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Journal ArticleDOI

LSD: A Fast Line Segment Detector with a False Detection Control

TL;DR: A linear-time line segment detector that gives accurate results, a controlled number of false detections, and requires no parameter tuning is proposed.
Abstract: We propose a linear-time line segment detector that gives accurate results, a controlled number of false detections, and requires no parameter tuning. This algorithm is tested and compared to state-of-the-art algorithms on a wide set of natural images.
Citations
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Journal ArticleDOI
TL;DR: LSD is a linear-time Line Segment Detector giving subpixel accurate results and uses an a contrario validation approach according to Desolneux, Moisan, and Morel’s theory.
Abstract: LSD is a linear-time Line Segment Detector giving subpixel accurate results. It is designed to work on any digital image without parameter tuning. It controls its own number of false detections: on average, one false alarm is allowed per image [1]. The method is based on Burns, Hanson, and Riseman’s method [2], and uses an a contrario validation approach according to Desolneux, Moisan, and Morel’s theory [3, 4]. The version described here includes some further

714 citations

Journal ArticleDOI
TL;DR: A linear time line segment detector that gives accurate results, requires no parameter tuning, and runs up to 11 times faster than the fastest known line segment detectors in the literature; hence the name EDLines.

382 citations


Cites background or methods from "LSD: A Fast Line Segment Detector w..."

  • ...These small segments are then eliminated during the validation step, and therefore, only a few detections are made (Grompone von Gioi et al., 2010)....

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  • ...Similar to Desolneux et al. (2000) and LSD (Grompone von Gioi et al., 2008b, 2010), our line validation method is based on the Helmholtz principle, which basically states that for a structure to be perceptually meaningful, the expectation of this structure (grouping or Gestalt) by chance must be…...

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  • ...…get errors of +1 and 1), and an angle tolerance of 22.5 (refer to Section 5.2), we compute the gradient threshold by the following formula given in Grompone von Gioi et al. (2010): q ¼ 2 sinð22:5Þ ¼ 5:22 Going over the gradient map and eliminating pixels with gradient values less than ‘‘q’’, we…...

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  • ...Following the discussion in Grompone von Gioi et al. (2010), we set ‘‘q’’ so as to leave out points where angle error is larger than the angle tolerance....

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  • ...Table 2 shows the dissection of the running times of EDLines, LSD (Grompone von Gioi et al., 2010) and KHT (Fernandes and Oliveira, 2008) on seven images given in Figs....

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Journal ArticleDOI
TL;DR: PL-SLAM is proposed, a stereo visual SLAM system that combines both points and line segments to work robustly in a wider variety of scenarios, particularly in those where point features are scarce or not well-distributed in the image.
Abstract: Traditional approaches to stereo visual simultaneous localization and mapping (SLAM) rely on point features to estimate the camera trajectory and build a map of the environment. In low-textured environments, though, it is often difficult to find a sufficient number of reliable point features and, as a consequence, the performance of such algorithms degrades. This paper proposes PL-SLAM, a stereo visual SLAM system that combines both points and line segments to work robustly in a wider variety of scenarios, particularly in those where point features are scarce or not well-distributed in the image. PL-SLAM leverages both points and line segments at all the instances of the process: visual odometry, keyframe selection, bundle adjustment, etc. We contribute also with a loop-closure procedure through a novel bag-of-words approach that exploits the combined descriptive power of the two kinds of features. Additionally, the resulting map is richer and more diverse in three-dimensional elements, which can be exploited to infer valuable, high-level scene structures, such as planes, empty spaces, ground plane, etc. (not addressed in this paper). Our proposal has been tested with several popular datasets (such as EuRoC or KITTI), and is compared with state-of-the-art methods such as ORB-SLAM2, revealing a more robust performance in most of the experiments while still running in real time. An open-source version of the PL-SLAM C++ code has been released for the benefit of the community.

329 citations


Cites methods from "LSD: A Fast Line Segment Detector w..."

  • ...Finally, by the time of the first submission of this paper, a work with the same name (PL-SLAM, [36]) was published extending the monocular algorithm ORB-SLAM to the case of including line segment features computed through the line segment detector (LSD) detector [37]....

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  • ...The LSD method [37] has been employed to extract line segments, providing high precision and repeatability....

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Proceedings ArticleDOI
01 Jul 2017
TL;DR: An improved version of the Simple Linear Iterative Clustering superpixel segmentation is presented, which is non-iterative, enforces connectivity from the start, requires lesser memory, and is faster than SLIC.
Abstract: We present an improved version of the Simple Linear Iterative Clustering (SLIC) superpixel segmentation. Unlike SLIC, our algorithm is non-iterative, enforces connectivity from the start, requires lesser memory, and is faster. Relying on the superpixel boundaries obtained using our algorithm, we also present a polygonal partitioning algorithm. We demonstrate that our superpixels as well as the polygonal partitioning are superior to the respective state-of-the-art algorithms on quantitative benchmarks.

280 citations


Cites methods from "LSD: A Fast Line Segment Detector w..."

  • ...The authors of CONPOLY detect preliminary line segments using Line Segment Detector [34]....

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Journal ArticleDOI
21 Jul 2013
TL;DR: This work uses human annotations and presents a new methodology for segmenting and annotating materials in Internet photo collections suitable for crowdsourcing (e.g., through Amazon's Mechanical Turk), and designs a multi-stage set of annotation tasks with quality checks and validation.
Abstract: The appearance of surfaces in real-world scenes is determined by the materials, textures, and context in which the surfaces appear. However, the datasets we have for visualizing and modeling rich surface appearance in context, in applications such as home remodeling, are quite limited. To help address this need, we present OpenSurfaces, a rich, labeled database consisting of thousands of examples of surfaces segmented from consumer photographs of interiors, and annotated with material parameters (reflectance, material names), texture information (surface normals, rectified textures), and contextual information (scene category, and object names).Retrieving usable surface information from uncalibrated Internet photo collections is challenging. We use human annotations and present a new methodology for segmenting and annotating materials in Internet photo collections suitable for crowdsourcing (e.g., through Amazon's Mechanical Turk). Because of the noise and variability inherent in Internet photos and novice annotators, designing this annotation engine was a key challenge; we present a multi-stage set of annotation tasks with quality checks and validation. We demonstrate the use of this database in proof-of-concept applications including surface retexturing and material and image browsing, and discuss future uses. OpenSurfaces is a public resource available at http://opensurfaces.cs.cornell.edu/.

238 citations


Cites methods from "LSD: A Fast Line Segment Detector w..."

  • ...We obtain VPs by finding line segments with LSD [von Gioi et al. 2010], then clustering the segments with J-Linkage [Toldo and Fusiello 2008], and solving for the optimal VP for each cluster [Tardif 2009; Feng et al. 2010]....

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References
More filters
Journal ArticleDOI
TL;DR: There is a natural uncertainty principle between detection and localization performance, which are the two main goals, and with this principle a single operator shape is derived which is optimal at any scale.
Abstract: This paper describes a computational approach to edge detection. The success of the approach depends on the definition of a comprehensive set of goals for the computation of edge points. These goals must be precise enough to delimit the desired behavior of the detector while making minimal assumptions about the form of the solution. We define detection and localization criteria for a class of edges, and present mathematical forms for these criteria as functionals on the operator impulse response. A third criterion is then added to ensure that the detector has only one response to a single edge. We use the criteria in numerical optimization to derive detectors for several common image features, including step edges. On specializing the analysis to step edges, we find that there is a natural uncertainty principle between detection and localization performance, which are the two main goals. With this principle we derive a single operator shape which is optimal at any scale. The optimal detector has a simple approximate implementation in which edges are marked at maxima in gradient magnitude of a Gaussian-smoothed image. We extend this simple detector using operators of several widths to cope with different signal-to-noise ratios in the image. We present a general method, called feature synthesis, for the fine-to-coarse integration of information from operators at different scales. Finally we show that step edge detector performance improves considerably as the operator point spread function is extended along the edge.

28,073 citations

Journal ArticleDOI
TL;DR: It is shown how the boundaries of an arbitrary non-analytic shape can be used to construct a mapping between image space and Hough transform space, which makes the generalized Houghtransform a kind of universal transform which can beused to find arbitrarily complex shapes.

4,310 citations

Journal ArticleDOI
TL;DR: The algorithm appears to be more effective than previous techniques for two key reasons: 1) the gradient orientation is used as the initial organizing criterion prior to the extraction of straight lines, and 2) the global context of the intensity variations associated with a straight line is determined prior to any local decisions about participating edge elements.
Abstract: This paper presents a new approach to the extraction of straight lines in intensity images. Pixels are grouped into line-support regions of similar gradient orientation, and then the structure of the associated intensity surface is used to determine the location and properties of the edge. The resulting regions and extracted edge parameters form a low-level representation of the intensity variations in the image that can be used for a variety of purposes. The algorithm appears to be more effective than previous techniques for two key reasons: 1) the gradient orientation (rather than gradient magnitude) is used as the initial organizing criterion prior to the extraction of straight lines, and 2) the global context of the intensity variations associated with a straight line is determined prior to any local decisions about participating edge elements.

742 citations


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Journal ArticleDOI
TL;DR: LSD is a linear-time Line Segment Detector giving subpixel accurate results and uses an a contrario validation approach according to Desolneux, Moisan, and Morel’s theory.
Abstract: LSD is a linear-time Line Segment Detector giving subpixel accurate results. It is designed to work on any digital image without parameter tuning. It controls its own number of false detections: on average, one false alarm is allowed per image [1]. The method is based on Burns, Hanson, and Riseman’s method [2], and uses an a contrario validation approach according to Desolneux, Moisan, and Morel’s theory [3, 4]. The version described here includes some further

714 citations


"LSD: A Fast Line Segment Detector w..." refers background in this paper

  • ...This procedure can lead to an erroneous line angle estimation when the background shows a slow intensity variation, see [ 20 ]....

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  • ...In some cases, this helps improving the search, see [ 20 ]....

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  • ...Multisegment detection produces good results (even if the parallel detections problem is still present and in some cases hallucinates global aligned structures not present, see [ 20 ])....

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  • ...See [ 20 ] for a deeper analysis of the experiments....

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  • ...See [ 20 ]. However, this experiment made with strong artificial noise does not imply that a multiscale theory for line segment detection is necessary....

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Journal ArticleDOI
TL;DR: The progressive probabilistic Hough transform minimizes the amount of computation needed to detect lines by exploiting the difference in the fraction of votes needed to reliably detect lines with different numbers of supporting points.

604 citations


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