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Journal articles on the topic 'Line feature'

Author: Grafiati

Published: 4 June 2021

Last updated: 1 February 2022

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1

Li, Stan. "Nearest feature line." Scholarpedia 3, no. 3 (2008): 4357. http://dx.doi.org/10.4249/scholarpedia.4357.

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2

Wei Ng, Pan. "Feature Separation: An Approach for Product Line Evolution." International Journal of Engineering and Technology 6, no. 2 (2014): 99–103. http://dx.doi.org/10.7763/ijet.2014.v6.674.

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3

Kang, K. C., Jaejoon Lee, and P. Donohoe. "Feature-oriented product line engineering." IEEE Software 19, no. 4 (2002): 58–65. http://dx.doi.org/10.1109/ms.2002.1020288.

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4

Yan, Lijun, Jun‐Bao Li, Xiaorui Zhu, Jeng‐Shyang Pan, and Linlin Tang. "Bilinear discriminant feature line analysis for image feature extraction." Electronics Letters 51, no. 4 (2015): 336–38. http://dx.doi.org/10.1049/el.2014.3834.

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5

Imani, Maryam, and Hassan Ghassemian. "Feature extraction using median–mean and feature line embedding." International Journal of Remote Sensing 36, no. 17 (2015): 4297–314. http://dx.doi.org/10.1080/01431161.2015.1079665.

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6

Zhao, Liang, Shoudong Huang, Lei Yan, and Gamini Dissanayake. "A new feature parametrization for monocular SLAM using line features." Robotica 33, no. 3 (2014): 513–36. http://dx.doi.org/10.1017/s026357471400040x.

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SUMMARYThis paper presents a new monocular SLAM algorithm that uses straight lines extracted from images to represent the environment. A line is parametrized by two pairs of azimuth and elevation angles together with the two corresponding camera centres as anchors making the feature initialization relatively straightforward. There is no redundancy in the state vector as this is a minimal representation. A bundle adjustment (BA) algorithm that minimizes the reprojection error of the line features is developed for solving the monocular SLAM problem with only line features. A new map joining algo

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7

Krueger, Charles, and Paul Clements. "An Enterprise Feature Ontology for Feature‐Based Product Line Engineering." INSIGHT 22, no. 2 (2019): 34–42. http://dx.doi.org/10.1002/inst.12244.

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8

Krueger, Charles, and Paul Clements. "An Enterprise Feature Ontology for Feature-Based Product Line Engineering." INCOSE International Symposium 27, no. 1 (2017): 533–47. http://dx.doi.org/10.1002/j.2334-5837.2017.00377.x.

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9

Kamaei, Kamran, and Hakan Altınçay. "Editing the nearest feature line classifier." Intelligent Data Analysis 19, no. 3 (2015): 563–80. http://dx.doi.org/10.3233/ida-150732.

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10

Yan-wen, Guo, Peng Qun-sheng, Hu Guo-fei, and Wang Jin. "Smooth feature line detection for meshes." Journal of Zhejiang University-SCIENCE A 6, no. 5 (2005): 460–68. http://dx.doi.org/10.1631/jzus.2005.a0460.

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11

Pietroni, Nico, Stefano Nuvoli, Thomas Alderighi, Paolo Cignoni, and Marco Tarini. "Reliable feature-line driven quad-remeshing." ACM Transactions on Graphics 40, no. 4 (2021): 1–17. http://dx.doi.org/10.1145/3476576.3476737.

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12

Pietroni, Nico, Stefano Nuvoli, Thomas Alderighi, Paolo Cignoni, and Marco Tarini. "Reliable feature-line driven quad-remeshing." ACM Transactions on Graphics 40, no. 4 (2021): 1–17. http://dx.doi.org/10.1145/3450626.3459941.

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13

An, Su-Yong, Jeong-Gwan Kang, Lae-Kyoung Lee, and Se-Young Oh. "Line Segment-Based Indoor Mapping with Salient Line Feature Extraction." Advanced Robotics 26, no. 5-6 (2012): 437–60. http://dx.doi.org/10.1163/156855311x617452.

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14

Majhi, Banshider, Y. Santhosh Reddy, and D. Prasanna Babu. "Novel Features for Off-line Signature Verification." International Journal of Computers Communications & Control 1, no. 1 (2006): 17. http://dx.doi.org/10.15837/ijccc.2006.1.2268.

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In this paper a novel feature extraction scheme has been suggested for offline signature verification. The proposed method used geometric center for feature extraction. Euclidean distance model was used for classification. This classifier is well suitable for features extracted and fast in computation. Method proposed in this paper leads to better results than existing offline signature verification methods. Threshold selection is based on statistical parameters like average and standard deviation (sigma).

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15

Zeng, Jiexian, Liqin Zhan, Xiang Fu, and Binbin Wang. "Straight line matching method based on line pairs and feature points." IET Computer Vision 10, no. 5 (2016): 459–68. http://dx.doi.org/10.1049/iet-cvi.2014.0372.

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16

Hwang, Jae-Ho. "Feature Extraction by Line-clustering Segmentation Method." KIPS Transactions:PartB 13B, no. 4 (2006): 401–8. http://dx.doi.org/10.3745/kipstb.2006.13b.4.401.

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17

Jeng-Shyang Pan, Qingxiang Feng, Lijun Yan, and Jar-Ferr Yang. "Neighborhood Feature Line Segment for Image Classification." IEEE Transactions on Circuits and Systems for Video Technology 25, no. 3 (2015): 387–98. http://dx.doi.org/10.1109/tcsvt.2014.2351092.

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18

Fu Chang, Ya-Ching Lu, and T. Pavlidis. "Feature analysis using line sweep thinning algorithm." IEEE Transactions on Pattern Analysis and Machine Intelligence 21, no. 2 (1999): 145–58. http://dx.doi.org/10.1109/34.748823.

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19

Pang, Y., Y. Yuan, and X. Li. "Generalised nearest feature line for subspace learning." Electronics Letters 43, no. 20 (2007): 1079. http://dx.doi.org/10.1049/el:20072176.

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20

LIANG, S. C., and W. J. CHEN. "Extraction of Line Feature in Binary Images." IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences E91-A, no. 8 (2008): 1890–97. http://dx.doi.org/10.1093/ietfec/e91-a.8.1890.

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21

Jiang, Junjun, Ruimin Hu, Zhen Han, and Tao Lu. "Nearest feature line embedding for face hallucination." Electronics Letters 49, no. 8 (2013): 536–38. http://dx.doi.org/10.1049/el.2012.3724.

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22

Han, Deqiang, Chongzhao Han, and Yi Yang. "Modified center-based feature line classification approach." Frontiers of Electrical and Electronic Engineering in China 5, no. 2 (2010): 173–78. http://dx.doi.org/10.1007/s11460-010-0004-3.

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23

Zorec, J. "Emission-like feature due to a latitudinal differential rotation." Symposium - International Astronomical Union 162 (1994): 257–58. http://dx.doi.org/10.1017/s0074180900214976.

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24

Xia, Zhen, Hai Jiao Gao, and Xiu Hua Zhang. "An Effective Multi-Feature Extraction Method for Manually Plotted Well-Log Curve Line." Applied Mechanics and Materials 511-512 (February 2014): 452–56. http://dx.doi.org/10.4028/www.scientific.net/amm.511-512.452.

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An effective multi-feature extraction method for well-log curve line plotted manually. Several log curve line features are used to extract the well log curve information in the proposed method: gray value similarity feature, connected region Length feature, projection feature and distance and angular feature. The experimental results show the good effect of the method for the well log curve information extraction.

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25

Weber, Christopher, Stefanie Hahmann, Hans Hagen, and Georges-Pierre Bonneau. "Sharp feature preserving MLS surface reconstruction based on local feature line approximations." Graphical Models 74, no. 6 (2012): 335–45. http://dx.doi.org/10.1016/j.gmod.2012.04.012.

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26

Seo, Kukhwan, Jongyeol Kim, Jongmin Yoon, and Kyusik Chung. "Comparison of Feature Performance and its Application to Feature Combination in Off-Line Handwritten Korean Alphabet Recognition." International Journal of Pattern Recognition and Artificial Intelligence 12, no. 02 (1998): 251–61. http://dx.doi.org/10.1142/s0218001498000178.

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This paper presents (1) a quantitative performance comparison of the features used in off-line handwritten Korean alphabet recognition, and (2) some experimental results based on the feature combination approach. We classified the features into three: geometrical/topological, statistical and global. For each class, we selected four or five features and performed recognition experiments with PE92 database using neural network classifiers. We compared their recognition performances and selected the top four among those thirteen features: Up Down Left Right Hole, Gradient, Structure, and Mesh. Wi

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27

Haris, Malik, and Adam Glowacz. "Lane Line Detection Based on Object Feature Distillation." Electronics 10, no. 9 (2021): 1102. http://dx.doi.org/10.3390/electronics10091102.

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In order to meet the real-time requirements of the autonomous driving system, the existing method directly up-samples the encoder’s output feature map to pixel-wise prediction, thus neglecting the importance of the decoder for the prediction of detail features. In order to solve this problem, this paper proposes a general lane detection framework based on object feature distillation. Firstly, a decoder with strong feature prediction ability is added to the network using direct up-sampling method. Then, in the network training stage, the prediction results generated by the decoder are regarded

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28

SIMON, J. C., and O. BARET. "REGULARITIES AND SINGULARITIES IN LINE PICTURES." International Journal of Pattern Recognition and Artificial Intelligence 05, no. 01n02 (1991): 57–77. http://dx.doi.org/10.1142/s0218001491000065.

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The main difficulty in pattern recognition is the interpretation of the iconic picture, which consists of pixels, into primitive features. The definition of a feature classification as regular or singular recalled and justified. These ideas are applied to line images, and particularly to handwritten words. A regular feature, the axis, is found on the graph representation of a word. It allows segmentation of the word representation and the obtaining of a descriptive chain. Examples of such symbolic descriptions are given with their interpretations as words from a list of 25. The results show 87

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29

Ferreira, Johnny Maikeo, Silvia Regina Vergilio, and Marcos Quinaia. "Software Product Line Testing Based on Feature Model Mutation." International Journal of Software Engineering and Knowledge Engineering 27, no. 05 (2017): 817–39. http://dx.doi.org/10.1142/s0218194017500309.

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The Feature Model (FM) is a fundamental artifact of the Software Product Line (SPL) engineering, used to represent commonalities and variabilities, and also to derive products for testing. However, the test of all features combinations (products) is not always possible in practice. Due to the growing complexity of the applications, only a subset of products is usually selected. The selection is generally based on combinatorial testing, to test features interactions. This kind of selection does not consider different classes of faults that can be present in the FM. The application of a fault-ba

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30

Li, Zeyu, Jinling Wang, Kai Chen, and Yu Sun. "New Environmental Line Feature-based Vision Navigation: Design and Analysis." Journal of Navigation 70, no. 5 (2017): 1133–52. http://dx.doi.org/10.1017/s0373463317000224.

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Vision navigation using environmental features has been widely applied when satellite signals are not available. However, the matching performance of traditional environmental features such as keypoints degrades significantly in weakly textured areas, deteriorating navigation performance. Further, the user needs to evaluate and assure feature matching quality. In this paper, a new feature, named Line Segment Intersection Feature (LSIF), is proposed to solve the availability problem in weakly textured regions. Then a combined descriptor involving global structure and local gradient is designed

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31

Eyal Salman, Hamzeh, Abdelhak-Djamel Seriai, and Christophe Dony. "Feature-Level Change Impact Analysis Using Formal Concept Analysis." International Journal of Software Engineering and Knowledge Engineering 25, no. 01 (2015): 69–92. http://dx.doi.org/10.1142/s0218194015400045.

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Software Product Line Engineering (SPLE) is a systematic reuse approach to develop a short time-to-market and quality products, called Software Product Line (SPL). Usually, a SPL is not developed from scratch but it is developed by reusing features (resp. their implementing source code elements) of existing similar systems previously developed by ad-hoc reuse techniques. The features implementations that are reused may be changed for developing new products (SPL) using SPLE. Any code element can be a part of (shared by) different features implementations; modifying one feature's implementation

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32

Liang, Y., and Y. H. Sheng. "BUILDING FACADE MODELING UNDER LINE FEATURE CONSTRAINT BASED ON CLOSE-RANGE IMAGES." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-3 (April 30, 2018): 993–97. http://dx.doi.org/10.5194/isprs-archives-xlii-3-993-2018.

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To solve existing problems in modeling facade of building merely with point feature based on close-range images , a new method for modeling building facade under line feature constraint is proposed in this paper. Firstly, Camera parameters and sparse spatial point clouds data were restored using the SFM , and 3D dense point clouds were generated with MVS; Secondly, the line features were detected based on the gradient direction , those detected line features were fit considering directions and lengths , then line features were matched under multiple types of constraints and extracted from mult

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33

Yadav, Hitesh, and A. Charan Kumari. "Analysis of Features using Feature Model in Software Product Line: A Case Study." International Journal of Education and Management Engineering 8, no. 2 (2018): 48–57. http://dx.doi.org/10.5815/ijeme.2018.02.06.

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34

Altınçay, Hakan, and Zafer Erenel. "Avoiding the interpolation inaccuracy in nearest feature line classifier by spectral feature analysis." Pattern Recognition Letters 34, no. 12 (2013): 1372–80. http://dx.doi.org/10.1016/j.patrec.2013.04.018.

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35

Cheng, S., J. Yang, Z. Kang, and P. H. Akwensi. "A SCENE-ASSISTED POINT-LINE FEATURE BASED VISUAL SLAM METHOD FOR AUTONOMOUS FLIGHT IN UNKNOWN INDOOR ENVIRONMENTS." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-2/W13 (June 5, 2019): 777–83. http://dx.doi.org/10.5194/isprs-archives-xlii-2-w13-777-2019.

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<p><strong>Abstract.</strong> Since Global Navigation Satellite System may be unavailable in complex dynamic environments, visual SLAM systems have gained importance in robotics and its applications in recent years. The SLAM system based on point feature tracking shows strong robustness in many scenarios. Nevertheless, point features over images might be limited in quantity or not well distributed in low-textured scenes, which makes the behaviour of these approaches deteriorate. Compared with point features, line features as higher-dimensional features can provide more enviro

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36

Zhou, Fei, Limin Zhang, Chaolong Deng, and Xinyue Fan. "Improved Point-Line Feature Based Visual SLAM Method for Complex Environments." Sensors 21, no. 13 (2021): 4604. http://dx.doi.org/10.3390/s21134604.

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Traditional visual simultaneous localization and mapping (SLAM) systems rely on point features to estimate camera trajectories. However, feature-based systems are usually not robust in complex environments such as weak textures or obvious brightness changes. To solve this problem, we used more environmental structure information by introducing line segments features and designed a monocular visual SLAM system. This system combines points and line segments to effectively make up for the shortcomings of traditional positioning based only on point features. First, ORB algorithm based on local ada

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37

Jian, Shengchao, Xiangang Peng, Haoliang Yuan, Chun Sing Lai, and Loi Lei Lai. "Transmission Line Fault-Cause Identification Based on Hierarchical Multiview Feature Selection." Applied Sciences 11, no. 17 (2021): 7804. http://dx.doi.org/10.3390/app11177804.

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Fault-cause identification plays a significant role in transmission line maintenance and fault disposal. With the increasing types of monitoring data, i.e., micrometeorology and geographic information, multiview learning can be used to realize the information fusion for better fault-cause identification. To reduce the redundant information of different types of monitoring data, in this paper, a hierarchical multiview feature selection (HMVFS) method is proposed to address the challenge of combining waveform and contextual fault features. To enhance the discriminant ability of the model, an ε-d

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38

Xia, Xinghua, Xiaoyu Song, Fangun Luan, Jungang Zheng, Zhili Chen, and Xiaofu Ma. "Discriminative feature selection for on-line signature verification." Pattern Recognition 74 (February 2018): 422–33. http://dx.doi.org/10.1016/j.patcog.2017.09.033.

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39

Huang, Kai, and Hong Yan. "Off-line signature verification using structural feature correspondence." Pattern Recognition 35, no. 11 (2002): 2467–77. http://dx.doi.org/10.1016/s0031-3203(01)00222-9.

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40

Li, S. Z., and Juwei Lu. "Face recognition using the nearest feature line method." IEEE Transactions on Neural Networks 10, no. 2 (1999): 439–43. http://dx.doi.org/10.1109/72.750575.

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41

Du, Hao, and Yan Qiu Chen. "Rectified nearest feature line segment for pattern classification." Pattern Recognition 40, no. 5 (2007): 1486–97. http://dx.doi.org/10.1016/j.patcog.2006.10.021.

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42

Chen, Xijiang, and Kegen Yu. "Feature Line Generation and Regularization From Point Clouds." IEEE Transactions on Geoscience and Remote Sensing 57, no. 12 (2019): 9779–90. http://dx.doi.org/10.1109/tgrs.2019.2929138.

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43

Yuan Meng, 袁梦, 李艾华 Li Aihua, 郑勇 Zheng Yong, 崔智高 Cui Zhigao, and 鲍振强 Bao Zhengqiang. "Point-Line Feature Fusion in Monocular Visual Odometry." Laser & Optoelectronics Progress 55, no. 2 (2018): 021501. http://dx.doi.org/10.3788/lop55.021501.

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44

Ren Qiancheng, 任前程. "Method for Extraction of Point Cloud Feature Line." Laser & Optoelectronics Progress 56, no. 6 (2019): 062803. http://dx.doi.org/10.3788/lop56.062803.

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45

Kamaya, Hideyuki. "Double emission line feature from tiny cold clouds." Astrophysics and Space Science 291, no. 2 (2004): 185–91. http://dx.doi.org/10.1023/b:astr.0000032064.68168.1f.

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46

Lee, Jaejoon, and Dirk Muthig. "Feature-oriented variability management in product line engineering." Communications of the ACM 49, no. 12 (2006): 55–59. http://dx.doi.org/10.1145/1183236.1183266.

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47

Yanwei Pang, Yuan Yuan, and Xuelong Li. "Iterative Subspace Analysis Based on Feature Line Distance." IEEE Transactions on Image Processing 18, no. 4 (2009): 903–7. http://dx.doi.org/10.1109/tip.2008.2011167.

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48

Ye, Ruifang, Chia-Sheng Pan, Ming Chang, and Chia-Ping Hsieh. "In-line inspection of surface feature and defect." Microsystem Technologies 24, no. 8 (2017): 3233–40. http://dx.doi.org/10.1007/s00542-017-3678-0.

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49

Huang, Dong, and Jian Gao. "On-line Signature Verification Based on GA-SVM." International Journal of Online Engineering (iJOE) 11, no. 6 (2015): 49. http://dx.doi.org/10.3991/ijoe.v11i6.5122.

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With the development of pen-based mobile device, on-line signature verification is gradually becoming a kind of important biometrics verification. This thesis proposes a method of verification of on-line handwritten signatures using both Support Vector Data Description (SVM) and Genetic Algorithm (GA). A 27-parameter feature set including shape and dynamic features is extracted from the on-line signatures data. The genuine signatures of each subject are treated as target data to train the SVM classifier. As a kernel based one-class classifier, SVM can accurately describe the feature distributi

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50

Kawabata, Nobuo. "Attention and Depth Perception." Perception 15, no. 5 (1986): 563–72. http://dx.doi.org/10.1068/p150563.

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The Necker cube is a line drawing with two possible solutions in depth perception. The process of interpreting a two-dimensional line drawing as a three-dimensional object was investigated using the Necker cube. Attention was directed to a local feature of a briefly presented cube, ie an angle at a vertex. The attended angle was perceived as a front part of the cube and other parts were interpreted so as to match this interpretation. Results show that the local feature to which attention was directed was interpreted first and then global features and other local features were interpreted so as

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