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Vanishing point detection using the teaching learning-based optimisation algorithm

IET IMAGE PROCESSING 2020年第11期14卷 2487-2494页

作者： Lopez-Martinez, Alan Cuevas, Francisco J. Ctr Invest Opt AC Opt Metrol Lomas Bosque 115 Guanajuato Mexico

Within the computer vision field, estimating image vanishing points has many applications regarding robotic navigation, camera calibration, image understanding, visual measurement, 3D reconstruction, among others. Different methods for detecting vanishing points relies on accumulator space techniques, while others employ a heuristic approach such as RANSAC. Nevertheless, these types of methods suffer from low accuracy or high computational cost. To explore a different technique, this paper focuses on improving the efficiency of the metaheuristic search for vanishing points by using a recently proposed population-based method: The Teaching Learning Based Optimisation algorithm (TLBO). The TLBO algorithm is a metaheuristic technique inspired by the teaching-learning process. In our method, the TLBO algorithm is used after a line segment detection, to cluster line segments according to their more optimal vanishing point. Thus, our algorithm detects both orthogonal and nonorthogonal vanishing points in real images. To corroborate the performance of our proposed algorithm, different comparison and tests with other approaches were carried out. The results validate the accuracy and efficiency of our proposed method. Our approach had an average computational time of1.42 seconds and obtained a cumulative focal length error of 1 pixel, and cumulative angular error of 0.1 degrees.

关键词： image segmentation pattern clustering search problems optimisation computer vision edge detection TLBO algorithm line segment detection optimal vanishing point nonorthogonal vanishing points vanishing point detection teaching learning-based optimisation algorithm computer vision image vanishing points heuristic approach metaheuristic search population-based method metaheuristic technique teaching-learning process computational cost

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Topological Image Reconstruction of Regular Grounding Network Based on Hough Transform

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IEEE SENSORS JOURNAL 2024年第17期24卷 27587-27596页

作者： Song, Hengli Xiong, Yixiang Zhao, Qingpu China Univ Geosci Sch Automat Wuhan 430074 Hubei Peoples R China Hubei Key Lab Adv Control & Intelligent Automat Co Wuhan 430074 Hubei Peoples R China Minist Educ Engn Res Ctr Intelligent Technol Geoexplorat Wuhan 430074 Hubei Peoples R China Zhejiang Dahua Technol Co Ltd Hangzhou 310053 Peoples R China

In order to ensure the normal operation of substation equipment, it is necessary to periodically diagnose the corrosion of the grounding grid. Due to the expansion and daily maintenance of the substation, the topology of the grounding grid is often changed, so it is necessary to extract and obtain the topology of the grounding grid. However, most studies mainly use image processing to obtain the topology of the grounding grid, and the obtained topology has the problems of image blurring, noise, and conductor alignment bending. In this article, we propose a topology detection method with local peak algorithm and Hough line segment reconstruction for regular grounded networks. The method first adopts the local peak algorithm to realize the extraction of the characteristic points of the grounding grid in the measurement area according to the distribution trend of the magnetic response signal formed by the current-carrying conductor on the ground surface, and the method effectively eliminates the noise interference within the topology. Then, the Hough transform is used to detect the geometric information in the feature point map, and the straight lines in the feature point map are reconstructed to obtain the reconstructed image of the grounding grid topology. Experiments show that the Hough line segment detection can well suppress the line segment bending and noise problems and improve the imaging effect of the grounding grid topology.

关键词： Grounding Conductors Topology Image reconstruction Substations Magnetic resonance imaging Magnetic field measurement Grounding grids Hough transform image line segment detection topology

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LSM: perceptually accurate line segment merging

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JOURNAL OF ELECTRONIC IMAGING 2016年第6期25卷 1-11页

作者： Hamid, Naila Khan, Nazar Univ Punjab Coll Informat Technol Comp Vis & Machine Learning Grp Old CampusMall Rd Lahore Pakistan

Existing line segment detectors tend to break up perceptually distinct line segments into multiple segments. We propose an algorithm for merging such broken segments to recover the original perceptually accurate line segments. The algorithm proceeds by grouping line segments on the basis of angular and spatial proximity. Then those line segment pairs within each group that satisfy unique, adaptive mergeability criteria are successively merged to form a single line segment. This process is repeated until no more line segments can be merged. We also propose a method for quantitative comparison of line segment detection algorithms. Results on the York Urban dataset show that our merged line segments are closer to human-marked ground-truth line segments compared to state-of-the-art line segment detection algorithms. (C) 2016 SPIE and IS& T

关键词： line segments line segment detection line detection grouping merging spatial proximity angular proximity perception Gestalt perceptually accurate line segments quantitative evaluation

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An Unsupervised Hair segmentation and Counting System in Microscopy Images

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IEEE SENSORS JOURNAL 2015年第6期15卷 3565-3572页

作者： Shih, Huang-Chia Yuan Ze Univ Dept Elect Engn Innovat Ctr Big Data & Digital Convergence Human Comp Interact Multimedia Lab Zhongli 320 Taiwan

This paper focuses on the development of medical software for clinical applications using advanced image processing algorithms. Three critical issues of hair segmentation and counting are addressed in this paper. First, the removal of any bright spots due to oil or moisture, which generate circular patterns in the middle of the hair and significantly affect the accuracy of determining the line. Second, two contacting or overlapping hairs are recognized and counted as a single hair. To solve this problem, we proposed a hair-bundling algorithm to calculate any concealed hairs. Finally, hairs may be wavy or curly, making the conventional Hough-based line detection algorithm unsuitable, since it suffers from parameter selections, such as the minimum length of line segment, and distance between line segments. Our proposed hair counting algorithm is substantially more accurate than the Hough-based one, and robust to curls, oily scalp, noise-corruption, and overlapping hairs, under various white balance.

关键词： Hair counting scalp diagnosis hair care diagnosis hair follicle diagnosis line segment detection

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The Whole and the Parts: The Minimum Description Length Principle and the A-Contrario Framework

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SIAM JOURNAL ON IMAGING SCIENCES 2022年第3期15卷 1282-1313页

作者： von Gioi, Rafael Grompone Paulino, Ignacio Ramirez Randall, Gregory Univ Paris Saclay CNRS Ctr Borelli ENS Paris Saclay Gif Sur Yvette France Univ Republica Inst Ingn Electr Montevideo Uruguay

This work explores the connections between the minimum description length (MDL) principle as developed by Rissanen, and the a-contrario framework for structure detection proposed by Desolneux, Moisan, and Morel. The MDL principle focuses on the best interpretation for the whole data while the a-contrario approach concentrates on detecting parts of the data with anomalous statistics. Although framed in different theoretical formalisms, we show that both methodologies share many common concepts and tools in their machinery and yield very similar formulations in a number of interesting scenarios ranging from simple toy examples to practical applications such as polygonal approximation of curves and line segment detection in images. We also formulate the conditions under which both approaches are formally equivalent.

关键词： model selection structure detection MDL a-contrario framework nonaccidentalness principle NFA polygonal approximation line segment detection

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A structure-aware axis-aligned grid deformation approach for robust image retargeting

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MULTIMEDIA TOOLS AND APPLICATIONS 2018年第6期77卷 7717-7739页

作者： Kim, Yoonhyung Jung, Seungjun Jung, Chanho Kim, Changick Korea Adv Inst Sci & Technol Sch Elect Engn Daejeon 34141 South Korea Hanbat Natl Univ Dept Elect Engn Daejeon 34158 South Korea

Content-based image retargeting is a technique that resizes an input image to a given target resolution while minimizing distortions of important objects caused by aspect ratio variations. Conventional approaches have shared a similar methodology which aims to preserve salient regions as much as possible while allowing distortions of less important regions. Those methods have shown satisfactory results for input images whose objects are distinct and backgrounds are monotonous. However, their performance is not always guaranteed for images containing structural components such as straight lines, which are prone to be distorted after resizing and sensitive to human visual perception. In this paper, we propose a structure-aware axis-aligned grid deformation approach for robust image retargeting. Based on axis-aligned grid, our method finds the optimal grid for target image by quadratic optimization represented by two objective functions. The first one is the As-similar-as-possible (ASAP) energy function, which aims to preserve important regions while allowing distortions of trivial regions. The second one is the Adaptive Laplacian regularization (ALR) energy function, which aims to relieve structural distortions. Those two energy functions are combined into single quadratic optimization model ensuring the global convexity and solved by a quadratic programming solver for finding the optimal grid. Experimental results show that our method is robust to structural distortions while achieving the basic purpose of content-based image retargeting. For objective comparisons with other methods, we have provided objective evaluation scores by using a recent state-of-the-art image retargeting quality assessment scheme.

关键词： Content-based image retargeting Quadratic programming Saliency detection line segment detection

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LSNetv2: Improving weakly supervised power line detection with bipartite matching

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EXPERT SYSTEMS WITH APPLICATIONS 2024年 250卷

作者： Tran, Duy Khoi Nguyen, Van Nhan Roverso, Davide Jenssen, Robert Kampffmeyer, Michael UiT Arctic Univ Norway Dept Phys & Technol N-9019 Tromso Norway eSmart Syst Analyt Dept N-1783 Halden Norway

This paper addresses the crucial task of power line detection and localization in electrical infrastructure inspection using Unmanned Aerial Vehicles (UAVs) from weak supervision, polyline annotations. We first identify several limitations in the state-of-the-art approach LSNet. In particular, the inability of LSNet to detect line-crossings and lines in close proximity. To overcome these limitations, we propose LSNetv2, which enhances LSNet with multi-line segment detection capability facilitated via a bipartite matching loss. Additionally, we update LSNet's regression loss in order to stabilize training by reducing the interdependence between predicted coordinates. Finally, LSNetv2 makes use of an increased receptive field to extract global information, improving overall detection performance. Through extensive evaluations on various power line detection datasets, LSNetv2 demonstrates superior performance and robustness. On the public datasets PLDU, PLDM and TTPLA, it achieved F-beta scores of 0.857, 0.875, and 0.671, respectively, while using only modified weak polyline annotation, establishing itself as an effective and efficient solution for power line detection in UAV-based electrical infrastructure inspections.

关键词： line segment detection Power line detection Power line inspection Deep learning

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Automatic soccer field of play registration

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PATTERN RECOGNITION 2020年 103卷 107278-107278页

作者： Cuevas, Carlos Quilon, Daniel Garcia, Narciso Univ Politecn Madrid UPM Informat Proc & Telecommun Ctr IPTC Grp Tratamiento Imagenes GTI Madrid 28040 Spain Univ Politecn Madrid UPM ETSI Telecomunicac Madrid 28040 Spain

This paper proposes a strategy for the automatic registration of soccer images on a model of the field of play. First, a robust and efficient preprocessing is applied to discard the areas of the image that do not belong to the field of play and eliminate most edge points that are not part of the line marks. Then, a novel probabilistic decision tree is used to identify the most probable classification for the set of all the straight lines in the image. Additionally, the line surrounding the center circle is also modeled for providing results when only few straight lines are visible. Finally, a three-step analysis stage is applied to ensure the validity of the results. To assess its quality, the strategy has been tested on several sequences corresponding to three stadiums with different characteristics. The results obtained have shown that the registration is successful in most images (94%). (C) 2020 Elsevier Ltd. All rights reserved.

关键词： Soccer Football Field of play Playing field Registration segmentation line segment detection line classification Validation

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Joint Contours, Corner and T-Junction detection: An Approach Inspired by the Mammal Visual System

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JOURNAL OF MATHEMATICAL IMAGING AND VISION 2018年第3期60卷 341-354页

作者： Buades, Antonio von Gioi, Rafael Grompone Navarro, Julia Univ Illes Balears Ctra Valldemossa Km 7-5 Palma De Mallorca Spain ENS Cachan CMLA Ave President Wilson Cachan France

We introduce a new algorithm that allows the detection of line segments, contours, corners and T-junctions. The proposed model is inspired by the mammal visual system. The detection of corners and T-junctions plays a role as part of the process in contour detection. This method unifies tasks that have been traditionally worked apart. An a-contrario validation is applied to select the most meaningful contours without the need of fixing any critical parameter.

关键词： Contour detection line segment detection Visual system A-contrario validation

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LB-LSD: A length-based line segment detector for real-time applications

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PATTERN RECOGNITION LETTERS 2019年 128卷 247-254页

作者： Liu, Yang Xie, Zongwu Liu, Hong Harbin Inst Technol State Key Lab Robot & Syst Harbin 150000 Heilongjiang Peoples R China

line segments play an important role in the perception and representation of images by providing the geometry information about the scene. These segments can also be used as low-level features to analyze and detect more elaborated shapes. A length-based line segment detector is proposed in this paper. Based on the edge proportion statistics, an adaptive, robust and effective edge detection method is presented to extract edge segments from the image. Each of the segments is a clean, contiguous, 1-pixel wide chain of pixels. The line segment detector then approximates all the edge segments in a way that the curve satisfies a criterion of length condition. The detection result is a series of piecewise-linear segments with some dominant corners. Experimental results indicate that the proposed detector has a good detection accuracy and outperforms the state-of-the-art methods in terms of execution time. (C) 2019 Elsevier B.V. All rights reserved.

关键词： line segment detection Adaptive threshold Edge segment Helmholtz principle Real-time Length condition

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