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A bio-inspired approach to line segment detection utilizing orientation-selective neurons

SIGNAL PROCESSING 2025年 233卷

作者： Yang, Daipeng Peng, Bo Wu, Xi Southwest Jiaotong Univ Sch Comp & Artificial Intelligence Chengdu Peoples R China Chengdu Univ Informat Technol Sch Comp Sci Chengdu Peoples R China

line segment detection is essential for tasks like SLAM, camera pose estimation, and 3D reconstruction. Although many excellent line segment detection methods have been proposed, detecting more true positives while rejecting false positives remains challenging. The human visual system can effectively perceive line segments in complex environments through a processing pathway involving multiple visual cortices. Inspired by this, we propose a novel bio-inspired line segment detection method that mimics the perception of line segments in the visual cortex. Our method models orientation-selective neurons in the primary and secondary visual cortices. Based on the preferred orientations of these neurons, we integrate them to mimic the function of orientation and curvature domains in the fourth visual cortex, generating continuous and smooth edge segments. A post-processing step, including least squares line fitting and gap merging, is employed to obtain line segments. We evaluated our method against other state-of-the-art methods on YorkUrban-linesegment and Wireframe. Results show that our method achieves a higher F-score, improving by 2.9% and 2.1%, respectively, while ensuring both precision and recall. Additionally, in 3D reconstruction, our method produces more complete and accurate scenes with fewer fragments and omissions compared to other methods. Our code is available at https://***/DaipengYang7/BILSD.

关键词： line segment detection Bio-inspired method Orientation-selective neurons Gap merging

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line segment detection based on probability map 36

Line segment detection based on probability map

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第36届中国控制会议

作者： Fei Wu Sun Li Bo Wang Jinlei Ma School of Automation Beijing Institute of Technology

ISBN: (纸本)9781538629185

In this paper,a novel line segment detection method based on probability map is ***,the local gradient information is used to estimate if a pixel belongs to a line segment and a probability map is *** probability map combines gradient orientation with gradient magnitude information and can provide candidate points for edge chain ***,these candidate points are connected together to generate edge chains and then edge chains are split to candidate line segments by using least square line fitting *** last,we apply Helmholtz principle to validate the detected line *** results demonstrate that the proposed method outperforms other methods in term of visual comparison and quantitative assessment.

关键词： line segment detection probability map least square line fitting Helmholtz principle

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line segment detection via Random line Fitting 11

Line Segment Detection via Random Line Fitting

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11th International Symposium on Multispectral Image Processing and Pattern Recognition (MIPPR) - Pattern Recognition and Computer Vision

作者： Shang, Ke Lei, Tao Wang, Quan Zhang, Yu Zhang, Hao Tian, Jinwen Tianjin Jinhang Tech Phys Inst Tianjin Peoples R China Tianjin Infrared Imageing Technol Engn Ctr Tianjin Peoples R China Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Wuhan Peoples R China

ISBN: (纸本)9781510636385

In this study we propose a line segment detector that generates accurate results. The proposed algorithm, which is linear-time for the number of edge pixels, provides highly accurate result and does not break off at cross points. The proposed algorithm starts from a randomly selected pixel and uses the improved least-square fitting method. This improved method is designed to process incremental data in linear-time. The proposed algorithm is highly suitable for the vision measurement and camera calibration applications.

关键词： line segment detection line Fitting sub-pixel

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LSDNet: Trainable Modification of LSD Algorithm for Real-Time line segment detection

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IEEE ACCESS 2022年 10卷 45256-45265页

作者： Teplyakov, Lev Erlygin, Leonid Shvets, Evgeny Russian Acad Sci Inst Informat Transmiss Problems Moscow 119991 Russia Moscow Inst Phys & Technol Dept Control & Appl Math Moscow 117303 Russia

As of today, the best accuracy in line segment detection (LSD) is achieved by algorithms based on convolutional neural networks - CNNs. Unfortunately, these methods utilize deep, heavy networks and are slower than traditional model-based detectors. In this paper we build an accurate yet fast CNN-based detector, LSDNet, by incorporating a lightweight CNN into a classical LSD detector. Specifically, we replace the first step of the original LSD algorithm - construction of line segments heatmap and tangent field from raw image gradients - with a lightweight CNN, which is able to calculate more complex and rich features. The second part of the LSD algorithm is used with only minor modifications. Compared with several modern line segment detectors on standard Wireframe dataset, the proposed LSDNet provides the highest speed (among CNN-based detectors) of 214 FPS with a competitive accuracy of 78 F-H. Although the best-reported accuracy is 83 F-H at 33 FPS, we speculate that the observed accuracy gap is caused by errors in annotations and the actual gap is significantly lower. We point out systematic inconsistencies in the annotations of popular line detection benchmarks - Wireframe and York Urban, carefully reannotate a subset of images and show that (i) existing detectors have improved quality on updated annotations without retraining, suggesting that new annotations correlate better with the notion of correct line segment detection;(ii) the gap between accuracies of our detector and others diminishes to negligible 0.2 F-H, with our method being the fastest.

关键词： Convolutional neural networks edge detection line segment detection U-net LSD

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UND: Unite-and-Divide Method in Fourier and Radon Domains for line segment detection

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IEEE TRANSACTIONS ON IMAGE PROCESSING 2013年第6期22卷 2500-2505页

作者： Shi, Daming Gao, Junbin Rahmdel, Payam S. Antolovich, Michael Clark, Tony Middlesex Univ Sch Sci & Technol London NW4 4BT England Harbin Inst Technol London NW4 4BT England Charles Sturt Univ Sch Comp & Math Bathurst NSW 2795 Australia

In this paper, we extend our previously proposed line detection method to line segmentation using a so-called unite-and-divide (UND) approach. The methodology includes two phases, namely the union of spectra in the frequency domain, and the division of the sinogram in Radon space. In the union phase, given an image, its sinogram is obtained by parallel 2D multilayer Fourier transforms, Cartesian-to-polar mapping and 1D inverse Fourier transform. In the division phase, the edges of butterfly wings in the neighborhood of every sinogram peak are firstly specified, with each neighborhood area corresponding to a window in image space. By applying the separated sinogram of each such windowed image, we can extract the line segments. The division Phase identifies the edges of butterfly wings in the neighborhood of every sinogram peak such that each neighborhood area corresponds to a window in image space. line segments are extracted by applying the separated sinogram of each windowed image. Our experiments are conducted on benchmark images and the results reveal that the UND method yields higher accuracy, has lower computational cost and is more robust to noise, compared to existing state-of-the-art methods.

关键词： Fourier transform Hough transform line segment detection radon transform

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PLSD: A Perceptually Accurate line segment detection Approach

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IEEE ACCESS 2020年 8卷 42595-42607页

作者： Yu, Qida Xu, Guili Cheng, Yuehua Zhu, Zheng H. Nanjing Univ Aeronaut & Astronaut Coll Automat Engn Nanjing 211106 Peoples R China Minist Ind & Informat Technol Key Lab Nondestruct Detect & Monitoring Technol High Spee Nanjing 211100 Peoples R China York Univ Dept Mech Engn Toronto ON M3J 1P3 Canada

Most existing line segment detection methods suffer from the over-segmentation phenomenon. An improved line segment detection method is presented in this work, which can generate more and longer line segments, yet still accurately reflect the structural details of the image. line segment grouping, line segment validation and a multiscale framework are adopted to reach this end. Specifically, smart grouping rules are introduced to locate potential homologous line segments (derived from the same boundaries). Novel merging criteria based on Helmholtz principle is then used to evaluate the meaningfulness between separate line segments and their merged ones. The improved multiscale framework facilitates line segments merging in detection and post-detection processes, yielding more high-quality line segments. Finally, the proposed method is compared with four leading methods on the famous public dataset, YorkUrban-linesegment. Experimental results demonstrate that the method has good continuity and outperforms the leading methods in F-measure.

关键词： A contrario approach grouping rules line segment detection line segment validation multiscale

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ULSD: Unified line segment detection across pinhole, fisheye, and spherical cameras

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ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING 2021年 178卷 187-202页

作者： Li, Hao Yu, Huai Wang, Jinwang Yang, Wen Yu, Lei Scherer, Sebastian Wuhan Univ Sch Elect Informat Wuhan 430072 Peoples R China Carnegie Mellon Univ Robot Inst Pittsburgh PA 15213 USA

Image line segment detection is a fundamental problem in computer vision and remote sensing. Although numerous state-of-the-art methods have shown great performance for straight line segment detection, line segment detection for distorted images without undistortion is still a challenging problem. Besides, there is a lack of a unified line segment detection framework for both distorted and undistorted images. To address these two problems, we propose a novel learning-based Unified line segment detection method (i.e., ULSD) for distorted and undistorted images in this paper. Specifically, we first propose a novel equipartition point-based Bezier curve representation to model arbitrary distorted line segments. Then the line segment detection is tackled by equipartition point regression with an end-to-end trainable neural network. Consequently, the proposed ULSD is independent of camera distortion parameters and does not need any undistortion preprocessing. In the experiments, the proposed method is firstly evaluated on the pinhole, fisheye, and spherical image datasets, respectively, as well as trained and tested on the mixed dataset with differently distorted images. The experimental results on each distortion model show that the proposed ULSD is more competitive than the state-of-the-art methods for both accuracy and efficiency, especially for the results of the unified model trained on the mixed datasets, thus demonstrating the effectiveness and generality of the proposed ULSD to real-world scenarios.

关键词： line segment detection Bezier curve Image distortion Neural network

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Learning Regional Attraction for line segment detection

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IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE 2021年第6期43卷 1998-2013页

作者： Xue, Nan Bai, Song Wang, Fu-Dong Xia, Gui-Song Wu, Tianfu Zhang, Liangpei Torr, Philip H. S. Wuhan Univ Sch Comp Sci Wuhan 430072 Hubei Peoples R China Univ Oxford Oxford OX1 2JD England Wuhan Univ State Key Lab LIESMARS Wuhan 430072 Hubei Peoples R China North Carolina State Univ Dept Elect & Comp Engn Raleigh NC 27695 USA

This paper presents regional attraction of line segment maps, and hereby poses the problem of line segment detection (LSD) as a problem of region coloring. Given a line segment map, the proposed regional attraction first establishes the relationship between line segments and regions in the image lattice. Based on this, the line segment map is equivalently transformed to an attraction field map (AFM), which can be remapped to a set of line segments without loss of information. Accordingly, we develop an end-to-end framework to learn attraction field maps for raw input images, followed by a squeeze module to detect line segments. Apart from existing works, the proposed detector properly handles the local ambiguity and does not rely on the accurate identification of edge pixels. Comprehensive experiments on the Wireframe dataset and the YorkUrban dataset demonstrate the superiority of our method. In particular, we achieve an F-measure of 0.831 on the Wireframe dataset, advancing the state-of-the-art performance by 10.3 percent.

关键词： Image segmentation Image edge detection Detectors Lattices Machine learning Electronic mail Training line segment detection low-level vision deep learning

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Mean shift based clustering of Hough domain for fast line segment detection

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PATTERN RECOGNITION LETTERS 2006年第6期27卷 578-586页

作者： Bandera, A Pérez-Lorenzo, JM Bandera, JP Sandoval, F Univ Malaga ETSI Telecomunic Dpto Tecnol Elect E-29071 Malaga Spain

This paper proposes a new algorithm for extracting line segments from edge images. Basically, the method performs two consecutive stages. In the first stage, the algorithm follows a line segment random window randomized Hough transform (RWRHT) based approach. This approach provides a mechanism for finding more favorable line segments from a global point of view. In our case, the RWRHT based approach is used to actualise an accurate Hough parameter space. In the second stage, items of this parameter space are unsupervisedly clustered in a set of classes using a variable bandwidth mean shift algorithm. Cluster modes provided by this algorithm constitute a set of base lines. Thus, clustering process allows using accurate Hough parameters and, however, detecting only one line when pixels along it are not exactly collinear. Edge pixels lying on the lines grouped to generate each base line are projected onto this base line. A fast and purely local grouping algorithm is employed to merge points along each base line into line segments. We have performed several experiments to compare the performance of our method with that of other methods. Experimental results show that the performance of the proposed method is very high in terms of line segment detection ability and execution time. (c) 2005 Elsevier B.V. All rights reserved.

关键词： line segment detection random window randomized Hough transform mean shift based clustering line segment grouping

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Contrast-Guided line segment detection

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IEEE SIGNAL PROCESSING LETTERS 2024年 31卷 281-285页

作者： Wang, Zikai Zhong, Baojiang Han, Dongxu Ma, Kai-Kuang Soochow Univ Sch Comp Sci & Technol Suzhou 215006 Peoples R China Nanjing Univ Aeronaut & Astronaut Coll Elect & Informat Engn Nanjing 210000 Peoples R China

Due to the effects of quantization error and image noise, detecting 'meaningful' line segments from an image with high continuity is a challenging task. To pursue this goal, a novel line segment detector, called the contrast-guided line segment detector (CGLSD), is proposed in this paper. Our basic idea is to integrate a low-level image attribute, i.e., edge contrast, into the line segment detection process for improving line continuity. After applying an edge detector to the input image, the edge contrast is exploited to guide the growth of a line-support region for each line segment individually. This is achieved by evaluating edge pixels as well as those non-edge pixels that are nearby the edges. As a result, some of the non-edge pixels are re-considered as 'edge' pixels and included for establishing the support region. Reversely, certain edge pixels might be treated as 'non-edge' pixels instead and excluded from the region. Since each support region is supposed to yield only one line segment, each formed support region needs to have a refinement by removing those edge pixels that do not belong to it. Lastly, the support region is required to pass through a validation check that might lead to a complete discard of the line segment due to its low confidence. Extensive experiments are conducted and compared with multiple state-of-the-arts on two datasets, including the one from us with manually-annotated ground truth. The results have shown that the proposed CGLSD can deliver superior performance in nearly all test cases.

关键词： line segment detection contrast guided line-support region refinement line segment validation

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