusion Curve, our method uses strokes as the basic graphics primitives, which are able to intersect each other and much more consistent with the intuition and painting habits of human. We finally demonstrate that professional artists can create multiple genres of artworks with our painting system. [ABSTRACT FROM AUTHOR]

Through this paper we want to show the ways in which we can exploit the CAD data model in the GIS environment. Knowing that in the GIS environment we also work with vector graphics in addition to raster graphics, it is visible the resemblance to computer-aided design (CAD), the editing aspects of digital geometry being similar. On the other hand, it is known that these formats that contain vector graphics and are accepted as a global exchange standard (DXF, DWG - promoted by AutoCAD / Autodesk) or (DGN - promoted by MicroStation / Bentley) can be an important primary source for processes and modelling highlighted on GIS platforms. The basic idea of the paper is created on the CAD model's residence in the native environment on the one hand, but also its relocation in the GIS environment, demonstrating portability through import-export operations. [ABSTRACT FROM AUTHOR]

Abstract Diffusion curves can be used to generate vector graphics images with smooth variation by solving Poisson equations. However, using the classical diffusion curve model, it is difficult to ensure that the generated diffusion image satisfies desired constraints. In this paper, we develop a model for producing a diffusion image by solving a diffusion equation with diffusion coefficients, in which color layers and coefficient layers are introduced to facilitate the generation of the diffusion image. Doing so allows us to impose various constraints on the diffusion image, such as diffusion strength, diffusion direction, diffusion points, etc., in a unified computational framework. Various examples are presented in this paper to illustrate the capabilities of our model.

We introduce a differentiable rasterizer that bridges the vector graphics and raster image domains, enabling powerful raster-based loss functions, optimization procedures, and machine learning techniques to edit and generate vector content. We observe that vector graphics rasterization is differentiable after pixel prefiltering. Our differentiable rasterizer offers two prefiltering options: an analytical prefiltering technique and a multisampling anti-aliasing technique. The analytical variant is faster but can suffer from artifacts such as conflation. The multisampling variant is still efficient, and can render high-quality images while computing unbiased gradients for each pixel with respect to curve parameters. We demonstrate that our rasterizer enables new applications, including a vector graphics editor guided by image metrics, a painterly rendering algorithm that fits vector primitives to an image by minimizing a deep perceptual loss function, new vector graphics editing algorithms that exploit well-known image processing methods such as seam carving, and deep generative models that generate vector content from raster-only supervision under a VAE or GAN training objective. [ABSTRACT FROM AUTHOR]

The aim of this paper is to show the method of the perspective restitution in the photography of the long lost Valencian palace of Jura Real, in order to digitally rebuild its façade. This work has been carried out through a search for old graphic and photographic material, in public and private archives. Fortunately, some elements have been found that belonged to the demolished palace, which provide a metric reference. As a result of the graphic procedure, details of which can be found in this text, the missing façade has been redrawn by introducing the information into a vector-drawing program (Autocad). [ABSTRACT FROM AUTHOR]

The problem of differences identifying in vector graphics data packages and how to solve it is considered. Map information and vectorized data of remote sensing of Earth are sources of vector data in the article. The aim is to design a method for detecting differences in vector data packages, providing reliable results for updating map and monitoring areas tasks. Research and development is done by mathematical modeling of the task in MATLAB. The article provides developed method for solving the task and results of its application for finding differences between two vector data packages, obtained from the target information of remote sensing of the Earth, and/or vector layers of digital district map. This method allows you to automate the process and reduce the time of thematic analysis of cosmic information, obtained from remote sensing of the Earth for topographic mapping and monitoring areas.

• A novel tool for interactively creating vector graphics of complex color gradients, which allows painting in regions in an intuitive way. • A new diffusion curve colorization algorithm is proposed for fitting sparse colors of the user input scribbles. • Our technique is implemented as a real-time drawing application and the system is able to achieve real-time performance. This paper proposes a novel colorizaiton tool for intuitively creating smooth-shaded vector graphics. Our technique takes advantage of diffusion curves, a powerful vector graphics representation. Rather than specifying colors along curves as done in previous works, we allow a user to intuitively paint in regions of arbitrary line drawings. Once the user scribbles on drawings, our algorithm automatically estimates the colors along the curves of drawings, resulting in smooth color regions as close as possible to the user specification. Different from the previous color estimation techniques for image vectorization, A new diffusion curve colorization algorithm is proposed for fitting sparse colors of input scribbles. Our approach is fast and provides instant feedback to the user. We have tested our system on a variety of line drawings with varying shape complexity, and shown that our technique can produce visually pleasing smooth-shaded images intuitively and effectively. [ABSTRACT FROM AUTHOR]

In this paper, we introduce the CPatch, a curved primitive that can be used to construct arbitrary vector graphics. A CPatch is a generalization of a 2D polygon: Any number of curves up to a cubic degree bound a primitive. We show that a CPatch can be rasterized efficiently in a hierarchical manner on the GPU, locally discarding irrelevant portions of the curves. Our rasterizer is fast and scalable, works on all patches in parallel, and does not require any approximations. We show a parallel implementation of our rasterizer, which naturally supports all kinds of color spaces, blending and super‐sampling. Additionally, we show how vector graphics input can efficiently be converted to a CPatch representation, solving challenges like patch self intersections and false inside‐outside classification. Results indicate that our approach is faster than the state‐of‐the‐art, more flexible and could potentially be implemented in hardware. [ABSTRACT FROM AUTHOR]

Image tracing is a foundational component of the workflow in graphic design, engineering, and computer animation, linking hand-drawn concept images to collections of smooth curves needed for geometry processing and editing. Even for clean line drawings, modern algorithms often fail to faithfully vectorize junctions, or points at which curves meet; this produces vector drawings with incorrect connectivity. This subtle issue undermines the practical application of vectorization tools and accounts for hesitance among artists and engineers to use automatic vectorization software. To address this issue, we propose a novel image vectorization method based on state-of-the-art mathematical algorithms for frame field processing. Our algorithm is tailored specifically to disambiguate junctions without sacrificing quality. [ABSTRACT FROM AUTHOR]

Analyzing and evaluating the quality of processes in an organization is of great importance for the future direction of the organization. To achieve accurate results, managers use many useful methods. SWOT analysis can be an effective tool. It is a structured planning method used to assess strengths, weaknesses, opportunities, and threats. The paper deals with the analysis of the supply process and the creation of vector graph strategies. [ABSTRACT FROM AUTHOR]