Robust and accurate vectorization of line drawings

Hilaire, Xavier; Tombre, Karl

doi:10.1109/tpami.2006.127

Cited by 144 publications

(87 citation statements)

References 52 publications

(42 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Thus, using segments is more efficient than using points. Although primitives or salient points of contours are commonly utilised in their representation [5], [20], [31], the associated computation is relatively expensive and as there had been considerable research using "fragmented" contour segments [57], we decided to use this approach. We first divide a contour into a set of equal-length segments and then encode the spatial distributions and orientations of these segments.…”

Section: ) Dividing a Contour Into Segmentsmentioning

confidence: 99%

Perceptually Motivated Image Features Using Contours

Dong

Chantler

2016

IEEE Trans. on Image Process.

View full text Add to dashboard Cite

Abstract-Dong et al. examined the ability of 51 computational feature sets to estimate human perceptual texture similarity, however, none performed well for this task. While it is well-known that the human visual system is extremely adept at exploiting longer-range aperiodic (and periodic) "contour" characteristics in images, none of the investigated feature sets exploit higher order statistics (HOS) over larger image regions (>19×19 pixels). We therefore hypothesise that long-range HOS, in the form of contour data, are useful for perceptual texture similarity estimation.We present the results of a psychophysical experiment that shows that contour data are more important, than local image patches, or global 2nd-order data, to human observers for this task.Inspired by this finding, we propose a set of perceptually motivated image features (PMIF) that encode the long-range HOS computed from spatial and angular distributions of contour segments. We use two perceptual texture similarity estimation tasks to compare PMIF against the 51 feature sets referred to above and four commonly used contour representations. This new feature set is also examined in the context of two additional tasks: sketch-based image retrieval and natural scene recognition. The results show that the proposed feature set performs better, or at least comparably to, all the other feature sets. We attribute this promising performance to the fact that the proposed feature set exploits both short-range and long-range HOS.

show abstract

Section: ) Dividing a Contour Into Segmentsmentioning

confidence: 99%

Perceptually Motivated Image Features Using Contours

Dong

Chantler

2016

IEEE Trans. on Image Process.

View full text Add to dashboard Cite

show abstract

“…Hilaire and Tombre [31] proposed a systematical way to vectorize the line drawings. Among their proposed algorithms, one was for arc and circle.…”

Section: Incorporating Different Sampling Strategiesmentioning

confidence: 99%

Circle Detection Based on Voting for Maximum Compatibility

Liu

Huang

2012

IEICE Trans. Inf. & Syst.

View full text Add to dashboard Cite

SUMMARYWe present a fast voting scheme for localizing circular objects among clutter and occlusion. Typical solutions for the problem are based on Hough transform that evaluates an instance of circle by counting the number of edge points along its boundary. The evaluated value is proportional to radius, making the normalization with respect to the factor necessary for detecting circles with different radii. By representing circle with a number of sampled points, we get rid of the step. To evaluate an instance then involves obtaining the same number of edge points, each close to a sampled point in both spatial position and orientation. The closeness is measured by compatibility function, where a truncating operation is used to suppress noise and deal with occlusion. To evaluate all instances of circle is fulfilled by letting edge point vote in a maximizing way such that any instance possesses a set of maximally compatible edge points. The voting process is further separated into the radius-independent anddependent parts. The time-consuming independent part can be shared by different radii and outputs the sparse matrices. The radius-dependent part shifts these sparse matrices according to the radius. We present precisionrecall curves showing that the proposed approach outperforms the solutions based on Hough transform, at the same time, achieves the comparable time complexity as algorithm of Hough transform using 2D accumulator array.

show abstract

“…This has a drawback, compared to [5], for instance, since we cannot account for the exact positioning of the estimated circle within the line thickness. This is further analyzed in section 6.…”

Section: Data Points and Selection Criteriamentioning

confidence: 99%

Robust Circle Detection

Gaucher

Fritz

2007

Ninth International Conference on Document Analysis and Recognition (ICDAR 2007)

View full text Add to dashboard Cite

In this paper we present a method of robustly detect circles in a line drawing image. The method is fast, robust and very reliable, and is capable of assessing the quality of its detection. It is based on Random Sample Consensus minimization, and uses techniques that are inspired from object tracking in image sequences.Note : some details of the illustrations in this paper are in colour. Reading them on a greylevel printout will reduce their intelligibility.

show abstract

Robust and accurate vectorization of line drawings

Cited by 144 publications

References 52 publications

Perceptually Motivated Image Features Using Contours

Perceptually Motivated Image Features Using Contours

Circle Detection Based on Voting for Maximum Compatibility

Robust Circle Detection

Contact Info

Product

Resources

About