Rapid Automated Polygonal Image Decomposition

Swaminarayan, Sriram; Prasad, Lakshman

doi:10.1109/aipr.2006.30

Cited by 26 publications

(14 citation statements)

References 1 publication

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“…Lecot and Levy [2006] decompose images into a set of regions delimited by cubic splines, each with their own precomputed color gradients. Swaminarayan and Prasad [2006] placed triangle primitives along edges in images and sampled the triangle colors from the original image, resulting in an artistic abstraction of the input. Demaret et al [2006] combined an adaptive triangulation with a first-order spline in order to more accurately preserve input image details, specifically for image compression.…”

Section: Related Workmentioning

confidence: 99%

Hierarchical diffusion curves for accurate automatic image vectorization

et al. 2014

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Figure 1:We are able to accurately vectorize raster images ("Input") by automatically tracing a sparse set of multi-scale diffusion curves to a broad class of images, including vector art (i.e., diffusion curve images), fluid simulations with turbulent flows, and natural images. AbstractDiffusion curve primitives are a compact and powerful representation for vector images. While several vector image authoring tools leverage these representations, automatically and accurately vectorizing arbitrary raster images using diffusion curves remains a difficult problem. We automatically generate sparse diffusion curve vectorizations of raster images by fitting curves in the Laplacian domain. Our approach is fast, combines Laplacian and bilaplacian diffusion curve representations, and generates a hierarchical representation that accurately reconstructs both vector art and natural images. The key idea of our method is to trace curves in the Laplacian domain, which captures both sharp and smooth image features, across scales, more robustly than previous image-and gradientdomain fitting strategies. The sparse set of curves generated by our method accurately reconstructs images and often closely matches tediously hand-authored curve data. Also, our hierarchical curves are readily usable in all existing editing frameworks. We validate our method on a broad class of images, including natural images, synthesized images with turbulent multi-scale details, and traditional vector-art, as well as illustrating simple multi-scale abstraction and color editing results.

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Section: Related Workmentioning

confidence: 99%

Hierarchical diffusion curves for accurate automatic image vectorization

et al. 2014

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“…The information is represented in the Scalable Vector Graphics (SVG) format which allows a very compact encoding of the vector data. A related approach [7] that is used in the RaveGrid 978-1-4244-7493-6/10/$26.00 c 2010 IEEE ICME 2010 1124 software [8] is the use of the Delaunay triangulation that fits triangles to image regions. The limitation of this method (and of [4]) is that a large amount of vector data (triangles or polygons) is required for a faithful reconstruction of the original image.…”

Section: Related Workmentioning

confidence: 99%

A vector-based approach to digital image scaling

Velusamy

Bopardikar

Radhika

et al. 2010

2010 IEEE International Conference on Multimedia and Expo

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Image and video capture devices are often designed with different capabilities and end-users in mind. As a result, very often images are captured at one resolution and need to be displayed at another resolution. Resizing thus continues to be an important research area in today's milieu. Well-known pixelbased methods (like bicubic interpolation) have traditionally been used for the purpose. However, these methods are often found lacking in quality from a perceptual standpoint as they tend to soften edges and blur details. Recently, vectorization based approaches have been explored because of their inherent property of artifact free scaling. However, these methods fail to faithfully reproduce textures and fine details in an image. In this work, we present a novel, layered approach of combining image vectorization with a pixel based interpolation technique to achieve high quality scaling of digital images. The proposed method decomposes an image into two layers: a 'coarse' layer capturing the visually important structure of the image, and a 'fine' layer containing the details. We vectorize only the coarse layer and render at the desired scale, while using classical interpolation on the fine layer. The final scaled image is composed by blending the independently scaled layers. We compare the performance of the proposed method with several state-of-theart vectorization and scaling methods, and report comparably better performance.

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“…Existing vectorization techniques for full-color raster images fall roughly into three categories. Triangulations A few algorithms have been proposed based on constrained Delaunay triangulation [16], [22], [29]. In triangulation-based representations, each curvilinear feature needs to be approximated by many short line segments.…”

Section: Related Workmentioning

confidence: 99%

A Subdivision-Based Representation for Vector Image Editing

Liao

Hoppe

Forsyth

et al. 2012

IEEE Trans. Visual. Comput. Graphics

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Abstract-Vector graphics has been employed in a wide variety of applications due to its scalability and editability. Editability is a high priority for artists and designers who wish to produce vector-based graphical content with user interaction. In this paper, we introduce a new vector image representation based on piecewise smooth subdivision surfaces, which is a simple, unified and flexible framework that supports a variety of operations, including shape editing, color editing, image stylization, and vector image processing. These operations effectively create novel vector graphics by reusing and altering existing image vectorization results. Because image vectorization yields an abstraction of the original raster image, controlling the level of detail of this abstraction is highly desirable. To this end, we design a feature-oriented vector image pyramid that offers multiple levels of abstraction simultaneously. Our new vector image representation can be rasterized efficiently using GPU-accelerated subdivision. Experiments indicate that our vector image representation achieves high visual quality and better supports editing operations than existing representations.

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Rapid Automated Polygonal Image Decomposition

Cited by 26 publications

References 1 publication

Hierarchical diffusion curves for accurate automatic image vectorization

Hierarchical diffusion curves for accurate automatic image vectorization

A vector-based approach to digital image scaling

A Subdivision-Based Representation for Vector Image Editing

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