Beautification of Design Sketches Using Trainable Stroke Clustering and Curve Fitting

Orbay, Gunay; Kara, Levent Burak

doi:10.1109/tvcg.2010.105

Cited by 55 publications

(54 citation statements)

References 29 publications

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“…Our work is also related to the previous interactive or automatic beautification systems [Igarashi et al 1997;Orbay and Kara 2011;Zitnick 2013], none of which, however, uses a picture to guide the beautification process. We do not regard our technique as a beautification system, since drawing is a creative process and we try to preserve the original drawing style of the user.…”

Section: Related Workmentioning

confidence: 99%

EZ-sketching

Wang

et al. 2014

ACM Trans. Graph.

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a b c e f d Figure 1: The proposed system automatically refines sketch lines (a, c, e) (created by different users) roughly traced over a single image in a three-level optimization framework. The refined sketches (b, d, f) show closer resemblance to the traced images and are often aesthetically more pleasing, as confirmed by the user study. Image credits: AbstractWe present a new image-guided drawing interface called EZSketching, 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.

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Section: Related Workmentioning

confidence: 99%

EZ-sketching

Wang

et al. 2014

ACM Trans. Graph.

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“…(5) is a slight modification of the discontinuity term introduced in the stage of stroke clustering in Ref. [18]. However, in some cases, two geometrically connected line segments may actually come from two semantically different objects.…”

Section: Coherence Line Matchingmentioning

confidence: 99%

User-guided line abstraction using coherence and structure analysis

Tsai

Lee

et al. 2017

Comp. Visual Media

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Line drawing is a style of image abstraction where the perceptual content of the image is conveyed using distinct straight or curved lines. However, extracting semantically salient lines is not trivial and mastered only by skilled artists. While many parametric filters have successfully extracted accurate and coherent lines, their results are sensitive to parameter choice and easily lead to either an excessive or insufficient number of lines. In this work, we present an interactive system to generate concise line abstractions of arbitrary images via a few user specified strokes. Specifically, the user simply has to provide a few intuitive strokes on the input images, including tracing roughly along edges and scribbling on the region of interest, through a sketching interface. The system then automatically extracts lines that are long, coherent and share similar textural structures to form a corresponding highly detailed line drawing. We have tested our system with a wide variety of images. Our experimental results show that our system outperforms state-of-the-art techniques in terms of quality and efficiency.

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“…While vectorization algorithms could be used to convert bitmaps into vectorial curves, state-of-the-art algorithms remain challenged by sketchy drawings [Noris et al 2013], or require the temporal information provided by digital sketching [Orbay and Kara 2011]. When applied on a rough sketch, the recent method by Noris et al [2013] produces multiple curves in the presence of overlapping strokes (Figure 3(a)(b)).…”

Section: Related Workmentioning

confidence: 99%

BendFields

2015

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and ADRIEN BOUSSEAU INRIADesigners frequently draw curvature lines to convey bending of smooth surfaces in concept sketches. We present a method to extrapolate curvature lines in a rough concept sketch, recovering the intended 3D curvature field and surface normal at each pixel of the sketch. This 3D information allows to enrich the sketch with 3D-looking shading and texturing.We first introduce the concept of regularized curvature lines that model the lines designers draw over curved surfaces, encompassing curvature lines and their extension as geodesics over flat or umbilical regions. We build on this concept to define the orthogonal cross field that assigns two regularized curvature lines to each point of a 3D surface. Our algorithm first estimates the projection of this cross field in the drawing, which is nonorthogonal due to foreshortening. We formulate this estimation as a scattered interpolation of the strokes drawn in the sketch, which makes our method robust to sketchy lines that are typical for design sketches. Our interpolation relies on a novel smoothness energy that we derive from our definition of regularized curvature lines. Optimizing this energy subject to the stroke constraints produces a dense nonorthogonal 2D cross field which we then lift to 3D by imposing orthogonality. Thus, one central concept of our approach is the generalization of existing cross field algorithms to the nonorthogonal case.We demonstrate our algorithm on a variety of concept sketches with various levels of sketchiness. We also compare our approach with existing work that takes clean vector drawings as input. Fig. 1. Designers commonly draw curvature lines to emphasize surface bending in concept sketches ((a), red lines). We extrapolate these lines in a bitmap to form a dense cross field, from which we estimate the 3D curvature directions and the surface normal at each pixel ((b)(c)). We use this information to compute shading, greatly enhancing the 3D look of the sketch (d). Note that this sketch is composed of several layers to represent the main body, handle, and nozzle of the vacuum cleaner.

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Beautification of Design Sketches Using Trainable Stroke Clustering and Curve Fitting

Cited by 55 publications

References 29 publications

EZ-sketching

EZ-sketching

User-guided line abstraction using coherence and structure analysis

BendFields

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