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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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Connectivity-Enforcing Hough Transform for the Robust Extraction of line segments

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IEEE TRANSACTIONS ON IMAGE PROCESSING 2012年第12期21卷 4819-4829页

作者： Guerreiro, Rui F. C. Aguiar, Pedro M. Q. Inst Super Tecn Inst Syst & Robot P-1049001 Lisbon Portugal

Global voting schemes based on the Hough transform (HT) have been widely used to robustly detect lines in images. However, since the votes do not take line connectivity into account, these methods do not deal well with cluttered images. On the other hand, the so-called local methods enforce connectivity but lack robustness to deal with challenging situations that occur in many realistic scenarios, e.g., when line segments cross or when long segments are corrupted. We address the critical limitations of the HT as a line segment extractor by incorporating connectivity in the voting process. This is done by only accounting for the contributions of edge points lying in increasingly larger neighborhoods and whose position and directional information agree with potential line segments. As a result, our method, which we call segment extraction by connectivity-enforcing HT (STRAIGHT), extracts the longest connected segments in each location of the image, thus also integrating into the HT voting process the usually separate step of individual segment extraction. The usage of the Hough space mapping and a corresponding hierarchical implementation make our approach computationally feasible. We present experiments that illustrate, with synthetic and real images, how STRAIGHT succeeds in extracting complete segments in situations where current methods fail.

关键词： Connected segments connectivity edge analysis Hough transform (HT) line pattern analysis line segment detection

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Detecting line segments in Motion-Blurred Images With Events

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IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE 2024年第5期46卷 2866-2881页

作者： Yu, Huai Li, Hao Yang, Wen Yu, Lei Xia, Gui-Song Wuhan Univ Sch Elect Informat Wuhan 430072 Peoples R China Baidu Inc Beijing 100085 Peoples R China Wuhan Univ Sch Comp Sci Wuhan 430072 Peoples R China

Making line segment detectors more reliable under motion blurs is one of the most important challenges for practical applications, such as visual SLAM and 3D line mapping. Existing line segment detection methods face severe performance degradation for accurately detecting and locating line segments when motion blur occurs. While event data shows strong complementary characteristics to images for minimal blur and edge awareness at high-temporal resolution, potentially beneficial for reliable line segment recognition. To robustly detect line segments over motion blurs, we propose to leverage the complementary information of images and events. Specifically, we first design a general frame-event feature fusion network to extract and fuse the detailed image textures and low-latency event edges, which consists of a channel-attention-based shallow fusion module and a self-attention-based dual hourglass module. We then utilize the state-of-the-art wireframe parsing networks to detect line segments on the fused feature map. Moreover, due to the lack of line segment detection datasets with pairwise motion-blurred images and events, we contribute two datasets, i.e., synthetic FE-Wireframe and realistic FE-Blurframe, for network training and evaluation. Extensive analyses on the component configurations demonstrate the design effectiveness of our fusion network. When compared to the state-of-the-arts, the proposed approach achieves the highest detection accuracy while maintaining comparable real-time performance. In addition to being robust to motion blur, our method also exhibits superior performance for line detection under high dynamic range scenes.

关键词： Frame-event fusion line segment detection motion blur

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Sensitivity analysis of augmented reality-assisted building damage reconnaissance using virtual prototyping

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AUTOMATION IN CONSTRUCTION 2013年 33卷 24-36页

作者： Dong, Suyang Feng, Chen Kamat, Vineet R. Univ Michigan Dept Civil & Environm Engn Ann Arbor MI 48109 USA

The timely and accurate assessment of the damage sustained by a building during catastrophic events, such as earthquakes or blasts, is critical in determining the building's structural safety and suitability for future occupancy. Among many indicators proposed for measuring structural integrity, especially inelastic deformations, Interstory Drift Ratio (IDR) remains the most trustworthy and robust metric at the story level. In order to calculate IDR, researchers have proposed several nondestructive measurement methods. Most of these methods rely on pre-installed target panels with known geometric shapes or with an emitting light source. Such target panels are difficult to install and maintain over the lifetime of a building. Thus, while such methods are nondestructive, they are not entirely non-contact. This paper proposes an Augmented Reality (AR)-assisted non-contact method for estimating IDR that does not require any pre-installed physical infrastructure on a building. The method identifies corner locations in a damaged building by detecting the intersections between horizontal building baselines and vertical building edges. The horizontal baselines are superimposed on the real structure using an AR algorithm, and the building edges are detected via a line segment detection (LSD) approach. The proposed method is evaluated using a Virtual Prototyping (VP) environment that allows testing of the proposed method in a reconfigurable setting. A sensitivity analysis is also conducted to evaluate the effect of instrumentation errors on the method's practical use. The experimental results demonstrate the potential of the new method to facilitate rapid building damage reconnaissance, and highlight the instrument precision requirements necessary for practical field implementation. (C) 2012 Elsevier B.V. All rights reserved.

关键词： Building damage Earthquake Reconnaissance Augmented reality line segment detection Nondestructive evaluation

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Joint A Contrario Ellipse and line detection

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IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE 2017年第4期39卷 788-802页

作者： Patraucean, Viorica Gurdjos, Pierre von Gioi, Rafael Grompone Univ Cambridge Dept Engn Cambridge CB2 1TN England IRIT ENSEEIHT Toulouse Midi Pyrenees France CMLA ENS Cachan F-94235 Cachan France

Wepropose a line segment and elliptical arc detector that produces a reduced number of false detections on various types of images without any parameter tuning. For a given region of pixels in a grey-scale image, the detector decides whether a line segment or an elliptical arc is present (model validation). If both interpretations are possible for the same region, the detector chooses the one that best explains the data (model selection). Wedescribe a statistical criterion based on the a contrario theory, which serves for both validation and model selection. The experimental results highlight the performance of the proposed approach compared to state-of-the-art detectors, when applied on synthetic and real images.

关键词： Ellipse detection line segment detection a contrario theory model selection

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Hough transform for feature detection in panoramic images

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PATTERN RECOGNITION LETTERS 2002年第14期23卷 1863-1874页

作者： Fiala, M Basu, A Univ Alberta Dept Comp Sci Fac Sci Edmonton AB T6G 2H1 Canada

Omni-directional sensors are useful in obtaining a 360degrees field-of-view. With a radially symmetric mirror and conventional lens system this can be achieved with a single camera. There are several proposed profiles for the mirror, but most violate the single viewpoint (SVP) criteria necessary to allow functional equivalence to the standard perspective projection, posing challenges that have not yet been addressed in the literature. Such a imaging system with a non-SVP optical system do not benefit from the affine quality of straight line features being represented as collinear points in the image plane. To utilize these non-SVP mirrors, a new method to recognize such features is required. This work describes an approach to detecting features in panoramic non-SVP images using a modified Hough transform. A mathematical model for this feature extraction process is given. Experimental results are presented to validate this model and show robust performance in identifying line features with only estimated calibration. (C) 2002 Elsevier Science B.V. All rights reserved.

关键词： omni-directional sensors panoramic hough transform line segment detection

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Monocular Depth Estimation for Glass Walls With Context: A New Dataset and Method

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IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE 2023年第12期45卷 15081-15097页

作者： Liang, Yuan Deng, Bailin Liu, Wenxi Qin, Jing He, Shengfeng South China Univ Technol Sch Comp Sci & Engn Guangzhou 510006 Peoples R China Singapore Management Univ Sch Comp & Informat Syst Singapore 188065 Singapore Cardiff Univ Sch Comp Sci & Informat Cardiff CF10 3AT Wales Fuzhou Univ Coll Comp & Data Sci Fuzhou 350025 Peoples R China Hong Kong Polytech Univ Sch Nursing Hung Hom Hong Kong Peoples R China

Traditional monocular depth estimation assumes that all objects are reliably visible in the RGB color domain. However, this is not always the case as more and more buildings are decorated with transparent glass walls. This problem has not been explored due to the difficulties in annotating the depth levels of glass walls, as commercial depth sensors cannot provide correct feedbacks on transparent objects. Furthermore, estimating depths from transparent glass walls requires the aids of surrounding context, which has not been considered in prior works. To cope with this problem, we introduce the first Glass Walls Depth Dataset (GW-Depth dataset). We annotate the depth levels of transparent glass walls by propagating the context depth values within neighboring flat areas, and the glass segmentation mask and instance level line segments of glass edges are also provided. On the other hand, a tailored monocular depth estimation method is proposed to fully activate the glass wall contextual understanding. First, we propose to exploit the glass structure context by incorporating the structural prior knowledge embedded in glass boundary line segment detections. Furthermore, to make our method adaptive to scenes without structure context where the glass boundary is either absent in the image or too narrow to be recognized, we propose to derive a reflection context by utilizing the depth reliable points sampled according to the variance between two depth estimations from different resolutions. High-resolution depth is thus estimated by the weighted summation of depths by those reliable points. Extensive experiments are conducted to evaluate the effectiveness of the proposed dual context design. Superior performances of our method is also demonstrated by comparing with state-of-the-art methods. We present the first feasible solution for monocular depth estimation in the presence of glass walls, which can be widely adopted in autonomous navigation.

关键词： Glass Estimation Reflection Task analysis Reliability Cameras Transformers Glass detection line segment detection monocular depth estimation

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Multiscale detection of Circles, Ellipses and line segments, Robust to Noise and Blur

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IEEE ACCESS 2021年 9卷 25554-25578页

作者： Martorell, Onofre Buades, Antoni Lisani, Jose Luis Univ Illes Balears Inst Appl Comp & Community Code IAC3 Dept Math & Comp Sci Palma De Mallorca 07122 Spain

This paper proposes a basic taxonomy of image contours. Our goal is to classify smooth curves into five categories, namely, circles, ellipses, line segments, arcs of circles and arcs of ellipses. These geometrical structures have been chosen as they serve as input of many computer vision tasks. The proposed strategy is applied on a set of initial disjoint contours, which are grouped together to form the aforementioned structures. These, in turn, are validated using an a contrario approach that guarantees a reduced number of false detections. The use of a multiscale strategy permits the detection at different resolution levels, which makes the method robust to noise and blur.

关键词： Image edge detection Image segmentation Detectors Transforms Three-dimensional displays Task analysis Image resolution line segment detection circle detection ellipse detection a contrario validation

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Fast line segment grouping method for finding globally more favorable line segments

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PATTERN RECOGNITION 2002年第10期35卷 2235-2247页

作者： Jang, JH Hong, KS POSTECH Dept Elect Engn Image Informat Proc Lab Nam Ku Pohang 790784 Kyungbuk South Korea

In this paper, we propose a new method for extracting line segments from edge images. Our method basically follows a line segment grouping approach. This approach has many advantages over a Hough transform based approach in practical situations. However, since the process of the conventional line segment grouping approach is purely local, it does not provide a mechanism for finding more favorable line segments from a global point of view. Our method overcomes the local nature of the conventional line segment grouping approach, while retaining most of its advantages, by incorporating the useful concept of the Hough transform based approach into the line segment grouping approach. Our method is fast and allows elementary line segments to be shared simultaneously by several line segments, and the degree of sharing is determined by a user-specified threshold. We performed a series of tests to compare the performance of our method with that of six other methods. Throughout the tests, our method ranked in the top two of the tested methods both in detection rate and computation time. (C) 2002 Pattern Recognition Society. Published by Elsevier Science Ltd. All rights reserved.

关键词： line segment detection line segment grouping Hough transform

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Accurate and Robust line segment Extraction Using Minimum Entropy With Hough Transform

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IEEE TRANSACTIONS ON IMAGE PROCESSING 2015年第3期24卷 813-822页

作者： Xu, Zezhong Shin, Bok-Suk Klette, Reinhard Changzhou Inst Technol Dept Comp Sci Changzhou 213017 Peoples R China Nanyang Technol Univ Sch Elect & Elect Engn Singapore 639798 Singapore Univ Auckland Dept Comp Sci Auckland 1010 New Zealand

The Hough transform is a popular technique used in the field of image processing and computer vision. With a Hough transform technique, not only the normal angle and distance of a line but also the line-segment's length and midpoint (centroid) can be extracted by analysing the voting distribution around a peak in the Hough space. In this paper, a method based on minimum-entropy analysis is proposed to extract the set of parameters of a line segment. In each column around a peak in Hough space, the voting values specify probabilistic distributions. The corresponding entropies and statistical means are computed. The line-segment's normal angle and length are simultaneously computed by fitting a quadratic polynomial curve to the voting entropies. The line-segment's midpoint and normal distance are computed by fitting and interpolating a linear curve to the voting means. The proposed method is tested on simulated images for detection accuracy by providing comparative results. Experimental results on real-world images verify the method as well. The proposed method for line-segment detection is both accurate and robust in the presence of quantization error, background noise, or pixel disturbances.

关键词： Hough transform line segment detection entropy fitting and interpolation

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