Adaptive Vectorization of Line Drawing Images

Janssen, Rik D. T.; Vossepoel, Albert M.

doi:10.1006/cviu.1996.0484

Cited by 64 publications

(27 citation statements)

References 22 publications

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“…Prominent approaches are based on tracing [Freeman 1974], thinning [Lam et al 1992], or methods utilizing contours or projections such as the Hough transform [Liu and Dori 1998]. Due to the focus on technical images, many of these methods are restricted to fitting straight line segments to input drawings [Janssen and Vossepoel 1997]. Exceptions include the method by Chang and Yan [1998], which fits Bezier curves, and the method by Zou and Yan [2001], which focuses on issues such as jaggy line boundaries and junction points.…”

Section: Related Workmentioning

confidence: 99%

Topology-driven vectorization of clean line drawings

et al. 2013

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Vectorization provides a link between raster scans of pencil-andpaper drawings and modern digital processing algorithms that require accurate vector representations. Even when input drawings are comprised of clean, crisp lines, inherent ambiguities near junctions make vectorization deceptively difficult. As a consequence, current vectorization approaches often fail to faithfully capture the junctions of drawn strokes. We propose a vectorization algorithm specialized for clean line drawings that analyzes the drawing's topology in order to overcome junction ambiguities. A gradientbased pixel clustering technique facilitates topology computation. This topological information is exploited during centerline extraction by a new "reverse drawing" procedure that reconstructs all possible drawing states prior to the creation of a junction and then selects the most likely stroke configuration. For cases where the automatic result does not match the artist's interpretation, our drawing analysis enables an efficient user interface to easily adjust the junction location. We demonstrate results on professional examples and evaluate the vectorization quality with quantitative comparison to hand-traced centerlines as well as the results of leading commercial algorithms.

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Section: Related Workmentioning

confidence: 99%

Topology-driven vectorization of clean line drawings

et al. 2013

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“…There exists extensive previous work on vectorization of non-photographic images [17], [18], [31], [32], which include fonts, clip arts, maps and line drawings. However, in this paper we focus on photographic images.…”

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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“…The basic idea is to search for a set of low-level geometrical features in rasterized line engineering drawings using pixel-trackingbased [9], [10], thinning-based [42], contour-based [43], or run-graph-based [44] methods. Some recent methods improve both the accuracy and robustness [6], [7], [11], [22], [23].…”

Section: Related Workmentioning

confidence: 99%

A Novel Knowledge-Based System for Interpreting Complex Engineering Drawings: Theory, Representation, and Implementation

Lü

Tai

Yang

et al. 2009

IEEE Trans. Pattern Anal. Mach. Intell.

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Abstract-We present a novel knowledge-based system to automatically convert real-life engineering drawings to content-oriented high-level descriptions. The proposed method essentially turns the complex interpretation process into two parts: knowledge representation and knowledge-based interpretation. We propose a new hierarchical descriptor-based knowledge representation method to organize the various types of engineering objects and their complex high-level relations. The descriptors are defined using an Extended Backus Naur Form (EBNF), facilitating modification and maintenance. When interpreting a set of related engineering drawings, the knowledge-based interpretation system first constructs an EBNF-tree from the knowledge representation file, then searches for potential engineering objects guided by a depth-first order of the nodes in the EBNF-tree. Experimental results and comparisons with other interpretation systems demonstrate that our knowledge-based system is accurate and robust for high-level interpretation of complex real-life engineering projects.

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Adaptive Vectorization of Line Drawing Images

Cited by 64 publications

References 22 publications

Topology-driven vectorization of clean line drawings

Topology-driven vectorization of clean line drawings

A Subdivision-Based Representation for Vector Image Editing

A Novel Knowledge-Based System for Interpreting Complex Engineering Drawings: Theory, Representation, and Implementation

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