Relevant bibliographies by topics / Speeded up robust features (SURF)

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  1. Journal articles
  2. Dissertations / Theses
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  4. Conference papers

Academic literature on the topic 'Speeded up robust features (SURF)'

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Published: 4 June 2025
Last updated: 1 August 2025

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Journal articles on the topic "Speeded up robust features (SURF)"

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Bay, Herbert, Andreas Ess, Tinne Tuytelaars, and Luc Van Gool. "Speeded-Up Robust Features (SURF)." Computer Vision and Image Understanding 110, no. 3 (2008): 346–59. http://dx.doi.org/10.1016/j.cviu.2007.09.014.

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Wang, Yin Tien, Chen Tung Chi, and Ying Chieh Feng. "Robot Simultaneous Localization and Mapping Using Speeded-Up Robust Features." Applied Mechanics and Materials 284-287 (January 2013): 2142–46. http://dx.doi.org/10.4028/www.scientific.net/amm.284-287.2142.

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An algorithm for robot mapping is proposed in this paper using the method of speeded-up robust features (SURF). Since SURFs are scale- and orientation-invariant features, they have higher repeatability than that of the features obtained by other detection methods. Even in the cases of using moving camera, the SURF method can robustly extract the features from image sequences. Therefore, SURFs are suitable to be utilized as the map features in visual simultaneous localization and mapping (SLAM). In this article, the procedures of detection and matching of the SURF method are modified to improve
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Zhang, Jianguang, Yongxia Li, An Tai, Xianbin Wen, and Jianmin Jiang. "Motion Video Recognition in Speeded-Up Robust Features Tracking." Electronics 11, no. 18 (2022): 2959. http://dx.doi.org/10.3390/electronics11182959.

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Motion video recognition has been well explored in applications of computer vision. In this paper, we propose a novel video representation, which enhances motion recognition in videos based on SURF (Speeded-Up Robust Features) and two filters. Firstly, the detector scheme of SURF is used to detect the candidate points of the video because it is an efficient faster local feature detector. Secondly, by using the optical flow field and trajectory, the feature points can be filtered from the candidate points, which enables a robust and efficient extraction of motion feature points. Additionally, w
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Wang, Yin-Tien, and Guan-Yu Lin. "Improvement of speeded-up robust features for robot visual simultaneous localization and mapping." Robotica 32, no. 4 (2013): 533–49. http://dx.doi.org/10.1017/s0263574713000830.

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SUMMARYA robot mapping procedure using a modified speeded-up robust feature (SURF) is proposed for building persistent maps with visual landmarks in robot simultaneous localization and mapping (SLAM). SURFs are scale-invariant features that automatically recover the scale and orientation of image features in different scenes. However, the SURF method is not originally designed for applications in dynamic environments. The repeatability of the detected SURFs will be reduced owing to the dynamic effect. This study investigated and modified SURF algorithms to improve robustness in representing vi
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Prajakta, H. Umale Chanchal H. Sahani Aboli S. Patil Anisha A. Gedam Kajal V. Kawale Prof. Aditya Turankar. "Planer Object Detection Using Sift and Surf in Image Processing." International Journal of Research in Computer & Information Technology 7, no. 2 (2022): 31–34. https://doi.org/10.5281/zenodo.6676111.

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Object Detection refers to the capability of computers and software to locate objects in an image/scene and identify each object. Object detection is a computer vision technique that works to identify and locate objects within an image or video. In this study, we compare and analyze Scale-invariant feature transform (SIFT) and speeded-up robust features (SURF) and propose various geometric transformations. To increase the accuracy, the proposed system firstly performs the separation of the image by reducing the pixel size, using the Scale-invariant feature transform (SIFT). Then the key points
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Hariprasath., S., S.M GiriRajkumar., Yahya. A. Mohamed, Krishna. M. Hari, and Kumaran. K. Krishna. "Object Detection using SURF features." International Journal of Multidisciplinary Research Transactions 5, no. 7 (2023): 110–16. https://doi.org/10.5281/zenodo.7933324.

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A common method for locating items in photos is object detection utilising the Speeded-Up Robust Features (SURF) algorithm. In order to identify the existence of a certain object, this method pulls important details from an image and compares them to a learned collection of features. The algorithm used in this method can identify items even when they are rotated or partially obscured. The SURF technique is particularly helpful in computer vision applications where object detection is crucial, such as facial recognition and autonomous vehicles. An overview of the SURF algorithm and its use in o
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Long, Xu Lin, Qiang Chen, and Jun Wei Bao. "Improvement of Image Mosaic Algorithm Based on SURF." Applied Mechanics and Materials 427-429 (September 2013): 1625–30. http://dx.doi.org/10.4028/www.scientific.net/amm.427-429.1625.

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The present study concerns about feature matching in image mosaic. In order to solve the problems of low accuracy and poor applicability in the traditional speeded up robust features algorithm, this paper presents an improved algorithm. Clustering algorithm based on density instead of random sample consensus method is used to eliminate mismatching pairs. The initial matching pairs are mapped onto a plane coordinate system, which can be regarded as points, by calculating the density of each point to extract the final matching pairs. The results show that this algorithm overcomes the limitations
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Umale, Prajakta, Aboli Patil, Chanchal Sahani, Anisha Gedam, and Kajal Kawale. "PLANER OBJECT DETECTION USING SURF AND SIFT METHOD." International Journal of Engineering Applied Sciences and Technology 6, no. 11 (2022): 36–39. http://dx.doi.org/10.33564/ijeast.2022.v06i11.008.

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Object Detection refers to the capability of computer and software to locate objects in an image/scene and identify each object. Object detection is a computer vision technique works to identify and locate objects within an image or video. In this study, we compare and analyze Scale-invariant feature transform (SIFT) and speeded up robust features (SURF) and propose a various geometric transformation. To increase the accuracy, the proposed system firstly performs the separation of the image by reducing the pixel size, using the Scale-invariant feature transform (SIFT). Then the key points are
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Edwin, Dr Anusha. "Identification of Cattle using Fuzzy Speeded up Robust Features (F-SURF)." International Journal of Research in Advent Technology 7, no. 4 (2019): 581–87. http://dx.doi.org/10.32622/ijrat.742019209.

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Jing Zhao, Jing Zhao. "Sports Motion Feature Extraction and Recognition Based on a Modified Histogram of Oriented Gradients with Speeded Up Robust Features." 電腦學刊 33, no. 1 (2022): 063–70. http://dx.doi.org/10.53106/199115992022023301007.

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<p>Traditional motion recognition methods can extract global features, but ignore the local features. And the obscured motion cannot be recognized. Therefore, this paper proposes a modified Histogram of oriented gradients (HOG) combining speeded up robust features (SURF) for sports motion feature extraction and recognition. This new method can fully extract the local and global features of the sports motion recognition. The new algorithm first adopts background subtraction to obtain the motion region. Direction controllable filter can effectively describe the motion edge features. The HO
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Dissertations / Theses on the topic "Speeded up robust features (SURF)"

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Jurgensen, Sean M. "The rotated speeded-up robust features algorithm (R-SURF)." Thesis, Monterey, California: Naval Postgraduate School, 2014. http://hdl.handle.net/10945/42653.

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Approved for public release; distribution is unlimited<br>Includes supplemental materials<br>Weaknesses in the Fast Hessian detector utilized by the speeded-up robust features (SURF) algorithm are examined in this research. We evaluate the SURF algorithm to identify possible areas for improvement in the performance. A proposed alternative to the SURF detector is proposed called rotated SURF (R-SURF). This method utilizes filters that are rotated 45 degrees counter-clockwise, and this modification is tested with standard detector testing methods against the regular SURF detector. Performance te
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Brykt, Andreas. "A testbed for distributed detection ofkeypoints and extraction of descriptors forthe Speeded-Up-Robust-Features (SURF)algorithm." Thesis, KTH, Kommunikationsnät, 2013. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-141475.

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Detecting keypoints and computing descriptors needed in an imagerecognition algorithm are tasks that require substantial processing powerif they are to be executed in a short time span. If a network of sensornodes is used to capture the images to be processed, then the sensor nodescould be used to perform the actual processing. The system would dis-tribute the computing tasks to the available nodes in the network, so thatthe computing load can be divided among the nodes. By this, the com-puting time could possibly still be kept low, despite the large differencein available computing power betw
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Zavalina, Viktoriia. "Identifikace objektů v obraze." Master's thesis, Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií, 2014. http://www.nusl.cz/ntk/nusl-220364.

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Master´s thesis deals with methods of objects detection in the image. It contains theoretical, practical and experimental parts. Theoretical part describes image representation, the preprocessing image methods, and methods of detection and identification of objects. The practical part contains a description of the created programs and algorithms which were used in the programs. Application was created in MATLAB. The application offers intuitive graphical user interface and three different methods for the detection and identification of objects in an image. The experimental part contains a test
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Chen, Chung-Ying, and 陳俊穎. "SURF (Speeded-Up Robust Features) Based Visual Localization for a Mobile Robot." Thesis, 2013. http://ndltd.ncl.edu.tw/handle/63955133610815789402.

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碩士<br>國立中興大學<br>機械工程學系所<br>101<br>In this thesis, we consider the application of a visual based localization and mapping algorithm for a 4-Mecanum omni-directional mobile robot. Digital images are took by a camera in different time and pose. Using basic image processing technology and Speeded-Up Robust Features (SURF) detector, the coordinates of some feature points in an image are extracted, and the feature points detected in successive different images, projected from the same scene points are then matched. Based on the matched feature points between two successive images and using the Struc
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Cai, Zong-Han, and 蔡宗翰. "Depth Measurement Based on Pixel Number Variation and Speeded Up Robust Features (SURF)." Thesis, 2011. http://ndltd.ncl.edu.tw/handle/26750087644131777790.

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碩士<br>國立臺灣師範大學<br>應用電子科技學系<br>100<br>This paper presents a method for depth measurement based on Speeded Up Robust Features (SURF) and pixel number variation of CCD Images. A single camera is used to capture two images in different photographing distances, where speeded up robust features in the images are extracted and matched. To remove mismatches from given putative point correspondences, an Identifying point correspondences by Correspondence Function (ICF) method is adopted in order to automatically select better reference points required by the pixel number variation method. Based on the
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Zeng, Ruo-han, and 曾若涵. "Feature extraction and matching of finger-vein pat-terns based on SURF(Speeded Up Robust Features)." Thesis, 2012. http://ndltd.ncl.edu.tw/handle/9zy2j8.

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碩士<br>國立臺灣科技大學<br>資訊工程系<br>100<br>In 20th century, electronic information let people have convenient life in the Global village. After 21s century, life become blend of intelligent technology gradually, but humans just not use intelligent technology to get information in a network, they also want use intelligent technology to create more convenient and comfortable life. If the technology is closely related to the life, then the technology safe is particularly important. Biometrics recognition is very popular in recent years, that use humans face、iris、voice、fingerprint to recognize, and the
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Pranata, Yoga Dwi, and 优嘉逸. "Deep Learning and Speeded Up Robust Features (SURF) for Calcaneus Fracture Classification and Detection on CT Images." Thesis, 2017. http://ndltd.ncl.edu.tw/handle/e6z4yr.

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碩士<br>國立中央大學<br>資訊工程學系在職專班<br>105<br>Calcaneus, also called as heel bone, is the largest tarsal bone that forms the rear part of the foot. Cuboid bone articulates with its anterior and superior sides together with talus bone. Calcaneus is known to be the most fracture prone tarsal bone. Calcaneal fractures represent only about 2% of all fractures but 60% of tarsal bones fractures[1]. Based on subtalar joint involvement, calcaneal fractures can be categorized into two types: intraarticular fracture and extraarticular fracture. Intraarticular fractures are more common where posterior talar artic
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Lin, Hung-Yang, and 林宏洋. "THE ENHANCEMENT OF THE SPEEDED-UP ROBUST FEATURE (SURF) ALGORITHM FOR DEPTH ESTIMATION THROUGH USING CHROMA INFORMATION." Thesis, 2019. http://ndltd.ncl.edu.tw/handle/gh72t3.

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碩士<br>大同大學<br>通訊工程研究所<br>107<br>The Speeded-Up Robust Feature (SURF) algorithm is an important algorithm commonly used in computer vision systems. The salient features of this algorithm includes: the largely reduced time for detecting feature points which are scale and rotation invariant. However, only luminance component of color images are used in the algorithm without taking full advantage of the information offered by color images, and thus resulting in the inadequate performance in locating feature points and mismatches of corresponding feature points. Therefore, this paper intends to mak
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Van, der Haar Dustin Terence. "Face recognition-based authentication and monitoring in video telecommunication systems." Thesis, 2012. http://hdl.handle.net/10210/5024.

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M.Sc. (Computer Science)<br>A video conference is an interactive meeting between two or more locations, facilitated by simultaneous two-way video and audio transmissions. People in a video conference, also known as participants, join these video conferences for business and recreational purposes. In a typical video conference, we should properly identify and authenticate every participant in the video conference, if information discussed during the video conference is confidential. This prevents unauthorized and unwanted people from being part of the conference and exposing any confidential in
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Liang, Jian-Kai, and 梁建凱. "Face Recognition based on Speeded-Up Robust Features." Thesis, 2018. http://ndltd.ncl.edu.tw/handle/8map55.

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碩士<br>國立臺北科技大學<br>電子工程系<br>106<br>In the decades, lots of neural network-based face recognition products have been announced due to the increased efficiency of Graphic Processing Unit (GPU). This kind of technology has been applied on many high-tech products like smartphone and so on. However, such neural network-based face recognition systems often suffer a highly computational burden due to the nonlinear nature of neural networks. As a result, finding a way to improve the run-time performance is the first to think about. In this dissertation, we apply the Facial Landmark and the Speeded Up R
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Book chapters on the topic "Speeded up robust features (SURF)"

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Bay, Herbert, Tinne Tuytelaars, and Luc Van Gool. "SURF: Speeded Up Robust Features." In Computer Vision – ECCV 2006. Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11744023_32.

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Fu, Jing, Xiaojun Jing, Songlin Sun, Yueming Lu, and Ying Wang. "C-SURF: Colored Speeded Up Robust Features." In Trustworthy Computing and Services. Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-35795-4_26.

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Zhang, Nan. "Computing Parallel Speeded-Up Robust Features (P-SURF) via POSIX Threads." In Emerging Intelligent Computing Technology and Applications. Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-04070-2_33.

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Jagadeeswari, M., C. S. Manikandababu, and M. Aiswarya. "Integral Images: Efficient Algorithms for Their Computation Systems of Speeded-Up Robust Features (Surf)." In Pervasive Computing and Social Networking. Springer Singapore, 2022. http://dx.doi.org/10.1007/978-981-16-5640-8_50.

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Janumala, Tabitha, and K. B. Ramesh. "Development of an Algorithm for Vertebrae Identification Using Speeded up Robost Features (SURF) Technique in Scoliosis X-Ray Images." In Advances in Intelligent Systems and Computing. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-51859-2_6.

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Karimah, Fathin Ulfah, and Agus Harjoko. "Classification of Batik Kain Besurek Image Using Speed Up Robust Features (SURF) and Gray Level Co-occurrence Matrix (GLCM)." In Communications in Computer and Information Science. Springer Singapore, 2017. http://dx.doi.org/10.1007/978-981-10-7242-0_7.

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Verma, Nishchal Kumar, Ankit Goyal, A. Harsha Vardhan, Rahul Kumar Sevakula, and Al Salour. "Object Matching Using Speeded Up Robust Features." In Proceedings in Adaptation, Learning and Optimization. Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-27000-5_34.

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Banwaskar, M. R., and A. M. Rajurkar. "Creating Video Summary Using Speeded Up Robust Features." In Applied Computer Vision and Image Processing. Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-4029-5_31.

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Kaur, Prabhjot, Nitin Kumar, and Maheep Singh. "Biometric-Based Key Handling Using Speeded Up Robust Features." In Lecture Notes in Networks and Systems. Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-19-9228-5_52.

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Kim, Minwoo, Deokho Kim, Kyungah Kim, and Won Woo Ro. "Efficient Descriptor-Filtering Algorithm for Speeded Up Robust Features Matching." In Lecture Notes in Electrical Engineering. Springer Berlin Heidelberg, 2014. http://dx.doi.org/10.1007/978-3-642-41674-3_19.

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Conference papers on the topic "Speeded up robust features (SURF)"

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Iparraguirre, Javier, Leandro Balmaceda, and Cristian Mariani. "Speeded-up robust features (SURF) as a benchmark for heterogeneous computers." In 2014 IEEE Biennial Congress of Argentina (ARGENCON). IEEE, 2014. http://dx.doi.org/10.1109/argencon.2014.6868545.

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Paul, Madhumita, Ram Kumar Karsh, and Fazal Ahmed Talukdar. "Image Hashing based on Shape Context and Speeded Up Robust Features (SURF)." In 2019 International Conference on Automation, Computational and Technology Management (ICACTM). IEEE, 2019. http://dx.doi.org/10.1109/icactm.2019.8776713.

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Zhao, Ming, and Shiyin Qin. "Socket connector recognition based on SVM with speeded up robust feature (SURF)." In Instruments (ICEMI). IEEE, 2009. http://dx.doi.org/10.1109/icemi.2009.5274714.

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Prinka and Vikas Wasson. "An efficient content based image retrieval based on speeded up robust features (SURF) with optimization technique." In 2017 2nd IEEE International Conference on Recent Trends in Electronics, Information & Communication Technology (RTEICT). IEEE, 2017. http://dx.doi.org/10.1109/rteict.2017.8256693.

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Arifin, Nizar Akbar, Budhi Irawan, and Casi Setianingsih. "Traffic sign recognition application using speeded-up robust features (SURF) and support vector machine (SVM) based on android." In 2017 IEEE Asia Pacific Conference on Wireless and Mobile (APWiMob). IEEE, 2017. http://dx.doi.org/10.1109/apwimob.2017.8284004.

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Abedin, Md Zainal, Prashengit Dhar, and Kaushik Deb. "Traffic Sign Recognition using SURF: Speeded up robust feature descriptor and artificial neural network classifier." In 2016 9th International Conference on Electrical and Computer Engineering (ICECE). IEEE, 2016. http://dx.doi.org/10.1109/icece.2016.7853890.

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Alfanindya, Alexandra, Noramiza Hashim, and Chikannan Eswaran. "Content Based Image Retrieval and Classification using speeded-up robust features (SURF) and grouped bag-of-visual-words (GBoVW)." In 2013 International Conference on Technology, Informatics, Management, Engineering & Environment (TIME-E 2013). IEEE, 2013. http://dx.doi.org/10.1109/time-e.2013.6611968.

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Kalia, Robin, Keun-Dong Lee, B. V. R. Samir, Sung-Kwan Je, and Weon-Geun Oh. "An analysis of the effect of different image preprocessing techniques on the performance of SURF: Speeded Up Robust Features." In 2011 17th Korea-Japan Joint Workshop on Frontiers of Computer Vision (FCV2011). IEEE, 2011. http://dx.doi.org/10.1109/fcv.2011.5739756.

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Do, Huan N., and Jongeun Choi. "Appearance-Based Outdoor Localization Using Group LASSO Regression." In ASME 2015 Dynamic Systems and Control Conference. American Society of Mechanical Engineers, 2015. http://dx.doi.org/10.1115/dscc2015-9865.

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This paper presents appearance-based localization for an omni-directional camera that builds on a combination of the group Least Absolute Shrinkage and Selection Operator (LASSO) and the extended Kalman filter (EKF). A histogram that represents the population of the Speeded-Up Robust Features (SURF points) is computed for each image, the features of which are selected via the group LASSO regression. The EKF takes the output of the LASSO regression-based first localization as observations for the final localization. The experimental results demonstrate the effectiveness of our approach.
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Thakoor, Kaveri A., Sophie Marat, Patrick J. Nasiatka, et al. "Attention biased speeded up robust featureS (AB-SURF): A neurally-inspired object recognition algorithm for a wearable aid for the visually-impaired." In 2013 IEEE International Conference on Multimedia and Expo Workshops (ICMEW). IEEE, 2013. http://dx.doi.org/10.1109/icmew.2013.6618345.

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