Object Contour and Edge Detection with RefineContourNet

Kelm, André Peter; Rao, Vijesh Soorya; Zölzer, Udo

doi:10.1007/978-3-030-29888-3_20

Cited by 38 publications

(22 citation statements)

References 31 publications

(37 reference statements)

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“…This section gives evaluation indicators and complete performance evaluation results. Regardless of the presence or absence of noise, the aggregate precision-recall curve (PR) [26] and experimentally based figure of merit (FOM) [27] are used to compare and evaluate the proposed method with eight state-of-the-art methods [10], [20]- [24], [28], [29]. The experiment of edge detection exactness and noise robustness is completed in three data sets: the BSDS500 data set, the NYUDv2 data set, and the challenging PASCAL VOC 2007 data set [30]- [32].…”

Section: Experimental Configurationmentioning

confidence: 99%

“…Its advantage is that the detection speed is fast, but the extracted features and details are not rich enough. Kelm et al [20] analyzed the reasons for the fast structure forest method and proposed a multipath refined CNN model based on the holistically nested edge detection method [17]. The model is used to obtain better contours, but the amount of calculation is large.…”

Section: Introductionmentioning

confidence: 99%

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Multiscale Anisotropic Morphological Directional Derivatives for Noise-Robust Image Edge Detection

Wang

Liu

et al. 2022

IEEE Access

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Different types of noise interference lead to low accuracy of image edge detection and severe loss of feature extraction details. A new noise-robust edge detection method is proposed, which uses a set of multiscale anisotropic morphological directional derivatives to extract the edge map of an input image. The main advantage of the method is that high edge resolution is maintained while reducing noise interference. The following five parts form the whole framework of this paper. First, multiscale anisotropic morphologic directional derivatives (MSAMDDs) are proposed to filter and obtain the local gray value of the image. Second, the edge strength map (ESM) is extracted by using spatial matching filters. In the third stage, multiscale edge direction maps (EDMs) based on the directional matched filters are fused, and the new EDM is constructed. Fourth, edge contours are obtained by embedding the ESM and the EDM into the standard route of Canny detection. Finally, the precision-recall curve and Pratt's figure of merit (FOM) are used to evaluate the proposed method against eight state-of-the-art methods on three data sets. The experimental results show that the proposed method can perform better for noise-free (F-measure value of 0.776) and Gaussian noise (FOM value of 95.75%) and attains the best performance in impulse noise images (highest FOM value of 98.90%).INDEX TERMS Edge detection, anisotropic morphological directional derivatives, multiscale, ground truth.

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Section: Experimental Configurationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Multiscale Anisotropic Morphological Directional Derivatives for Noise-Robust Image Edge Detection

Wang

Liu

et al. 2022

IEEE Access

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“…Recent studies on edge detection using deep learning techniques focus more on natural images [14][15][16][17][18][19] and remote sensing images [20]. Resulting edges are often thick and noisy and require post processing before thinned and sharp boundaries are obtained [21].…”

Section: Related Workmentioning

confidence: 99%

“…• The last point is not assigned as first or last point of a longer segment. [0, 1,190,191,192,193,194,195,196,197,198,199,200,201,202] [ [8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23] A segment is then identified and selected as a list containing a minimum number of points minL. The minimum segment length is adjusted according to the output requirements.…”

Section: Segment Detection and Filteringmentioning

confidence: 99%

Automatic Building Outline Extraction from ALS Point Clouds by Ordered Points Aided Hough Transform

Widyaningrum

Gorte²,

Lindenbergh

2019

Remote Sensing

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Many urban applications require building polygons as input. However, manual extraction from point cloud data is time- and labor-intensive. Hough transform is a well-known procedure to extract line features. Unfortunately, current Hough-based approaches lack flexibility to effectively extract outlines from arbitrary buildings. We found that available point order information is actually never used. Using ordered building edge points allows us to present a novel ordered points–aided Hough Transform (OHT) for extracting high quality building outlines from an airborne LiDAR point cloud. First, a Hough accumulator matrix is constructed based on a voting scheme in parametric line space (θ, r). The variance of angles in each column is used to determine dominant building directions. We propose a hierarchical filtering and clustering approach to obtain accurate line based on detected hotspots and ordered points. An Ordered Point List matrix consisting of ordered building edge points enables the detection of line segments of arbitrary direction, resulting in high-quality building roof polygons. We tested our method on three different datasets of different characteristics: one new dataset in Makassar, Indonesia, and two benchmark datasets in Vaihingen, Germany. To the best of our knowledge, our algorithm is the first Hough method that is highly adaptable since it works for buildings with edges of different lengths and arbitrary relative orientations. The results prove that our method delivers high completeness (between 90.1% and 96.4%) and correctness percentages (all over 96%). The positional accuracy of the building corners is between 0.2–0.57 m RMSE. The quality rate (89.6%) for the Vaihingen-B benchmark outperforms all existing state of the art methods. Other solutions for the challenging Vaihingen-A dataset are not yet available, while we achieve a quality score of 93.2%. Results with arbitrary directions are demonstrated on the complex buildings around the EYE museum in Amsterdam.

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“…Abdollahi et al introduced an end-to-end convolutional neural network called Generative Adversarial Network (GAN) to extract accurate building boundary [17]. Other researches had also designed specific convolution features network to refine the building contour [18][19][20][21]. However, most of the studies were tested in theoretical environment with high spatial resolution nadir remotely sensed images.…”

Section: Introductionmentioning

confidence: 99%

Extraction of Dense Urban Buildings From Photogrammetric and LiDAR Point Clouds

et al. 2021

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Point clouds derived from LiDAR (Light Detection and Ranging) and photogrammetry systems are used to extract building footprints in dense urban areas. Two extraction methods based on DSM (Digital Surface Model) images and point clouds are comprehensively evaluated and compared. Firstly, photogrammetric point clouds are generated from aerial images of downtown Guangzhou, China, and compared with corresponding LiDAR point clouds. Then, DSM images are created using these point clouds and a threshold segmentation method is applied for building extraction. Although regularized buildings can be extracted according to the selection of appropriate height thresholds for the LiDAR DSM and photogrammetric DSM, blurry building boundaries exist for results of photogrammetric DSM when high trees are available nearby. LiDAR DSM extraction performs better in terms of Precision, Recall, and F-score metrics. A DoN (Difference of Normals) approach based on point cloud datasets is also quantitatively and qualitatively demonstrated. Our experiments show that when a suitable radius threshold is selected, the method provides satisfactorily normal calculation results and can successfully isolate building roofs from other objects in densely built-up areas. The majority of building extraction results have a precision > 0.9 and favorable Recall and F-score results. There is high consistency between photogrammetric and LiDAR point clouds. Although LiDAR provides higher extraction accuracy, photogrammetry is also useful for its more convenient acquisition and higher point cloud densities.

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Object Contour and Edge Detection with RefineContourNet

Cited by 38 publications

References 31 publications

Multiscale Anisotropic Morphological Directional Derivatives for Noise-Robust Image Edge Detection

Multiscale Anisotropic Morphological Directional Derivatives for Noise-Robust Image Edge Detection

Automatic Building Outline Extraction from ALS Point Clouds by Ordered Points Aided Hough Transform

Extraction of Dense Urban Buildings From Photogrammetric and LiDAR Point Clouds

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