coherent line drawing

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Coherent Line Drawing 논논 논논논 논논논논 논논논 논논논 2008.5.22 1

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Coherent Line Drawing. 논문 세미나 그래픽스 연구실 윤종철 2008.5.22. 목차. Abstract 1. Introduction 1.1 Related work 1.2 Contribution and Overview 2. Flow construction 2.1 Edge Tangent Flow 3. Line construction 3.1 Flow-based Difference-of-Gaussians 3.2 Iterative FDoG filtering 4 . Results - PowerPoint PPT Presentation

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Coherent Line Drawing

논문 세미나그래픽스 연구실 윤종철

2008.5.22

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목차Abstract1. Introduction

1.1 Related work1.2 Contribution and Overview

2. Flow construction2.1 Edge Tangent Flow

3. Line construction3.1 Flow-based Difference-of-Gaussians3.2 Iterative FDoG filtering

4. Results5. Discussion and Future work

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Abstract

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AbstractImage 로부터 automatically Line 을 drawing 하는 NPR technique 제안

Coherent, smooth, stylistic line 에 초점

Noise 는 억제하고 , highly co-herent line 을 찾는 flow-guided anisotropic filtering 소개

간단하고 구현이 쉬운 method

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1. Introduction

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1. IntroductionLine drawing 은 prehistoric ages 로부터 visual communica-tion 의 the simplest, oldest임이 틀림없다 .

Line drawing 은 minimal amount of data 를 사용하고 , ob-ject shape 을 효율적으로 나타낼 수 있음

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1. IntroductionObject surface 의 tonal infor-mation 이 아닌 shape 을 그리는 Black-and-white line drawing에 초점

Image 로부터 line 을 그리는 Au-tomatic technique

Clean, smooth, coherent, stylistic line

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1. IntroductionFlow-driven anisotropic filter-ing framework 가 main con-tribution

Edge detection filter 를 변형하여 flow 에 의해 정의된 an-isotropic kernel 에 적용

Noise 억제

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1.1 Related workNPR community 에서 , 3D model 의 line 을 그리는 meth-ods

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Coherent Stylized Sil-houetees [Kalnins et al. 2003]

Suggestive Contours for Conveying Shape [De-Carlo et al. 2003]

A Few Good Lines: Suggestive Drawing of 3D ModelsSousa and Prusinkiewicz 2003]

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1.1 Related work순수한 line drawing 보다

부분적으로 사용 ex) color, tone, material etc.

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Interactive pen-and-ink illustration [Sal-isbury et al. 1994]

Processing images and video for an im-pressionist effect [L-itwinowicz 1997]

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1.1 Related workPhotograph tooning 같은 NPR style 은 explicit display of line을 요구

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Stylization and Abstraction of Photographs [DeCarlo and Santella 2002]

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1.1 Related work

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Real-time video abstraction [Winnemoller et al. 2006]

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1.2 contribution and Over-view

기술적인 contribution 2 가지◦ feature-preserving local edge flow(edge

tangent flow 라고 불리는 ), Kernel-based nonlinear vector smoothing technique 개발

◦ Line illustration 을 그리는 Flow-based aniso-tropic DoG filtering technique 제안

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1.2 contribution and Over-view

Advantages◦ Line coherence:

kernel size 조정으로 isolated edge point 의 set으로부터 line drawing 가능

◦ Robustness: noise 억제 spurious line 줄임

◦ Quality: good◦ Simplicity: 구현 쉬움◦ Generality:

flow-based filtering framework 가 general. Fea-ture preservation term 에서 다른 filter 사용가능

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2. Flow construction

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2.1 Edge Tangent FlowHigh-quality line drawing 을

위해 vector field 는 다음 요구를 만족해야◦ Vector flow must describe the salient edge

tangent direction in the neighborhood◦ Neighboring vectors must be smoothly

aligned except at sharp corners◦ Important edges must retain their original

directions

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2.1 Edge Tangent Flow

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2.1 Edge Tangent Flow각 pixel-centered kernel 에서 , nonlinear vector smoothing 을 실행◦ 두드러진 edge direction 은 보존 , 약한 edge

는 이웃의 지배적인 direction 을 따르게 .◦ Sharp corners 보존하고 원하지 않는 swirling

artifact 를 피하기 위해 similar orientation 의 edge 에 smoothing 을 장려 .

◦ 강하지만 관계없는 vector 에 영향을 받는 약한 vector 를 예방

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2.1 Edge Tangent Flow

X : (x, y) image pixel I(x) : input image : Neighborhood of x k : vector normalizing term t(x) : edge tangent: a vector perpendicular

to the image gradient

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2.1 Edge Tangent Flow

For the spatial weight function Ws, radially-symmetric box filter of radius r, where r is the ra-dius of the kernel :

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2.1 Edge Tangent Flow

The other two weight functions, Wm and Wd, play the key role in feature preserving.

Wm : magnitude weight function denotes the normalized gradient magnitude

at z, and controls the fall-off rate

Wd : direction weight function

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2.1 Edge Tangent Flow

denotes the ‘current’ normalized tangent vector at y

Sign function This induces tighter alignment of vectors while

avoiding swirling flows

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2.1 Edge Tangent Flow◦ t(x) 는 initial gradient map of the input im-

age I 로부터 perpendicular vector 를 구해서 얻음

◦ t(x) 는 normalize 된 후 사용◦ Initial gradient map 은 Sobel

operator(appendix 참고 ) 로 계산◦ Our filter 는 ETF 를 update 하기 위해 itera-

tively 제공할 수 있음 :◦ g(x) 도 따라서 update 됨 (gradient magni-

tude 는 변하지 않음 )◦ 본 논문에서는 2~3 번 update 했음

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Appendix : Sobel oper-ator

Mathematically, the operator uses two 3×3 kernels which are convolved with the original image to calculate approximations of the derivatives - one for horizontal changes, and one for verti-cal. If we define A as the source image, and Gx and Gy are two images which at each point contain the horizontal and vertical derivative approximations, the computations are as follow:

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2.2 Discussion

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3. Line construction

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3.1 Flow-based Difference-of-Gaussians

◦ 방정식 1 에 의해 만들어진 local flow 에 의해 모양이 정의된 커널을 사용하는 flow-guided anisotropic DoG filter 를 제공

◦ t(x) 는 local edge 방향을 나타내고 이것은 gradient 의 수직방향에서 highest contrast 를 가질 가능성이 높을 것이라는 것을 의미

◦ 이 idea 는 edge flow 를 따라서 이 gradient direction 에 linear DoG filter 를 제공하는 것

◦ flow 를 따라 filter 의 반응을 누적

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3.1 Flow-based Difference-of-Gaussians

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3.1 Flow-based Difference-of-Gaussians

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3.1 Flow-based Difference-of-Gaussians

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3.1 Flow-based Difference-of-Gaussians

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3.1 Flow-based Difference-of-Gaussians

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3.2 Iterative FDoG fil-tering

FDoG 에서 파라미터 변경하는 것보다 iterative FDoG filtering은 line coherence 를 향상에 종종 더 효과

원본 이미지에 (10) 에서 얻은 이미지 중첩시키고 다시 FDoG fil-ter 사용

만족할 때까지 반복FDoG filter 사용 전에 Gauss-ian-blur 쓰면 더 smooth 해짐

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초기에 disconnected compo-nent 는 connect 됨

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4. Results

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4. Results

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4. Results

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4. Results(Bonus)

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5. Discussion and Future work

DoG filter 기반인 우리의 FDoG filter 는 몇몇 limitation 공유

high-contrast background 일 때 , 비록 이 area 가 지각에 의해 중요하지 않아도 line 의 빽빽한 집합으로 채워짐

well-defined strokes 보다는 line 이 픽셀 집합처럼 형성

isolated edge segments 에 FDoG filter 유용 , but여전히 local kernel 상에서 작동하기 때문에 global scale subjective contour 는 찾기 어려움

future work 가속

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