Relevant bibliographies by topics / Quadrilateral detection

Academic literature on the topic 'Quadrilateral detection'

Author: Grafiati
Published: 10 December 2022
Last updated: 29 January 2023

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Journal articles on the topic "Quadrilateral detection"

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Gao, Pingping, and Yabin Gao. "Quadrilateral Interval Type-2 Fuzzy Regression Analysis for Data Outlier Detection." Mathematical Problems in Engineering 2019 (August 21, 2019): 1–9. http://dx.doi.org/10.1155/2019/4914593.

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This paper presents a fuzzy regression analysis method based on a general quadrilateral interval type-2 fuzzy numbers, regarding the data outlier detection. The Euclidean distance for the general quadrilateral interval type-2 fuzzy numbers is provided. In the sense of Euclidean distance, some parameter estimation laws of the type-2 fuzzy linear regression model are designed. Then, the data outlier detection-oriented parameter estimation method is proposed using the data deletion-based type-2 fuzzy regression model. Moreover, based on the fuzzy regression model, by using the root mean squared error method, an impact evaluation rule is designed for detecting data outlier. An example is finally provided to validate the presented methods.
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Wang, Mei, Zuo Peng Wang, and Yong Ling Chu. "Quadrilateral Extraction Method and its Application on Parking Cell Detection." Applied Mechanics and Materials 263-266 (December 2012): 2534–37. http://dx.doi.org/10.4028/www.scientific.net/amm.263-266.2534.

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This paper presents a quadrilate extraction method based on four vertices of the image object to solve the problrm of object area inaccuracy depending on conventional rectangular segmentation method. Firstly, four vertices are given by the mouse-click depending on quadilateral object spatial feature; and then the quadrilateral object shape feature is estimated by each side of the quadriangle computing by straight-line formulation of two adjacent vertices of object in order to acquare the quadrialeratal object inernal pixel. At last, the pixels of quadrialeratel external region are cleaned. Arbitrary quadrilateral region segmentation is achieved accurately. The method can be used for any quadrilateral region segmentation based on image processing, The experiments on actual color parking images show any parking cell template extraction accuracy reached 100% and parking cell state detection accuracy is 99.8%. The result verifies the algorithm effectiveness and robustnes
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Abu Hassan, Mohd Fadzil, Mohamad Hanif Md Saad, Mohd Faisal Ibrahim, and Aini Hussain. "A Finite State Machine Fall Detection Using Quadrilateral Shape Features." Bulletin of Electrical Engineering and Informatics 7, no. 3 (September 1, 2018): 359–66. http://dx.doi.org/10.11591/eei.v7i3.1184.

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A video-based fall detection system was presented; which consists of data acquisition, image processing, feature extraction, feature selection, classification and finite state machine. A two-dimensional human posture image was represented by 12 features extracted from the generalisation of a silhouette shape to a quadrilateral. The corresponding feature vectors for three groups of human pose were statistically analysed by using a non-parametric Kruskal Wallis test to assess the different significance level between them. From the statistical test, non-significant features were discarded. Four selected kernel-based Support Vector Machine: linear, quadratics, cubic and Radial Basis Function classifiers were trained to classify three human posture groups. Among four classifiers, the last one performed the best in terms of performance matric on testing set. The classifier outperformed others with high achievement ofaverage sensitivity, precision and F-score of 99.19%, 99.25% and 99.22%, respectively. Such pose classification model output was further used in a simple finite state machine to trigger the falling event alarms. The fall detection system was tested on different fall video sets and able to detect the presence offalling events in a frame sequence of videos with accuracy of 97.32% and low computional time.
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Patel, Ujjaval J., Nilesh G. Chothani, and Praghnesh J. Bhatt. "Adaptive quadrilateral distance relaying scheme for fault impedance compensation." Electrical, Control and Communication Engineering 14, no. 1 (July 1, 2018): 58–70. http://dx.doi.org/10.2478/ecce-2018-0007.

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Abstract Impedance reach of numerical distance relay is severely affected by Fault Resistance (RF), Fault Inception Angle (FIA), Fault Type (FT), Fault Location (FL), Power Flow Angle (PFA) and series compensation in transmission line. This paper presents a novel standalone adaptive distance protection algorithm for detection, classification and location of fault in presence of variable fault resistance. It is based on adaptive slope tracking method to detect and classify the fault in combination with modified Fourier filter algorithm for locating the fault. To realize the effectiveness of the proposed technique, simulations are performed in PSCAD using multiple run facility & validation is carried out in MATLAB® considering wide variation in power system disturbances. Due to adaptive setting of quadrilateral characteristics in accordance with variation in fault impedance, the proposed technique is 100 % accurate for detection & classification of faults with error in fault location estimation to be within 1 %. Moreover, the proposed technique provides significant improvement in response time and estimation of fault location as compared to existing distance relaying algorithms, which are the key attributes of multi-functional numerical relay
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Keserwani, Prateek, Ankit Dhankhar, Rajkumar Saini, and Partha Pratim Roy. "Quadbox: Quadrilateral Bounding Box Based Scene Text Detection Using Vector Regression." IEEE Access 9 (2021): 36802–18. http://dx.doi.org/10.1109/access.2021.3063030.

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Liu, Zhaoting, Longqing Zou, Xianglou Liu, Jiale Qiao, and Xiangbin Meng. "A point sound source location and detection method based on 19-element hemispheric distributed acoustic pressure sensor array." Insight - Non-Destructive Testing and Condition Monitoring 63, no. 8 (August 1, 2021): 479–87. http://dx.doi.org/10.1784/insi.2021.63.8.479.

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To solve the key problem of diagnosing the operating condition of an oil transfer pump unit in a 3D closed space, this paper presents an approach for a point sound source location and detection method based on a hemispheric distributed sound pressure sensor array. The array model consists of 19 sound pressure sensors acting in the radial direction and uniformly distributed over the hemispherical surface. A spatial rectangular coordinate system is established by taking the projection point of the central sensor arranged at the apex of the hemisphere to the ground as the origin of the spatial coordinates. With reference to the central sensor, the point sound source is located by selecting the maximum measured sound level and its spatial coordinate in each of the three layers of sensors surrounding it as parameters and using a triangular or a quadrilateral area location algorithm based on virtual instrument technology. According to the location of the source, the A-weighted sound level of the sound source point is derived by the inversion of the sound field distribution law. Results show that the triangular and quadrilateral area location algorithms are both effective. The errors in location become larger for a measured sound source far from the centre.
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Chen, YongQiang, Kai Luo, Liang Chen, Haobo Weng, and Wei Liang. "Application of edge detection based on hexagonal image structure to delamination detection of carbon fiber reinforced polymer material." Smart Materials and Structures 31, no. 4 (February 23, 2022): 045006. http://dx.doi.org/10.1088/1361-665x/ac5455.

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Abstract Delamination is one of the most critical damage modes in carbon fiber reinforced polymer (CFRP) materials, and is invisible to the naked eye. Although delamination defect images of CFRP materials can be obtained through ultrasonic tomography, it is still difficult to accurately identify the position and contours of defect images. In this study, four different delamination defect images of CFRP composite plates were obtained through a finite element simulation and fan-beam ultrasonic tomography. A hexagon algorithm based on interpolation is proposed that transforms the reconstructed defect images from square pixels into hexagonal pixels. The interpolation algorithm is based on the overlap between the square and hexagonal pixels. As the experiment results verify, the use of the hexagonal structure-based morphological method for edge detection significantly reduces the recognition error from 7.74% to 0.148% in comparison with a traditional quadrilateral structure. The experimental results also showed that this method can effectively reduce the artifact interference and obtain complete and accurate target edge information more effectively than a square structure.
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Zhao, Wen Dong, Hui Qi, and Hai Yan Zhou. "Segmentation Algorithm of Traffic Prohibited Area Based on Wavelet." Advanced Materials Research 542-543 (June 2012): 1316–19. http://dx.doi.org/10.4028/www.scientific.net/amr.542-543.1316.

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The status of transportation industry development of China has shown the necessity and urgency of the development of intelligent transportation systems. This article proposed a segmentation algorithm of traffic prohibited region based on wavelet transform. Based on the de-noising and image enhancement, sharpening pretreatment of the traffic video image captured on real-time, the algorithm combines the method determining the quadrilateral based on the sample images manually with the image segmentation based on wavelet transform in order to get the segmentation of traffic prohibited region which will be used in the detection of vehicle pressing highway central line region. The experimental results show that in the algorithm not only meet the real-time requirement of foundations but also improves the successful rate of detection results.
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Zhao, Fan, Sidi Shao, Lin Zhang, and Zhiquan Wen. "A Straightforward and Efficient Instance-Aware Curved Text Detector." Sensors 21, no. 6 (March 10, 2021): 1945. http://dx.doi.org/10.3390/s21061945.

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A challenging aspect of scene text detection is to handle curved texts. In order to avoid the tedious manual annotations for training curve text detector, and to overcome the limitation of regression-based text detectors to irregular text, we introduce straightforward and efficient instance-aware curved scene text detector, namely, look more than twice (LOMT), which makes the regression-based text detection results gradually change from loosely bounded box to compact polygon. LOMT mainly composes of curve text shape approximation module and component merging network. The shape approximation module uses a particle swarm optimization-based text shape approximation method (called PSO-TSA) to fine-tune the quadrilateral text detection results to fit the curved text. The component merging network merges incomplete text sub-parts of text instances into more complete polygon through instance awareness, called ICMN. Experiments on five text datasets demonstrate that our method not only achieves excellent performance but also has relatively high speed. Ablation experiments show that PSO-TSA can solve the text’s shape optimization problem efficiently, and ICMN has a satisfactory merger effect.
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Li, Shuangshuang, and Wenming Cao. "SEMPANet: A Modified Path Aggregation Network with Squeeze-Excitation for Scene Text Detection." Sensors 21, no. 8 (April 9, 2021): 2657. http://dx.doi.org/10.3390/s21082657.

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Recently, various object detection frameworks have been applied to text detection tasks and have achieved good performance in the final detection. With the further expansion of text detection application scenarios, the research value of text detection topics has gradually increased. Text detection in natural scenes is more challenging for horizontal text based on a quadrilateral detection box and for curved text of any shape. Most networks have a good effect on the balancing of target samples in text detection, but it is challenging to deal with small targets and solve extremely unbalanced data. We continued to use PSENet to deal with such problems in this work. On the other hand, we studied the problem that most of the existing scene text detection methods use ResNet and FPN as the backbone of feature extraction, and improved the ResNet and FPN network parts of PSENet to make it more conducive to the combination of feature extraction in the early stage. A SEMPANet framework without an anchor and in one stage is proposed to implement a lightweight model, which is embodied in the training time of about 24 h. Finally, we selected the two most representative datasets for oriented text and curved text to conduct experiments. On ICDAR2015, the improved network’s latest results further verify its effectiveness; it reached 1.01% in F-measure compared with PSENet-1s. On CTW1500, the improved network performed better than the original network on average.
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Dissertations / Theses on the topic "Quadrilateral detection"

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POCHET, AXELLE DANY JULIETTE. "MODELING OF GEOBODIES: AI FOR SEISMIC FAULT DETECTION AND ALL-QUADRILATERAL MESH GENERATION." PONTIFÍCIA UNIVERSIDADE CATÓLICA DO RIO DE JANEIRO, 2018. http://www.maxwell.vrac.puc-rio.br/Busca_etds.php?strSecao=resultado&nrSeq=35861@1.

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PONTIFÍCIA UNIVERSIDADE CATÓLICA DO RIO DE JANEIRO
COORDENAÇÃO DE APERFEIÇOAMENTO DO PESSOAL DE ENSINO SUPERIOR
PROGRAMA DE EXCELENCIA ACADEMICA
A exploração segura de reservatórios de petróleo necessita uma boa modelagem numérica dos objetos geológicos da sub superfície, que inclui entre outras etapas: interpretação sísmica e geração de malha. Esta tese apresenta um estudo nessas duas áreas. O primeiro estudo é uma contribuição para interpretação de dados sísmicos, que se baseia na detecção automática de falhas sísmicas usando redes neurais profundas. Em particular, usamos Redes Neurais Convolucionais (RNCs) diretamente sobre mapas de amplitude sísmica, com a particularidade de usar dados sintéticos para treinar a rede com o objetivo final de classificar dados reais. Num segundo estudo, propomos um novo algoritmo para geração de malhas bidimensionais de quadrilaterais para estudos geomecânicos, baseado numa abordagem inovadora do método de quadtree: definimos novos padrões de subdivisão para adaptar a malha de maneira eficiente a qualquer geometria de entrada. As malhas obtidas podem ser usadas para simulações com o Método de Elementos Finitos (MEF).
Safe oil exploration requires good numerical modeling of the subsurface geobodies, which includes among other steps: seismic interpretation and mesh generation. This thesis presents a study in these two areas. The first study is a contribution to data interpretation, examining the possibilities of automatic seismic fault detection using deep learning methods. In particular, we use Convolutional Neural Networks (CNNs) on seismic amplitude maps, with the particularity to use synthetic data for training with the goal to classify real data. In the second study, we propose a new two-dimensional all-quadrilateral meshing algorithm for geomechanical domains, based on an innovative quadtree approach: we define new subdivision patterns to efficiently adapt the mesh to any input geometry. The resulting mesh is suited for Finite Element Method (FEM) simulations.
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Book chapters on the topic "Quadrilateral detection"

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Ferrão, José, Paulo Dias, and António J. R. Neves. "Detection of Aruco Markers Using the Quadrilateral Sum Conjuncture." In Lecture Notes in Computer Science, 363–69. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-93000-8_41.

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Conference papers on the topic "Quadrilateral detection"

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Bi, Yanguang, and Zhiqiang Hu. "Disentangled Contour Learning for Quadrilateral Text Detection." In 2021 IEEE Winter Conference on Applications of Computer Vision (WACV). IEEE, 2021. http://dx.doi.org/10.1109/wacv48630.2021.00095.

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Lee, Kai Ki, Ying Kin Yu, and Kin Hong Wong. "Multiple quadrilateral detection for projector-camera system applications." In 2015 IEEE 10th Conference on Industrial Electronics and Applications (ICIEA). IEEE, 2015. http://dx.doi.org/10.1109/iciea.2015.7334431.

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Fan, Jian. "Detection of quadrilateral document regions from digital photographs." In 2016 IEEE Winter Conference on Applications of Computer Vision (WACV). IEEE, 2016. http://dx.doi.org/10.1109/wacv.2016.7477661.

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Yang, Eun-Sung, and Gon-Woo Kim. "Robust quadrilateral detection method for using rectangle feature." In 2017 14th International Conference on Ubiquitous Robots and Ambient Intelligence (URAI). IEEE, 2017. http://dx.doi.org/10.1109/urai.2017.7992748.

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Liu, Yuliang, Sheng Zhang, Lianwen Jin, Lele Xie, Yaqiang Wu, and Zhepeng Wang. "Omnidirectional Scene Text Detection with Sequential-free Box Discretization." In Twenty-Eighth International Joint Conference on Artificial Intelligence {IJCAI-19}. California: International Joint Conferences on Artificial Intelligence Organization, 2019. http://dx.doi.org/10.24963/ijcai.2019/423.

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Scene text in the wild is commonly presented with high variant characteristics. Using quadrilateral bounding box to localize the text instance is nearly indispensable for detection methods. However, recent researches reveal that introducing quadrilateral bounding box for scene text detection will bring a label confusion issue which is easily overlooked, and this issue may significantly undermine the detection performance. To address this issue, in this paper, we propose a novel method called Sequential-free Box Discretization (SBD) by discretizing the bounding box into key edges (KE) which can further derive more effective methods to improve detection performance. Experiments showed that the proposed method can outperform state-of-the-art methods in many popular scene text benchmarks, including ICDAR 2015, MLT, and MSRA-TD500. Ablation study also showed that simply integrating the SBD into Mask R-CNN framework, the detection performance can be substantially improved. Furthermore, an experiment on the general object dataset HRSC2016 (multi-oriented ships) showed that our method can outperform recent state-of-the-art methods by a large margin, demonstrating its powerful generalization ability.
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Zhang, Yang, Yucan Chi, and Yongsheng Fan. "Highly Adaptive Ship Detection Based on Arbitrary Quadrilateral Bounding Box." In 2022 IEEE International Conference on Unmanned Systems (ICUS). IEEE, 2022. http://dx.doi.org/10.1109/icus55513.2022.9986765.

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Zhang, Jinfeng, Zhibin Jiao, Xiangjing An, and Yejun He. "Real-time Quadrilateral Object Corner Detection Algorithm Based on Deep Learning." In 2019 Computing, Communications and IoT Applications (ComComAp). IEEE, 2019. http://dx.doi.org/10.1109/comcomap46287.2019.9018794.

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Zhang, Qiang, Daokui Qu, Fang Xu, Kai Jia, Nan Jiang, and Fengshan Zou. "An improved method for object instance detection based on object center estimation and convex quadrilateral verification." In 2016 IEEE Information Technology, Networking, Electronic and Automation Control Conference (ITNEC). IEEE, 2016. http://dx.doi.org/10.1109/itnec.2016.7560343.

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Xue, Chuhui, Shijian Lu, and Wei Zhang. "MSR: Multi-Scale Shape Regression for Scene Text Detection." In Twenty-Eighth International Joint Conference on Artificial Intelligence {IJCAI-19}. California: International Joint Conferences on Artificial Intelligence Organization, 2019. http://dx.doi.org/10.24963/ijcai.2019/139.

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State-of-the-art scene text detection techniques predict quadrilateral boxes that are prone to localization errors while dealing with straight or curved text lines of different orientations and lengths in scenes. This paper presents a novel multi-scale shape regression network (MSR) that is capable of locating text lines of different lengths, shapes and curvatures in scenes. The proposed MSR detects scene texts by predicting dense text boundary points that inherently capture the location and shape of text lines accurately and are also more tolerant to the variation of text line length as compared with the state of the arts using proposals or segmentation. Additionally, the multi-scale network extracts and fuses features at different scales which demonstrates superb tolerance to the text scale variation. Extensive experiments over several public datasets show that the proposed MSR obtains superior detection performance for both curved and straight text lines of different lengths and orientations.
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Keaton, Jeffrey R., and Richard W. Gailing. "Monitoring Slope Deformation With Quadrilaterals for Pipeline Risk Management." In 2004 International Pipeline Conference. ASMEDC, 2004. http://dx.doi.org/10.1115/ipc2004-0197.

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Ground displacements, strains, and tilts can be calculated by repeated measurements of the lengths of six chords and relative elevations of an array of four points, known as a quadrilateral. Quadrilateral measurements allow ground-surface deformation and strain to be calculated. Typically, soil-pipeline interaction results in pipeline strain being less than ground strain. Strain gauges traditionally have been used on pipelines in landslide areas to aid in managing pipeline risk. Quadrilaterals may be economical alternatives to placing strain gauges on existing pipelines in areas of active or potential slope movements. A threshold ground deformation or strain is used to trigger more expensive means of evaluating pipeline integrity. Quadrilaterals are relatively inexpensive to install, but must be carefully located and founded deep enough to avoid seasonal shrink-swell effects of the soil. Measurements must be taken with precise instruments (tape extensometer) so that small changes can be detected with acceptable errors. Three contiguous quadrilaterals were installed in Spring 2003 in a landslide-prone area of southern California to aid in monitoring a slope between the main scarp of a recently active landslide and a pipeline bridge foundation. Engineering geologic evaluation supported a conclusion that the rate of headward crest advancement would be slow, but a method of detecting and quantifying slope deformation was needed for operational risk management.
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