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ColorSketch: A Drawing Assistant for Generating Color Sketches from Photos
Abstract
A color sketch creates a vivid depiction of a scene using sparse pencil strokes and casual colored brush strokes. In this paper, we introduce an interactive drawing system, called ColorSketch, for helping novice users generate color sketches from photos. Our system is motivated by the fact that novice users are often capable of tracing object boundaries using pencil strokes, but have difficulties to choose proper colors and brush over an image region in a visually pleasing way. To preserve artistic freedom and expressiveness, our system lets users have full control over pencil strokes for depicting object shapes and geometric details at an appropriate level of abstraction, and automatically augment pencil sketches using color brushes, such as color mapping, brush stroke rendering as well as blank area creation. Experimental and user study results demonstrate that users, especially novice ones, can generate much better color sketches more efficiently with our system than using traditional manual tools.
... Articles Increase efficiency in an activity [11], [24], [25], [26], [27], [28], [29], [30], [31], [32], [33], [34], [35], [36], [37], [38], [39], [40], [41], [42], [43], [44], [45], [46], [47], [48], [12], [1], [49], [50], [51], [52], [53], [54], [55], [56], [57], [58], [59] Better cope with an illness [60], [61], [62], [63], [64], [65], [66], [67], [68], [69], [70], [71], [41], [3], [72], [73], [74], [75], [76], [77], [78], [79], [80], [81] Resolve a problem [82], [51], [52], [83], [84], [85], [3], [39], [67], [86], [87], [58], [88], [89], [90], [91], [92], [93], [94] Support everyday situations [95], [96], [97], [98], [53], [54], [60], [99], [74], [77], [90], [91], [100] Attain a healthy life [101], [102], [2], [103], [104], [105], [106], [78], [59], [107], [93], [80], [81] Refine skills [108], [109], [110], [11], [24], [29], [111], [112], [113], [114], [115] Learn [49], [50], [116], [111], [117], [112], [113], [114] ...
... Articles Increase efficiency in an activity [11], [24], [25], [26], [27], [28], [29], [30], [31], [32], [33], [34], [35], [36], [37], [38], [39], [40], [41], [42], [43], [44], [45], [46], [47], [48], [12], [1], [49], [50], [51], [52], [53], [54], [55], [56], [57], [58], [59] Better cope with an illness [60], [61], [62], [63], [64], [65], [66], [67], [68], [69], [70], [71], [41], [3], [72], [73], [74], [75], [76], [77], [78], [79], [80], [81] Resolve a problem [82], [51], [52], [83], [84], [85], [3], [39], [67], [86], [87], [58], [88], [89], [90], [91], [92], [93], [94] Support everyday situations [95], [96], [97], [98], [53], [54], [60], [99], [74], [77], [90], [91], [100] Attain a healthy life [101], [102], [2], [103], [104], [105], [106], [78], [59], [107], [93], [80], [81] Refine skills [108], [109], [110], [11], [24], [29], [111], [112], [113], [114], [115] Learn [49], [50], [116], [111], [117], [112], [113], [114] ...
... Articles Health [101], [60], [24], [61], [62], [63], [64], [65], [66], [67], [68], [36], [37], [102], [40], [69], [41], [70], [85], [3], [71], [73], [105], [74], [75], [76], [77], [86], [78], [79], [87], [88], [89], [114], [115], [107], [92], [93], [81] Education [95], [49], [50], [39], [43], [109], [116], [47], [112], [113] Online services and IoT [27], [30], [53], [32], [35], [42], [84], [46], [58], [100] Social [96], [31], [108], [34], [52], [54], [90], [91], [80] Domestic environment [97], [98], [33], [83], [45], [72], [99], [111] Computer science [26], [82], [28], [38], [12], [56] Enterprise / Industry [51], [48], [55], [57], [94] Fitness [2], [103], [104], [106], [59] Driving [11], [25], [44], [110] Art [29], [1], [117] Target user Articles Patients [60], [96], [61], [62], [63], [64], [65], [66], [67], [68], [36], [37], [102], [69], [41], [70], [85], [3], [71], [72], [73], [99], [105], [74], [75], [76], [77], [86], [78], [79], [88], [89], [107], [92], [93], [80], [81] Internet/Online services users [27], [98], [30], [53], [31], [32], [35], [42], [83], [84], [46], [100] Students [95], [49], [50], [39], [43], [109], [116], [47], [117], [112], [113] Learner/Trainee users [29], [33], [108], [45], [1], [111], [115] Workers [101], [51], [48], [55], [57], [58], [94] General users [97], [34], [52], [87], [90], [91] Computer experts [26], [82], [28], [38], [12], [56] Fitness users [2], [103], [104], [106], [59] Drivers [11], [25], [44], [110] Health professionals [24], [40], [114] Senior citizens [54] 3.2.1. RQ1. ...
An intelligent assistant (IA) is a computer system endowed with artificial intelligence and/or machine learning techniques capable of intelligently assisting people. IAs have gained in popularity over recent years due to their usefulness, significant commercial developments, and a myriad of scientific and technological advances resulting from research efforts by the computer science community. In particular, these efforts have led to an increasingly extensive and complex state of the art of IAs, making evident the need to carry out a review in order to identify and catalogue the advances in the construction of IAs as well as to detect potential areas of further research. This paper presents a systematic review aiming to (i) describe, classify, and organize recent advances in IAs; (ii) characterize IAs’ objectives, application domains, and workings; (iii) analyze how IAs have been evaluated; and (iv) identify what artificial intelligence and machine learning techniques are used to enable the intelligence of IAs. As a result of this systematic review, it is also proposed a taxonomy of IAs and a set of potential future research directions to further improve IAs. A set of research questions was formulated to guide this systematic review and address the proposed objectives. Well-known scientific databases were searched for articles on IAs published from January 2015 to June 2021 using 172 search strings. A total of 22,554 articles were retrieved and after applying inclusion, exclusion, and quality criteria, an overall number of 99 articles were selected, which are the basis for this systematic review.
... Pencil is one of the most accessible and easy-to-use painting tools. As one of the most fundamental drawing languages to abstract the understanding of the nature scene [1], pencil drawing has a unique attraction. With the increasing popularity of the image capture devices and the Internet, the artistic style of converting nature images into pencil drawings has become increasingly popular. ...
... With the increasing popularity of the image capture devices and the Internet, the artistic style of converting nature images into pencil drawings has become increasingly popular. At the same time, the Non-Photorealistic Rendering (NPR) [1]- [5] of images The associate editor coordinating the review of this manuscript and approving it for publication was Jiachen Yang . has increased in line with the public aesthetics, since it pays attention to the graphic art and the personalized expression. ...
The artistic stylization transformation of images is an important part in the research of Non-Photorealistic Rendering (NPR). This kind of non-realistic stylized research has a very strong realistic romantic temperament and it can express the artistic pursuit of researchers very well. It is a good example of practical application combining computer science and art. In the technology of image style conversion, the simulation of the realistic art style includes mainly cartoon style, oil painting style, ink painting style and pencil drawing style. Previously, some researchers proposed a style conversion method based on the prior knowledge that there might be crosses at the junction of two lines where the tone distribution has a certain rule. However, in this paper, we have focused on the use of an edge extraction algorithm based on Non-symmetry and Anti-packing pattern representation Model (NAM) to enhance the pencil strokes and highlight the details of the image. Then, by combining the line pencil strokes with the tone drawing, we can transform the image into an image with pencil sketch style. Finally, compared with the most important previous methods, the experimental results presented in this paper showed that our proposed method showed more clearly the scene and the content and more details of the image than the previous methods. We also verified our method and calculated Structural Similarity (SSIM) and Peak Signal-to-Noise Ratio (PSNR) of original images and color pencil drawings with different algorithms on the 7 nature images and the BSDS500 data set.
... Many drawing support tools adopt reference images and provide intelligent assistance to novices, e.g., beautifying user's sketches with extracted image features [10][11][12][13], or providing educational guidance to novice users [14][15][16]. We share a similar goal to Refs. ...
Image-guided drawing can compensate for a lack of skill but often requires a significant number of repetitive strokes to create textures. Existing automatic stroke synthesis methods are usually limited to predefined styles or require indirect manipulation that may break the spontaneous flow of drawing. We present an assisted drawing system to autocomplete repetitive short strokes during a user’s normal drawing process. Users draw over a reference image as usual; at the same time, our system silently analyzes the input strokes and the reference to infer strokes that follow the user’s input style when certain repetition is detected. Users can accept, modify, or ignore the system’s predictions and continue drawing, thus maintaining fluid control over drawing. Our key idea is to jointly analyze image regions and user input history to detect and predict repetition. The proposed system can effectively reduce the user’s workload when drawing repetitive short strokes, helping users to create results with rich patterns.
... Beberapa pengkaji menjelaskan aktiviti melakar boleh dibuat dalam pelbagai jenis seperti pensel (Shah et al., 2001;Ullman et al., 1990) pen (Olofsson & Sjölén, 2006), pensel warna (Zhang, Li, Wong, Ji, & Liu, 2018;Li, Bi, Wang, Xu, & Yu, 2017), pastel, pen marker, arang, krayon dan media campuran (Tachie-Menson, Asare, & Essel, 2015). Pelajar harus terdedah dan membuat eksperimen untuk membezakan pelbagai jenis pensel (2B, 4B,6B,8B), pen, arang, warna pastel, warna pensel, marker dan warna air, cat minyak, pen teknikal dan dakwat cina malahan pahat dan berlian juga boleh digunakan dalam berkarya bagi meningkatkan kualiti dan menghasilkan karya yang lebih menarik dan kreatif. ...
Lakaran boleh digunakan sebagai medium komunikasi untuk menyampaikan maklumat. Dalam konteks seni visual, lakaran digunakan sebagai asas penjanaan idea, untuk mencipta atau menghasilkan karya. Walau bagaimanapun, lakaran yang dihasilkan perlu beraras tinggi supaya penciptaan idea menepati aspek pengkaryaan yang kreatif, kritis dan mempunyai kualiti estetik yang diperlukan. Artikel ini bertujuan untuk mengemukakan konsep dan aplikasi Kemahiran Melakar Aras Tingi (KMAT) dalam pengkaryaan seni visual yang sesuai untuk sekolah menengah. Konsep KMAT diutarakan sejajar cabaran pengajaran dan pembelajaran yang perlu memfokuskan kepada Kemahiran Berfikir Aras Tinggi (KBAT) yang meletakkan aras mencipta di tahap tertinggi. Untuk mencapai KMAT, terdapat tiga tahap yang perlu dikuasai semasa proses penghasilan karya. Ia dimulai dengan (i) peringkat imitasi iaitu peniruan 100% dan memahami bentuk asas, (ii) peringkat manipulasi iaitu kepelbagaian penggunaan alat dan bahan, kefahaman kadar banding dan komposisi, kefahaman perspektif dan peringkat tertinggi iaitu (iii) Naturalisasi yang melibatkan penzahiran idea dengan cara tersendiri dan fasih mengaitkan isu, tema dan budaya dalam lakaran. Namun, kunci kepada kejayaan KMAT iaitu penguasaan kefahaman dan pengaplikasian unsur seni dan prinsip rekaan yang menyeluruh dalam pengkaryaan disertai dengan minat dan bakat yang mendalam. Oleh itu, artikel ini mencadangkan supaya pelajar, guru dan golongan yang berminat dalam pengkaryaan seperti pelukis atau pereka dapat menggunakan KMAT sebagai satu panduan melahirkan idea lakaran yang lebih kritis, kreatif di tahap tertinggi bagi memenuhi kualiti penciptaan yang diperlukan sejajar dengan konteks revolusi Industri 4.0.
... Our approach is based on a combination of two existing GAN-based techniques, the cGAN of Isola et al. 5 Conditional GAN Image synthesis via imageto-image translation is an important task in computer vision. Image-to-image translations can be achieved by convolutional neural networks 7,8 or GANs. 9 For instance, GAN-based models have a proven capacity to colorize 10 or restyle images automatically. ...
Automated image style transfer is of great interest given the recent advances in generative adversarial networks (GANs). However, it is challenging to generate synthesized images from abstract masks while preserving detailed patterns for certain kinds of art given small datasets. We propose an intelligent GAN-based system enhanced with user intent and prior knowledge for generating images styled as Cantonese porcelain using user-defined masks. Given a mask with specified objects, our system first generates a synthesized natural image. We then use a novel semantic user intent enhancement module to retrieve semantically relevant images from an image dataset. Objects in the retrieved image are used to refine local patterns in the synthesized image. Finally, the refined image is restyled in the Cantonese porcelain style. The system is trained by 454 pairs of natural images and semantic segmentation of 24 objects from the COCO dataset
... Many drawing support tools adopt reference images and provide intelligent assistance to novices, e.g., beautifying users' sketches with extracted image features [20,25,41,47], or providing educational guidance to novice users [16,31,45]. We share a similar goal to [3,10,43] so as to reduce the user workload. ...
Image-guided drawing can compensate for the lack of skills but often requires a significant number of repetitive strokes to create textures. Existing automatic stroke synthesis methods are usually limited to predefined styles or require indirect manipulation that may break the spontaneous flow of drawing. We present a method to autocomplete repetitive short strokes during users' normal drawing process. Users can draw over a reference image as usual. At the same time, our system silently analyzes the input strokes and the reference to infer strokes that follow users' input style when certain repetition is detected. Users can accept, modify, or ignore the system predictions and continue drawing, thus maintaining the fluid control of drawing. Our key idea is to jointly analyze image regions and operation history for detecting and predicting repetitions. The proposed system can effectively reduce users' workload in drawing repetitive short strokes and facilitates users in creating results with rich patterns.
... In recent years, there has been a new wave of research on developing intelligent user interfaces to help novice users create artistically expressive sketches and drawings. Representative examples include ShadowDraw [31], EZ-Sketch [42], ColorSketch [32], and the repetition autocompletion system [49]. By combining intelligent computational algorithms with intuitive UI controls, these systems enable novice users with little or no formal artistic training to create static drawings of high quality with minimal user effort, which otherwise cannot be achieved using traditional tools. ...
Creating sketch animations using traditional tools requires special artistic skills, and is tedious even for trained professionals. To lower the barrier for creating sketch animations,
we propose a new system, Live Sketch, which allows novice users to interactively bring static drawings to life by applying deformation-based animation effects that are extracted from video examples. Dynamic deformation is first extracted as a sparse set of moving control points from videos and then transferred to a static drawing. Our system addresses a few major technical challenges, such as motion extraction from video, video-to-sketch alignment, and many-to-one motion driven sketch animation. While each of the sub-problems could be difficult to solve fully automatically, we present reliable solutions by combining new computational algorithms with intuitive user interactions. Our pilot study shows that our system allows both users with or without animation skills to easily add dynamic deformation to static drawings.
The development of deep learning algorithms has prompted many attempts to apply AI(Artificial Intelligence) to artistic creation in the last decade. It has been observed that the number of intelligent systems applied to drawing is also increasing. However, many concepts of intelligent systems applied to drawing or drawing assistance still need to be clarified, lacking in-depth research on the contents of drawing and human. No widely used uniform principles, methods, or frameworks have been developed. Although some review studies have been carried out on human-AI co-creation systems, their focus has been chiefly on interactive modes, application areas, workflows, evaluation methods, and technical approaches. There needs to be a more comprehensive analysis of intelligent systems devoted explicitly to the creation of drawings. More attention should be paid to the various contents and tasks of drawing. In this paper, we added the dimension of drawing contents and combined it with other dimensions used in previous literature reviews to analyze intelligent systems or tools applied to drawing since the last decade. This investigation aims to uncover the development status, drawbacks, trends, and reasons. Based on these findings, we provide suggestions for the future development of intelligent assistance drawing systems.KeywordsIntelligent systems and toolsDrawingAI generated content
Pencil drawing is recognized as a typical non-photorealistic visual art form. It is attractive to automatically generate high-quality pencil drawings from real-world photographs. Traditional model-based methods are highlighted in their interpretability, but are limited in modeling scene semantics, leading to weak sketch lines or limited pencil tone rendering effects. To address this issue, we propose a novel pencil drawing generation method via explicitly leveraging image semantic information. Specifically, the sketch lines generated from the weak boundaries can be boosted by fusing the segmented boundaries. In addition, the segmentation mask provides the spatial guidance for diversifying the tone shading effects. By combining the enhanced pencil drawing patterns, the visual quality of the generated pencil drawings can be therefore strengthened. We conduct several experiments to validate the effectiveness of our method, including ablation studies and comparison with other methods, in which promising results can be obtained.
Recent advances in deep learning based algorithms have made it feasible to transfer image styles from an example image to other images. However, it is still hard to transfer the style of color sketches due to their unique texture statistics. In this paper, an automatic color sketch generation system is developed from existing real-time style transfer methods. We choose a suitable image from a set of carefully selected color sketch examples as the style target for every content image during training. We also propose a novel style transfer CNN with spatial refinement to realize high-resolution style transfer. Finally, gouache color is introduced to the generated images via a linear color transform followed by a guided filtering operation. Experimental results illustrate that our system can produce vivid color sketch images and greatly reduce artifacts compared to previous state-of-the-art methods.
Current digital painting tools are primarily targeted at professionals and are often overwhelmingly complex for use by novices. At the same time, simpler tools may not invoke the user creatively, or are limited to plain styles that lack visual sophistication. There are many people who are not art professionals, yet would like to partake in digital creative expression. Challenges and rewards for novices differ greatly from those for professionals. In this paper, we leverage existing works in Creativity and Creativity Support Tools (CST) to formulate design goals specifically for digital art creation tools for novices. We implemented these goals within a digital painting system, called Painting with Bob. We evaluate the efficacy of the design and our prototype with a user study, and we find that users are highly satisfied with the user experience, as well as the paintings created with our system.
We present a new image-guided drawing interface called EZ-Sketching, which uses a tracing paradigm and automatically corrects sketch lines roughly traced over an image by analyzing and utilizing the image features being traced. While previous edge snapping methods aim at optimizing individual strokes, we show that a co-analysis of multiple roughly placed nearby strokes better captures the user's intent. We formulate automatic sketch improvement as a three-level optimization problem and present an efficient solution to it. EZ-Sketching can tolerate errors from various sources such as indirect control and inherently inaccurate input, and works well for sketching on touch devices with small screens using fingers. Our user study confirms that the drawings our approach helped generate show closer resemblance to the traced images, and are often aesthetically more pleasing.
We present an interactive drawing tool that provides automated guidance over model photographs to help people practice traditional drawing-by-observation techniques. The drawing literature describes a number of techniques to support this task and help people gain consciousness of the shapes in a scene and their relationships. We compile these techniques and derive a set of construction lines that we automatically extract from a model photograph. We then display these lines over the model to guide its manual reproduction by the user on the drawing canvas. Finally, we use shape-matching to register the user's sketch with the model guides. We use this registration to provide corrective feedback to the user. Our user studies show that automatically extracted construction lines can help users draw more accurately. Furthermore, users report that guidance and corrective feedback help them better understand how to draw.
It is well established that in certain domains, noisy inputs can be reliably combined to obtain a better answer than any individual. It is now possible to consider the crowdsourc-ing of physical actions, commonly used for creative expres-sions such as drawing, shading, and singing. We provide al-gorithms for converting low-quality input obtained from the physical actions of a crowd into high-quality output. The inputs take the form of line drawings, shaded images, and songs. We investigate single-individual crowds (multiple in-puts from a single human) and multiple-individual crowds.
Abstract In this paper, we introduce an NPR system to generate color sketches in a free-hand drawing style. From observation, we found out that in a free-hand style both shape and color of image regions are stylized, which contributes cooperatively to the visual effect of a sketch. With the color regions obtained by our interactive segmentation algorithm, we create a stylized color sketch in two steps. First, in order to manifest the lighting and shading of each region, we shrink the boundary,of the color regions with a luminance-based algorithm emphasizing the highlight parts, which enforces consistency with the free-hand artist style of the sketch. Second, the proposed color shift algorithm is applied to image regions to emphasize the main content of the sketch and acquires a visually pleasing combination,of color. We choose the optimal combination,by minimizing the energy function based on an artist drawn color database, artistic drawing rules and input image colors. The generated results show that our system can produce aesthetically pleasing sketches. Keywords: non-photorealistic rendering, boundary shrinking,
When asked to draw, many people are hesitant because they consider themselves unable to draw well. This paper describes the first system for a computer to provide direction and feedback for assisting a user to draw a human face as accurately as possible from an image. Face recognition is first used to model the features of a human face in an image, which the user wishes to replicate. Novel sketch recognition algorithms were developed to use the information provided by the face recognition to evaluate the hand-drawn face. Two design iterations and user studies led to nine design principles for providing such instruction, presenting reference media, giving corrective feedback, and receiving actions from the user. The result is a proof-of-concept application that can guide a person through step-by-step instruction and generated feedback toward producing his/her own sketch of a human face in a reference image.
We present ShadowDraw, a system for guiding the freeform drawing of objects. As the user draws, ShadowDraw dynamically updates a shadow image underlying the user's strokes. The shadows are suggestive of object contours that guide the user as they continue drawing. This paradigm is similar to tracing, with two major differences. First, we do not provide a single image from which the user can trace; rather ShadowDraw automatically blends relevant images from a large database to construct the shadows. Second, the system dynamically adapts to the user's drawings in real-time and produces suggestions accordingly. ShadowDraw works by efficiently matching local edge patches between the query, constructed from the current drawing, and a database of images. A hashing technique enforces both local and global similarity and provides sufficient speed for interactive feedback. Shadows are created by aggregating the edge maps from the best database matches, spatially weighted by their match scores. We test our approach with human subjects and show comparisons between the drawings that were produced with and without the system. The results show that our system produces more realistically proportioned line drawings.
We present a semantics-driven approach for stroke-based painterly rendering, based on recent image parsing techniques [Tu et al. 2005; Tu and Zhu 2006] in computer vision. Image parsing integrates segmentation for regions, sketching for curves, and recognition for object categories. In an interactive manner, we decompose an input image into a hierarchy of its constituent components in a parse tree representation with occlusion relations among the nodes in the tree. To paint the image, we build a brush dictionary containing a large set (760) of brush examples of four shape/appearance categories, which are collected from professional artists, then we select appropriate brushes from the dictionary and place them on the canvas guided by the image semantics included in the parse tree, with each image component and layer painted in various styles. During this process, the scene and object categories also determine the color blending and shading strategies for inhomogeneous synthesis of image details. Compared with previous methods, this approach benefits from richer meaningful image semantic information, which leads to better simulation of painting techniques of artists using the high-quality brush dictionary. We have tested our approach on a large number (hundreds) of images and it produced satisfactory painterly effects.
This paper investigates two fundamental problems in computer vision: contour detection and image segmentation. We
present state-of-the-art algorithms for both of these tasks. Our contour detector combines multiple local cues into a globalization
framework based on spectral clustering. Our segmentation algorithm consists of generic machinery for transforming the output of any
contour detector into a hierarchical region tree. In this manner, we reduce the problem of image segmentation to that of contour
detection. Extensive experimental evaluation demonstrates that both our contour detection and segmentation methods significantly
outperform competing algorithms. The automatically generated hierarchical segmentations can be interactively refined by userspecified
annotations. Computation at multiple image resolutions provides a means of coupling our system to recognition applications.
We describe a system for transforming an input video into a highly abstracted, spatio-temporally coherent cartoon animation with a range of styles. To achieve this, we treat video as a space-time volume of image data. We have developed an anisotropic kernel mean shift technique to segment the video data into contiguous volumes. These provide a simple cartoon style in themselves, but more importantly provide the capability to semi-automatically rotoscope semantically meaningful regions. In our system, the user simply outlines objects on keyframes. A mean shift guided interpolation algorithm is then employed to create three dimensional semantic regions by interpolation between the keyframes, while maintaining smooth trajectories along the time dimension. These regions provide the basis for creating smooth two dimensional edge sheets and stroke sheets embedded within the spatio-temporal video volume. The regions, edge sheets, and stroke sheets are rendered by slicing them at particular times. A variety of styles of rendering are shown. The temporal coherence provided by the smoothed semantic regions and sheets results in a temporally consistent non-photorealistic appearance.
We propose a new method for the large-scale collection and analysis of drawings by using a mobile game specifically designed to collect such data. Analyzing this crowdsourced drawing database, we build a spatially varying model of artistic consensus at the stroke level. We then present a surprisingly simple stroke-correction method which uses our artistic consensus model to improve strokes in real-time. Importantly, our auto-corrections run interactively and appear nearly invisible to the user while seamlessly preserving artistic intent. Closing the loop, the game itself serves as a platform for large-scale evaluation of the effectiveness of our stroke correction algorithm.
We propose a new system to produce pencil drawing from natural images. The results contain various natural strokes and patterns, and are structurally representative. They are accomplished by novelly combining the tone and stroke structures, which complement each other in generating visually constrained results. Prior knowledge on pencil drawing is also incorporated, making the two basic functions robust against noise, strong texture, and significant illumination variation. In light of edge, shadow, and shading information conveyance, our pencil drawing system establishes a style that artists use to understand visual data and draw them. Meanwhile, it lets the results contain rich and well-ordered lines to vividly express the original scene.
While edge detection is an important first step for many vision systems, the linked lists of edge points produced by most existing edge detectors lack the higher level of curve description needed for many visual tasks. For example, they do not specify the tangent direction or curvature of an edge or the locations of tangent discontinuities. In this paper, a method is presented for describing linked edge points at a range of scales by selecting intervals of the curve and scales of smoothing that are most likely to represent the underlying structure of the scene. This multiscale analysis of curves is complementary to any multiscale detection of the original edge points. A solution is presented for the problem of shrinkage of curves during Gaussian smoothing, which has been a significant impediment to the use of smoothing for practical curve description. The curve segmentation method is based on a measure of smoothness minimizing the third derivative of Gaussian convolution. The smoothness measure is used to identify discontinuities of curve tangents simultaneously with selecting the appropriate scale of smoothing. The averaging of point locations during smoothing provides for accurate subpixel curve localization. This curve-description method can be implemented efficiently and should prove practical for a wide range of applications including correspondence matching, perceptual grouping, and model-based recognition.
This paper presents an interactive watercolor rendering technique that recreates the specific visual effects of lavis watercolor. Our method allows the user to easily process images and 3d models and is organized in two steps: an abstraction step that recreates the uniform color regions of watercolor and an effect step that filters the resulting abstracted image to obtain watercolor-like images. In the case of 3d environments we also propose two methods to produce temporally coherent animations that keep a uniform pigment repartition while avoiding the shower door effect.
Occlusion reasoning, necessary for tasks such as navi- gation and object search, is an important aspect of every- day life and a fundamental problem in computer vision. We believe that the amazing ability of humans to reason about occlusions from one image is based on an intrinsically 3D interpretation. In this paper, our goal is to recover the occlusion boundaries and depth ordering of free-standing structures in the scene. Our approach is to learn to identify and label occlusion boundaries using the traditional edge and region cues together with 3D surface and depth cues. Since some of these cues require good spatial support (i.e., a segmentation), we gradually create larger regions and use them to improve inference over the boundaries. Our experi- ments demonstrate the power of a scene-based approach to occlusion reasoning.
Using generic interpolation machinery based on solving Poisson equations, a variety of novel tools are introduced for seamless editing of image regions. The first set of tools permits the seamless importation of both opaque and transparent source image regions into a destination region. The second set is based on similar mathematical ideas and allows the user to modify the appearance of the image seamlessly, within a selected region. These changes can be arranged to affect the texture, the illumination, and the color of objects lying in the region, or to make tileable a rectangular selection.
This article introduces a programmable approach to nonphotorealistic line drawings from 3D models, inspired by programmable shaders in traditional rendering. This approach relies on the assumption generally made in NPR that style attributes (color, thickness, etc.) are chosen depending on generic properties of the scene such as line characteristics or depth discontinuities, etc. We propose a new image creation model where all operations are controlled through user-defined procedures in which the relations between style attributes and scene properties are specified. A view map describing all relevant support lines in the drawing and their topological arrangement is first created from the D model so as to ensure the continuity of all scene properties along its edges; a number of style modules operate on this map, by procedurally selecting, chaining, or splitting lines, before creating strokes and assigning drawing attributes. Consistent access to properties of the scene is provided from the different elements of the map that are manipulated throughout the whole process. The resulting drawing system permits flexible control of all elements of drawing style: First, different style modules can be applied to different types of lines in a view; second, the topology and geometry of strokes are entirely controlled from the programmable modules; and third, stroke attributes are assigned procedurally and can be correlated at will with various scene or view properties. We illustrate the components of our system and show how style modules successfully encode stylized visual characteristics that can be applied across a wide range of models.
Good information design depends on clarifying the meaningful structure in an image. We describe a computational approach to stylizing and abstracting photographs that explicitly responds to this design goal. Our system transforms images into a line-drawing style using bold edges and large regions of constant color. To do this, it represents images as a hierarchical structure of parts and boundaries computed using state-of-the-art computer vision. Our system identifies the meaningful elements of this structure using a model of human perception and a record of a user's eye movements in looking at the photo; the system renders a new image using transformations that preserve and highlight these visual elements. Our method thus represents a new alternative for non-photorealistic rendering both in its visual style, in its approach to visual form, and in its techniques for interaction.
In this new edition of the successful book Level Set Methods, Professor
Sethian incorporates the most recent advances in Fast Marching Methods,
many of which appear here for the first time. Continuing the expository
style of the first edition, this introductory volume presents cutting
edge algorithms in these groundbreaking techniques and provides the
reader with a wealth of application areas for further study. Fresh
applications to computer-aided design and optimal control are explored
and studies of computer vision, fluid mechanics, geometry, and
semiconductor manufacture have been revised and updated. The text
includes over thirty new chapters. It will be an invaluable reference
for researchers and students.
This paper presents a database containing `ground truth' segmentations produced by humans for images of a wide variety of natural scenes. We define an error measure which quantifies the consistency between segmentations of differing granularities and find that different human segmentations of the same image are highly consistent. Use of this dataset is demonstrated in two applications: (1) evaluating the performance of segmentation algorithms and (2) measuring probability distributions associated with Gestalt grouping factors as well as statistics of image region properties
We present an interactive system for creating pen-and-ink-style line drawings from greyscale images in which the strokes of the rendered illustration follow the features of the original image. The user, via new interaction techniques for editing a direction field, specifies an orientation for each region of the image; the computer draws oriented strokes, based on a user-specified set of example strokes, that achieve the same tone as the image via a new algorithm that compares an adaptively-blurred version of the current illustration to the target tone image. By aligning the direction field with surface orientations of the objects in the image, the user can create textures that appear attached to those objects instead of merely conveying their darkness. The result is a more compelling pen-and-ink illustration than was previously possible from 2D reference imagery.
California: A Sketchbook
- E Cooper
EZ-Sketching: Three-Level Optimization for Error-Tolerant Image Tracing
- Q Su
Ez-sketching: Hierarchical optimization for error-tolerant image tracing
- Q Su
- W H Li
- J Wang
- H Fu
Q. Su, W. H. Li, J. Wang, and H. Fu, "Ez-sketching: Hierarchical optimization for error-tolerant image tracing," ACM Trans. Graph., vol. 21,
no. 3, 2014.
Poisson Image Editing
- pérez