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Relevant bibliographies by topics / Bidirectional matching

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Academic literature on the topic 'Bidirectional matching'

Author: Grafiati

Published: 4 June 2021

Last updated: 1 February 2022

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Journal articles on the topic "Bidirectional matching"

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Liu, XiangShao, Shangbo Zhou, Hua Li, and Kun Li. "Bidirectional scale-invariant feature transform feature matching algorithms based on priority k-d tree search." International Journal of Advanced Robotic Systems 14, no. 1 (December 19, 2016): 172988141668270. http://dx.doi.org/10.1177/1729881416682700.

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In this article, a bidirectional feature matching algorithm and two extended algorithms based on the priority k-d tree search are presented for the image registration using scale-invariant feature transform features. When matching precision of image registration is below 50%, the discarding wrong match performance of many robust fitting methods like Random Sample Consensus (RANSAC) is poor. Therefore, improving matching precision is a significant work. Generally, a feature matching algorithm is used once in the image registration system. We propose a bidirectional algorithm that utilizes the priority k-d tree search twice to improve matching precision. There are two key steps in the bidirectional algorithm. According to the case of adopting the ratio restriction of distances in the two key steps, we further propose two extended bidirectional algorithms. Experiments demonstrate that there are some special properties of these three bidirectional algorithms, and the two extended algorithms can achieve higher precisions than previous feature matching algorithms.

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Gai, Rong Li, Fei Gao, Li Ming Duan, Xiao Hui Sun, and Hong Zheng Li. "Bidirectional Maximal Matching Word Segmentation Algorithm with Rules." Advanced Materials Research 926-930 (May 2014): 3368–72. http://dx.doi.org/10.4028/www.scientific.net/amr.926-930.3368.

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Bidirectional maximum matching algorithm (BMM) combined positive maximal matching and reverse maximal matching algorithm, it was a more commonly used word segmentation method now, but it was low efficient and couldn’t solve the ambiguity. Therefore, an improved method was proposed combining with improved dictionary structure, and changing maximal matching word length dynamically to improve the efficiency of word segmentation. In order to get the correct segmentation results, we also proposed several rules. Compared with traditional segmentation methods, it proves that bidirectional maximal matching word segmentation with rules has higher speed and precision.

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Belhadef, Hacene. "A New Bidirectional Method for Ontologies Matching." Procedia Engineering 23 (2011): 558–64. http://dx.doi.org/10.1016/j.proeng.2011.11.2547.

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Kucherov, Gregory, Kamil Salikhov, and Dekel Tsur. "Approximate string matching using a bidirectional index." Theoretical Computer Science 638 (July 2016): 145–58. http://dx.doi.org/10.1016/j.tcs.2015.10.043.

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Schnattinger, Thomas, Enno Ohlebusch, and Simon Gog. "Bidirectional search in a string with wavelet trees and bidirectional matching statistics." Information and Computation 213 (April 2012): 13–22. http://dx.doi.org/10.1016/j.ic.2011.03.007.

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Dou, Jian Fang, and Jian Xun Li. "Automatic Image Mosaic Based on SIFT Using Bidirectional Matching." Advanced Materials Research 457-458 (January 2012): 841–47. http://dx.doi.org/10.4028/www.scientific.net/amr.457-458.841.

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In the paper, an image mosaic algorithm based on SIFT feature matching is proposed. For an image mosaic method based on feature matching, feature detection is needed to perform in each image. Thus a rapid detection operator is essential to the efficiency of the whole algorithm. In this paper, we use SIFT to extract features. The extracted features are matched by k-d tree and bidirectional matching strategy to enhance the accuracy of matching. Then, a RANSAC algorithm is applied to eliminate outliers to ensure effectiveness of the matching. Finally images are stitched by weighted average blending algorithm. The presented algorithm overcomes the disadvantages of the traditional image mosaic methods which are susceptible to different scale and moving objects, and can achieve sub-pixel accuracy of matching and algorithm is still available to the images at different scale. Experimental results show that the method with strong robustness performs effectively.

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Jia, Xiaogang, Wei Chen, and Zhengfa Liang. "Bidirectional Stereo Matching Network With Double Cost Volumes." IEEE Access 9 (2021): 19651–58. http://dx.doi.org/10.1109/access.2021.3050540.

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Chala, Sisay Adugna, Fazel Ansari, Madjid Fathi, and Kea Tijdens. "Semantic matching of job seeker to vacancy: a bidirectional approach." International Journal of Manpower 39, no. 8 (November 5, 2018): 1047–63. http://dx.doi.org/10.1108/ijm-10-2018-0331.

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Purpose The purpose of this paper is to propose a framework of an automatic bidirectional matching system that measures the degree of semantic similarity of job-seeker qualifications and skills, against the vacancy provided by employers or job-agents. Design/methodology/approach The paper presents a framework of bidirectional jobseeker-to-vacancy matching system. Using occupational data from various sources such as the WageIndicator web survey, International Standard Classification of Occupations, European Skills, Competences, Qualifications, and Occupations as well as vacancy data from various open access internet sources and job seekers information from social networking sites, the authors apply machine learning techniques for bidirectional matching of job vacancies and occupational standards to enhance the contents of job vacancies and job seekers profiles. The authors also apply bidirectional matching of job seeker profiles and vacancies, i.e., semantic matching vacancies to job seekers and vice versa in the individual level. Moreover, data from occupational standards and social networks were utilized to enhance the relevance (i.e. degree of similarity) of job vacancies and job seekers, respectively. Findings The paper provides empirical insights of increase in job vacancy advertisements on the selected jobs – Internet of Things – with respect to other job vacancies, and identifies the evolution of job profiles and its effect on job vacancies announcements in the era of Industry 4.0. In addition, the paper shows the gap between job seeker interests and available jobs in the selected job area. Research limitations/implications Due to limited data about jobseekers, the research results may not guarantee high quality of recommendation and maturity of matching results. Therefore, further research is required to test if the proposed system works for other domains as well as more diverse data sets. Originality/value The paper demonstrates how online jobseeker-to-vacancy matching can be improved by use of semantic technology and the integration of occupational standards, web survey data, and social networking data into user profile collection and matching.

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Dou, Jian Fang, and Jian Xun Li. "Automatic Image Mosaic Based on SIFT Using Bidirectional Matching." Advanced Materials Research 457-458 (January 2012): 841–47. http://dx.doi.org/10.4028/scientific5/amr.457-458.841.

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Kwok-Wai Cheung, Dit-Yan Yeung, and R. T. Chin. "Bidirectional deformable matching with application to handwritten character extraction." IEEE Transactions on Pattern Analysis and Machine Intelligence 24, no. 8 (August 2002): 1133–39. http://dx.doi.org/10.1109/tpami.2002.1024135.

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Dissertations / Theses on the topic "Bidirectional matching"

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Chala, Sisay Adugna [Verfasser]. "Bidirectional job matching through unsupervised feature learning / Sisay Adugna Chala." Siegen : Universitätsbibliothek der Universität Siegen, 2018. http://d-nb.info/1154308588/34.

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Iqbal, Rashid. "Hardware bidirectional real time motion estimator on a Xilinx Virtex II Pro FPGA." Thesis, Linköping University, Department of Electrical Engineering, 2006. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-6355.

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This thesis describes the implementation of a real-time, full search, 16x16 bidirectional motion estimation at 24 frames per second with the record performance of 155 Gop/s (1538 ops/pixel) at a high clock rate of 125 MHz. The core of bidirectional motion estimation uses close to 100% FPGA resources with 7 Gbit/s bandwidth to external memory. The architecture allows extremely controlled, macro level floor-planning with parameterized block size, image size, placement coordinates and data words length. The FPGA chip is part of the board that was developed at the Institute of Computer & Communication Networking Engineering, Technical University Braunschweig Germany, in collaboration with Grass Valley Germany in the FlexFilm research project. The goal of the project was to develop hardware and programming methodologies for real-time digital film image processing. Motion estimation core uses FlexWAFE reconfigurable architecture where FPGAs are configured using macro components that consist of weakly programmable address generation units and data stream processing units. Bidirectional motion estimation uses two cores of motion estimation engine (MeEngine) forming main data processing unit for backward and forward motion vectors. The building block of the core of motion estimation is an RPM-macro which represents one processing element and performs 10-bit difference, a comparison, and 19-bit accumulation on the input pixel streams. In order to maximize the throughput between elements, the processing element is replicated and precisely placed side-by-side by using four hierarchal levels, where each level is a very compact entity with its own local control and placement methodology. The achieved speed was further improved by regularly inserting pipeline stages in the processing chain.

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"影响融资租赁公司与承租中小企业的双向匹配的因素." Doctoral diss., 2019. http://hdl.handle.net/2286/R.I.53536.

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abstract: 本研究旨在讨论融资租赁公司与承租的中小企之间的匹配因素。研究从融资租赁的实际业务流程切入，研究1对H公司进行了案例分析，得到基本的影响因素结果，继而研究2和研究3分别在中小企客户和融资租赁公司两类资料中独立展开分析，并比较这些因素的影响程度。研究结果发现了影响双向匹配的四个维度，以及在各自影响力的不同。研究最后分别对融资租赁公司和承租中小企提出了建议，以期提高双方匹配并达成业务的概率。
Dissertation/Thesis
Doctoral Dissertation Business Administration 2019

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Book chapters on the topic "Bidirectional matching"

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Kucherov, Gregory, Kamil Salikhov, and Dekel Tsur. "Approximate String Matching Using a Bidirectional Index." In Combinatorial Pattern Matching, 222–31. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-07566-2_23.

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Schnattinger, Thomas, Enno Ohlebusch, and Simon Gog. "Bidirectional Search in a String with Wavelet Trees." In Combinatorial Pattern Matching, 40–50. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-13509-5_5.

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Maaß, Moritz G. "Linear Bidirectional On-Line Construction of Affix Trees." In Combinatorial Pattern Matching, 320–34. Berlin, Heidelberg: Springer Berlin Heidelberg, 2000. http://dx.doi.org/10.1007/3-540-45123-4_27.

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Gog, Simon, Kalle Karhu, Juha Kärkkäinen, Veli Mäkinen, and Niko Välimäki. "Multi-pattern Matching with Bidirectional Indexes." In Lecture Notes in Computer Science, 384–95. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-32241-9_33.

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Wu, Zhenxing, Jingling Wang, Chuanzhen Li, Yue Yan, and Chen Chu. "Bidirectional Matching Algorithm for Target Tracking Based on SIFT." In Advanced Research on Computer Education, Simulation and Modeling, 253–58. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-21783-8_42.

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Cui, Ge, Chunlin Ma, and Xin Wang. "Sequence-Based Bidirectional Merge Map-Matching Algorithm for Simplified Road Network." In Advances in Artificial Intelligence, 148–54. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-57351-9_19.

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Giunchiglia, Fausto. "Matching Classifications via a Bidirectional Integration of SAT and Linguistic Resources." In Frontiers of Combining Systems, 284. Berlin, Heidelberg: Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11559306_16.

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Zhang, Lingxiao, Jiangpeng Yan, Yujiu Yang, and Li Xiu. "Match4Rec: A Novel Recommendation Algorithm Based on Bidirectional Encoder Representation with the Matching Task." In Neural Information Processing, 491–503. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-63836-8_41.

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Joshi, Kapil, Minakshi Memoria, Laxman Singh, Parag Verma, and Archana Barthwal. "Multi-Modality Medical Image Fusion Using SWT & Speckle Noise Reduction with Bidirectional Exact Pattern Matching Algorithm." In Disruptive Technologies for Society 5.0, 339–59. Boca Raton: CRC Press, 2021. http://dx.doi.org/10.1201/9781003154686-20.

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Fathalla, Said. "Detecting Human Diseases Relatedness." In Data Analytics in Medicine, 471–86. IGI Global, 2020. http://dx.doi.org/10.4018/978-1-7998-1204-3.ch026.

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Due to the ubiquitous availability of the information on the web, there is a great need for a standardized representation of this information. Therefore, developing an efficient algorithm for retrieving information from knowledge graphs is a key challenge for many semantic web applications. This article presents spreading activation over ontology (SAOO) approach in order to detect the relatedness between two human diseases by applying spreading activation algorithm based on bidirectional search technique. The proposed approach detects two diseases relatedness by considering semantic domain knowledge. The methodology of the proposed work is divided into two phases: Semantic Matching and Diseases Relatedness Detection. In semantic matching, diseases within the user-submitted query are semantically identified in the ontology graph. In diseases relatedness detection, the relatedness between the two diseases is detected by using bidirectional-based spreading activation on the ontology graph. The classification of these diseases is provided as well.

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Conference papers on the topic "Bidirectional matching"

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Sorgi, Lorenzo, and Alessandro Neri. "Bidirectional Dynamic Programming for Stereo Matching." In 2006 International Conference on Image Processing. IEEE, 2006. http://dx.doi.org/10.1109/icip.2006.312671.

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Wang, Song, Xin Guo, Xiaomin Mu, and Lin Qi. "Bidirectional weight graph transformation matching algorithm." In 2014 International Conference on Audio, Language and Image Processing (ICALIP). IEEE, 2014. http://dx.doi.org/10.1109/icalip.2014.7009888.

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Hong-Yi Huang, Ruei-Iun Pu, and Ming-Ta Lee. "Simultaneous bidirectional transceiver with impedance matching." In 2008 15th IEEE International Conference on Electronics, Circuits and Systems - (ICECS 2008). IEEE, 2008. http://dx.doi.org/10.1109/icecs.2008.4674853.

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Siegl, Inge, Markus Haberler, and Christoph Steffan. "Matching Considerations for Bidirectional Current Mirrors." In 2019 Austrochip Workshop on Microelectronics (Austrochip). IEEE, 2019. http://dx.doi.org/10.1109/austrochip.2019.00023.

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Mine, Tsunenori, Tomoyuki Kakuta, and Akira Ono. "Reciprocal Recommendation for Job Matching with Bidirectional Feedback." In 2013 IIAI International Conference on Advanced Applied Informatics (IIAIAAI). IEEE, 2013. http://dx.doi.org/10.1109/iiai-aai.2013.91.

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Zhang, Peng-peng, Sheng-zheng Wang, Yu Qiao, Jie Yang, and Yong-hui Gao. "Affine SoftAssign with bidirectional distance for point matching." In 2013 20th IEEE International Conference on Image Processing (ICIP). IEEE, 2013. http://dx.doi.org/10.1109/icip.2013.6738261.

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Kim, Doojung, and Byung-Wook Min. "C-band bidirectional amplifier with switchable matching circuits." In 2015 IEEE Radio Frequency Integrated Circuits Symposium (RFIC). IEEE, 2015. http://dx.doi.org/10.1109/rfic.2015.7337783.

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Newman, Timothy S., and Anil K. Jain. "Bidirectional template matching for 3D CAD-based inspection." In IS&T/SPIE 1994 International Symposium on Electronic Imaging: Science and Technology, edited by Benjamin M. Dawson, Stephen S. Wilson, and Frederick Y. Wu. SPIE, 1994. http://dx.doi.org/10.1117/12.171215.

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Batsos, Konstantinos, Changjiang Cai, and Philippos Mordohai. "CBMV: A Coalesced Bidirectional Matching Volume for Disparity Estimation." In 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, 2018. http://dx.doi.org/10.1109/cvpr.2018.00220.

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Shaobo Zhang, Sheng Liu, Yuhang Mao, and Xin Wang. "Global optimization for bidirectional stereo matching with occlusion handling." In 2012 International Conference on Measurement, Information and Control (MIC). IEEE, 2012. http://dx.doi.org/10.1109/mic.2012.6273446.

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