Color by Numbers: Interactive Structuring and Vectorization of Sketch Imagery

Parakkat, Amal Dev; Cani, Marie‐Paule; Singh, Karan

doi:10.1145/3411764.3445215

Cited by 12 publications

(18 citation statements)

References 34 publications

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“…Vectorization Sketch vectorization is a widely studied topic for well-drawn pencil sketches (particularly pencil-andpaper scanned sketches) [142], [155], [162]- [167]; with a few studies begining to address it for free-hand sketches [56], [115], [130]. It aims to generate vector representations for raster sketch photographs.…”

Section: Sketch Perceptual Grouping (Spg)mentioning

confidence: 99%

Deep Learning for Free-Hand Sketch: A Survey

Xu¹,

Hospedales²,

Yin³

et al. 2023

IEEE Trans. Pattern Anal. Mach. Intell.

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Free-hand sketches are highly illustrative, and have been widely used by humans to depict objects or stories from ancient times to the present. The recent prevalence of touchscreen devices has made sketch creation a much easier task than ever and consequently made sketch-oriented applications increasingly popular. The progress of deep learning has immensely benefited free-hand sketch research and applications. This paper presents a comprehensive survey of the deep learning techniques oriented at free-hand sketch data, and the applications that they enable. The main contents of this survey include: (i) A discussion of the intrinsic traits and unique challenges of free-hand sketch, to highlight the essential differences between sketch data and other data modalities, e.g., natural photos. (ii) A review of the developments of free-hand sketch research in the deep learning era, by surveying existing datasets, research topics, and the state-of-the-art methods through a detailed taxonomy and experimental evaluation. (iii) Promotion of future work via a discussion of bottlenecks, open problems, and potential research directions for the community.

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Section: Sketch Perceptual Grouping (Spg)mentioning

confidence: 99%

Deep Learning for Free-Hand Sketch: A Survey

Xu¹,

Hospedales²,

Yin³

et al. 2023

IEEE Trans. Pattern Anal. Mach. Intell.

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“…Recent years, a few works develop deep learning based methods to automatically generate vector graphics for computer aided design or converting raster graphics to vector graphcis (i.e. vectorization) [30,31,32,33,34,35], while to the best of knowledge, our paper is the first to focus on recognition task on vector graphics. Koch et al [36] proposes a large 3D model dataset containing analytic representations, but it lacks semantic labeling to train recognition model.…”

Section: Related Workmentioning

confidence: 99%

Recognizing Vector Graphics without Rasterization

Jiang

Liu

Shan

et al. 2021

Preprint

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In this paper, we consider a different data format for images: vector graphics. In contrast to raster graphics which are widely used in image recognition, vector graphics can be scaled up or down into any resolution without aliasing or information loss, due to the analytic representation of the primitives in the document. Furthermore, vector graphics are able to give extra structural information on how low-level elements group together to form high level shapes or structures. These merits of graphic vectors have not been fully leveraged in existing methods. To explore this data format, we target on the fundamental recognition tasks: object localization and classification. We propose an efficient CNN-free pipeline that does not render the graphic into pixels (i.e. rasterization), and takes textual document of the vector graphics as input, called YOLaT (You Only Look at Text). YOLaT builds multi-graphs to model the structural and spatial information in vector graphics, and a dual-stream graph neural network is proposed to detect objects from the graph. Our experiments show that by directly operating on vector graphics, YOLaT outperforms raster-graphic based object detection baselines in terms of both average precision and efficiency.

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“…To achieve the first point, our method uses an intuitive flow-based colour propagation process on the underlying Delaunay triangulation [PCS21], making it as simple to use as Flood-fill. Second, instead of reconnecting gaps in the contours with straight lines, which would affect the aesthetic beauty of the final result, we make use of local contour information around gaps for generating the border of coloured regions.…”

Section: Introductionmentioning

confidence: 99%

“…In summary, while our method reuses the flow mechanism from Parakkat et al [PCS21] in the new context of colouring raster contour images with minimal user inputs, it also makes new technical contributions:…”

Section: Introductionmentioning

confidence: 99%

“…In summary, while our method reuses the flow mechanism from Parakkat et al . [PCS21] in the new context of colouring raster contour images with minimal user inputs, it also makes new technical contributions: A colour diffusion mechanism to minimize user efforts by propagating colours to uncoloured small regions (for examples hatches) An aesthetic curve completion technique to fill gaps in the input contours in a visually pleasing way An automatic colouring algorithm with possible applications in segmentation of medical images and sketches …”

Section: Introductionmentioning

confidence: 99%

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Delaunay Painting: Perceptual Image Colouring from Raster Contours with Gaps

Parakkat

Memari

Cani

2022

Computer Graphics Forum

Self Cite

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We introduce Delaunay Painting, a novel and easy-to-use method to flat-colour contour-sketches with gaps. Starting from a Delaunay triangulation of the input contours, triangles are iteratively filled with the appropriate colours, thanks to the dynamic update of flow values calculated from colour hints. Aesthetic finish is then achieved, through energy minimisation of contourcurves and further heuristics enforcing the appropriate sharp corners. To be more efficient, the user can also make use of our colour diffusion framework, which automatically extends colouring to small, internal regions such as those delimited by hatches. The resulting method robustly handles input contours with strong gaps. As an interactive tool, it minimizes user's efforts and enables any colouring strategy, as the result does not depend on the order of interactions. We also provide an automatized version of the colouring strategy for quick segmentation of contours images, that we illustrate with applications to medical imaging and sketch segmentation.

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Color by Numbers: Interactive Structuring and Vectorization of Sketch Imagery

Cited by 12 publications

References 34 publications

Deep Learning for Free-Hand Sketch: A Survey

Deep Learning for Free-Hand Sketch: A Survey

Recognizing Vector Graphics without Rasterization

Delaunay Painting: Perceptual Image Colouring from Raster Contours with Gaps

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