Relevant bibliographies by topics / SURF (Speed-Up Robust Features)

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Academic literature on the topic 'SURF (Speed-Up Robust Features)'

Author: Grafiati
Published: 3 June 2025
Last updated: 22 June 2025

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Journal articles on the topic "SURF (Speed-Up Robust Features)"

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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 the image processing speed and feature recognition rate. The sparse representation of SURF is also utilized to describe the environmental map in SLAM tasks. The purpose is to reduce the computation complexity in state estimation using extended Kalman filter (EKF). The EKF SLAM with SURF-based map is developed and implemented on a binocular vision system. The integrated system has been successfully validated to fulfill the basic capabilities of SLAM system.
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G, Kiran Kumar, Malathi Rani D, and Guru Mohan Rao E. "Fast palmprint retrieval using speed up robust features." Indian Journal of Science and Technology 13, no. 31 (2020): 3204–12. https://doi.org/10.17485/IJST/v13i31.404.

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Abstract <strong>Background/Objective:</strong>&nbsp;Biometric usage is increasing in exponential series in all organisations for multiple purposes like employee attendance, Aadhaar based authentication and secure login using finger print etc. This biometric process should be as quick as possible without making much delay to retrieve the respective finger print. So an efficient quick retrieval procedure is required, in this regards a fast retrieval method for palm prints is proposed in this article.&nbsp;<strong>Method:</strong>&nbsp;This method uses Speed up Robust Features (SURF) and an efficient look up table for fast retrieval of palm prints. A key is computed for each palmprint by matching with a pre-selected palmprint called representative. This key is used, to place the palmprint into the look up table like traditional database record. To identify a query palmprint, key is computed and selects a set of palm prints from the look up table which are having similar key as possible matches.&nbsp;<strong>Findings:</strong>&nbsp;This proposed solution is experimented with multiple representative images to check the improved performance. As an outcome we could achieve better hit rate by comparing with existing system<strong>Novelty:</strong>. &nbsp;This proposed method makes the new palm prints dynamically without disturbing the current records in the system. The entire solution is experimented on benchmark PolyU palmprint database of 7,753 images and significant performance is shown in results. This proposed solution shows better results with respect to hit rate and miss rate. <strong>Keywords:</strong> Palmprint; index key; SURF; similarity score; representative
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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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Wu, Shu Guang, Shu He, and Xia Yang. "Study on Image Matching Based on Speed up Robust Features Method." Advanced Materials Research 1044-1045 (October 2014): 1352–56. http://dx.doi.org/10.4028/www.scientific.net/amr.1044-1045.1352.

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Image registration is one of the fundamental problems in digital image processing, which is a prerequisite and key step for further comprehensive analysis,considering the advantages of the algorithm in speed and its disadvantage of more false matching points,a image matching method based on RANSAC and surf isproposed.The experiments results show that compared with the other algorithms,the surf algorithm improves the matching speed，as well as the matching accuracy，and exhibits good performance in terms of resisting rotation，noise,and brightness changes．
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Pandey, Ramesh Chand, Sanjay Kumar Singh, and K. K. Shukla. "Passive Copy- Move Forgery Detection Using Speed-Up Robust Features, Histogram Oriented Gradients and Scale Invariant Feature Transform." International Journal of System Dynamics Applications 4, no. 3 (2015): 70–89. http://dx.doi.org/10.4018/ijsda.2015070104.

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Copy-Move is one of the most common technique for digital image tampering or forgery. Copy-Move in an image might be done to duplicate something or to hide an undesirable region. In some cases where these images are used for important purposes such as evidence in court of law, it is important to verify their authenticity. In this paper the authors propose a novel method to detect single region Copy-Move Forgery Detection (CMFD) using Speed-Up Robust Features (SURF), Histogram Oriented Gradient (HOG), Scale Invariant Features Transform (SIFT), and hybrid features such as SURF-HOG and SIFT-HOG. SIFT and SURF image features are immune to various transformations like rotation, scaling, translation, so SIFT and SURF image features help in detecting Copy-Move regions more accurately in compared to other image features. Further the authors have detected multiple regions COPY-MOVE forgery using SURF and SIFT image features. Experimental results demonstrate commendable performance of proposed methods.
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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 visual landmarks in robot SLAM systems. Many modifications of the SURF algorithms are proposed in this study including the orientation representation of features, the vector dimension of feature description, and the number of detected features in an image. The concept of sparse representation is also used to describe the environmental map and to reduce the computational complexity when using extended Kalman filter (EKF) for state estimation. Effective procedures of data association and map management for SURFs in SLAM are also designed to improve accuracy in robot state estimation. Experimental works were performed on an actual system with binocular vision sensors to validate the feasibility and effectiveness of the proposed algorithms. The experimental examples include the evaluation of state estimation using EKF SLAM and the implementation of indoor SLAM. In the experiments, the performance of the modified SURF algorithms was compared with the original SURF algorithms. The experimental results confirm that the modified SURF provides better repeatability and better robustness for representing the landmarks in visual SLAM systems.
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Ariel, Muhammad Baresi, Ratri Dwi Atmaja, and Azizah Azizah. "Implementasi Metode Speed Up Robust Feature dan Scale Invariant Feature Transform untuk Identifikasi Telapak Kaki Individu." JURNAL Al-AZHAR INDONESIA SERI SAINS DAN TEKNOLOGI 3, no. 4 (2017): 178. http://dx.doi.org/10.36722/sst.v3i4.232.

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&lt;p&gt;&lt;em&gt;Abstrak&lt;/em&gt;&lt;strong&gt; - Biometrik merupakan metode pengidentifikasian individu berdasarkan ciri fisiknya. Salah satu ciri fisik yang dapat digunakan untuk biometrik adalah telapak kaki. Ciri fisik ini dipilih karena memiliki tingkat keunikan yang tinggi, sehingga hampir tidak terdapat individu yang memiliki ciri yang sama. Metode-metode ekstraksi ciri seperti Scale Invariant Feature Transform (SIFT) dan Speed Up Robust Feature (SURF) akan sesuai jika digunakan untuk mendukung sistem identifikasi telapak kaki. Tahapan yang dilakukan untuk mendapatkan deskriptor dimulai dari scanning telapak kaki, pre-processing, ekstraksi ciri dengan menggunakan SURF dan SIFT sampai pada proses matching pada saat pengujian. Perbandingan keduanya dilihat dari aspek akurasi. Proses penentuan klasifikasi dan kelas menggunakan algoritma K-Nearest Neighbor (K- NN). Hasilnya akan menjadi data-data penelitian dalam paper ini. Diharapkan menggunakan metode SIFT dan SURF akan memberikan hasil dengan tingkat keakurasian yang tinggi.&lt;/strong&gt;&lt;/p&gt;&lt;p&gt;&lt;em&gt;&lt;strong&gt;Kata Kunci&lt;/strong&gt; – Biometric, Footprint, SURF, SIFT, K- NN&lt;/em&gt;&lt;/p&gt;&lt;p&gt;&lt;em&gt;Abstract&lt;/em&gt;&lt;strong&gt; - Biometric is a method used to identify indivduals using their physical features. One of the physical features that can be used for biometric is the footprint. The footprint was chosen because of having a high level of uniqueness where it is almost impossible to find two individuals that have the same footprint. Feature extraction methods such as Scale Invariant Feature Transform (SIFT) and Speed Up Robust Feature (SURF) are appropriate if used for footprint identification system. The steps used in obtaining descriptor start from scanning the footprint, pre-processing, feature extraction using SURF and SIFT and last the matching process. The comparison between the two methods will be observed by their accuracy. The K-Nearest Neighbor (K-NN) algorithm will be used for the classification process. The outputs will be used for research data in this research proposal. It will be expected that using SIFT and SURF for the feature extraction will result in high accuracy.&lt;/strong&gt;&lt;/p&gt;&lt;p&gt;&lt;em&gt;&lt;strong&gt;Keywords&lt;/strong&gt; – Biometric, Footprint, SURF, SIFT, K- NN&lt;/em&gt;&lt;/p&gt;
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Lu, Kai, Junli Luo, Yueqi Zhong, and Xinyu Chai. "Identification of wool and cashmere SEM images based on SURF features." Journal of Engineered Fibers and Fabrics 14 (January 2019): 155892501986612. http://dx.doi.org/10.1177/1558925019866121.

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Pattern recognition and feature extraction methods are applied to identify cashmere and wool fibers, which are two kinds of very similar animal fibers. In this article, we proposed a new identification method based on Speed Up Robust Features of fiber images. The images of wool and cashmere fibers are obtained by scanning electron microscopy. Speed Up Robust Features of fiber images are extracted, and each fiber image is regarded as a collection of feature vectors in our logic. The vectors are fed into a support vector machine for supervised learning. The findings from scanning electron microscope images indicate that this method is effective; the recognition rate is higher than 93% for a broad range of blend proportions of the two fibers.
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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 of the traditional speeded up robust features mosaic method, improving the matching accuracy and speed, and the mosaic effect. It has certain theoretical and practical value.
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Weimert, Achim, Xueting Tan, and Xubo Yang. "Natural Feature Detection on Mobile Phones with 3D FAST." International Journal of Virtual Reality 9, no. 4 (2010): 29–34. http://dx.doi.org/10.20870/ijvr.2010.9.4.2788.

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In this paper, we present a novel feature detection approach designed for mobile devices, showing optimized solutions for both detection and description. It is based on FAST (Features from Accelerated Segment Test) and named 3D FAST. Being robust, scale-invariant and easy to compute, it is a candidate for augmented reality (AR) applications running on low performance platforms. Using simple calculations and machine learning, FAST is a feature detection algorithm known to be efficient but not very robust in addition to its lack of scale information. Our approach relies on gradient images calculated for different scale levels on which a modified9 FAST algorithm operates to obtain the values of the corner response function. We combine the detection with an adapted version of SURF (Speed Up Robust Features) descriptors, providing a system with all means to implement feature matching and object detection. Experimental evaluation on a Symbian OS device using a standard image set and comparison with SURF using Hessian matrix-based detector is included in this paper, showing improvements in speed (compared to SURF) and robustness (compared to FAST)
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Dissertations / Theses on the topic "SURF (Speed-Up Robust Features)"

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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 testing shows that the R-SURF outperforms the regular SURF detector when subject to image blurring, illumination changes and compression. Based on the testing results, the R-SURF detector outperforms regular SURF slightly when subjected to affine (viewpoint) changes. For image scale and rotation transformations, R-SURF outperforms for very small transformation values, but the regular SURF algorithm performs better for larger variations. The application of this research in the larger recognition process is also discussed.
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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 between a rack-server and a sensor node.This report describes the implementation of a testbed for the evaluationof distributed processing of visual features. The testbed is implementedin C++ using creditcard sized computers and Zigbee USB units. Com-munication between nodes utilizes ASN.1 defined types. The detectionand extraction stage use an implementation of the SURF algorithm fromOpenCV. Results are sent for matching to a server using a TCP-socketin the sink node. The system is evaluated in terms of data transmissionprotocol efficiency, and time spent on transmitting data vs. computation.
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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 results for an implemented program.
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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 Structure-from-Motion (SfM) algorithm, the translation vector and rotation matrix between the poses of camera in different time are calculated. The depths of feature points can then be retrieved in absolute scale with some constraints by using the obtained translation vector and rotation matrix. Finally, the camera attached to mobile robot and object coordinates in the world coordinate frame are found via the translation vector, rotation matrix, and the depths of the feature points. Experimental results are then presented for illustrating the performance of the suggested visual-based localization method.
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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 biometrics recognition mainstream become vein recognition. The vein recognition using Near-Infrared to illuminate the back of a hand、wrist、palm or finger to get the vein images form camera, and then use vein images to take features to recognize. Vein recognition is vital identification so it’s very difficult to forge and fake. Our proposed is use few features in the finger to identification. First to do normalization aimed at finger vein image on camera, second we use improve POSHE and Sobel to enhance the vein in finger vein image, then final we use SURF(Speeded Up Robust Features) to extraction and matching of finger-vein patterns. The experiment has proved that our proposed have high estimate、convenience and low cost.
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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 displacement of the camera at two photographing distances, feature points of the objects in the images are used to determine the distance measurement of the target objects. After that, we use the obtained distance information of the feature points of the target objects to construct the depth map by using smooth interpolation.
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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 articular facet involves calcaneus. Patient data can be stored in several kinds of imaging format, e.g. Computer Tomography (CT) data. The CT images is the evolution of the medical images that recently used for determine the disease from the patient. It is a sequence of 2-D images that construct 3-D images. CT images contain a significant amount of information, such as fracture information in each slice of 2-D images that may not be thoroughly and accurately analyzed via visual inspection. This study proposed a new method to classify and detect the fracture in calcaneus bone CT images. In this experiment, we do the experiment in two dataset. The first one in just one Dicom case and the second one in the all dataset (two Dicom case). Both morphological operation and edge detection methods were combined in order to achieve better input in classification and detection processes. In the pre-processing step, the images were resized into the same size (224 x 224) to fit in the CNN method. After that the input images were converted in the grayscale images. After that, the images were subtracted with the images mean. Convolutional Neural Network was also applied in the classification process in order to classify the bone into several classes. Two classes were classified (fracture and non-fracture) from three views that are coronal, transversal, and sagittal view. After classification, the detection were done from the fracture class and determine in which part of calcaneus bone was broken. The detection is from three views also that is transversal, sagittal, and coronal views.
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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 information during the video conference. Present existing video conferencing systems however, have problems in this area, resulting in some risks. These risks relate precisely to the lack of facilities to properly identify and authenticate participants, making it possible for unwanted/unauthorised participants to join the conference or masquerade as another participant. It is especially a problem, when facilitators or organisers are the only participants that know the authorised participants, or participants allowed in a video conference. In this dissertation, we review the risks that are present in video conferencing, and create a security system, (called BioVid) that mitigates the identification and authentication risks in video conferences. BioVid uses a Speeded-Up Robust Features or SURF-based face recognition approach, to identify and authenticate any participant in a video conference. BioVid continuously monitors the participants to check if masquerading has occurred and when it does detect an unauthorised participant, it informs the Service Provider. The Service Provider can then deal with the problem by either kicking the participant or asking the other participants to vote the unauthorised participant out of the video conference.
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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 make full advantage of the color information offered by color images to enhance the performance of the SURF algorithm in the number of feature points and the accuracy in matching the corresponding feature points. In order to efficiently apply the enhanced SURF algorithm to the depth map estimation of the objects with smooth surface and without having complex texture, the images with complex color texture will be projected on the target object so as to increase the number of feature points and the accuracy of the estimated depth map. The Hue angle defined in the LAB color space is used as the chromatic information for enhancing the SURF algorithm. The quantitative data associated with this information will be normalized to the range as the luminance before being fed into the computation involved in the SURF algorithm. As search for the corresponding feature points for depth map estimation, the histogram method is used find the outliers of the found feature points. The depth information is derived from the disparity model. Finally, the depth information is expressed in grayscale for visual inspection. Experimental results show that the number of both feature points and paired feature points are increased as expected, in which the number of feature points increased by using chroma information accounts for an average of 14% of the total feature points contributed also by the projection of complex color images average as 1.11 times of the number of feature points at the time of none projection. The depth map obtained by the detection is also similar to the shape of the actual three-dimensional object.
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Book chapters on the topic "SURF (Speed-Up Robust Features)"

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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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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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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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Hore, Sirshendu, Sankhadeep Chatterjee, Shouvik Chakraborty, and Rahul Kumar Shaw. "Analysis of Different Feature Description Algorithm in object Recognition." In Advances in Multimedia and Interactive Technologies. IGI Global, 2017. http://dx.doi.org/10.4018/978-1-5225-1025-3.ch004.

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Object recognition can be done based on local feature description algorithm or through global feature description algorithm. Both types of these descriptors have the efficiency in recognizing an object quickly and accurately. The proposed work judges their performance in different circumstances such as rotational effect scaling effect, illumination effect and blurring effect. Authors also investigate the speed of each algorithm in different situations. The experimental result shows that each one has some advantages as well as some drawbacks. SIFT (Scale Invariant Feature Transformation) and SURF (Speeded Up Robust Features) performs relatively better under scale and rotation change. MSER (Maximally stable extremal regions) performs better under scale change, MinEigen in affine change and illumination change while FAST (Feature from Accelerated segment test) and SURF consume less time.
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Hore, Sirshendu, Sankhadeep Chatterjee, Shouvik Chakraborty, and Rahul Kumar Shaw. "Analysis of Different Feature Description Algorithm in object Recognition." In Computer Vision. IGI Global, 2018. http://dx.doi.org/10.4018/978-1-5225-5204-8.ch023.

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Object recognition can be done based on local feature description algorithm or through global feature description algorithm. Both types of these descriptors have the efficiency in recognizing an object quickly and accurately. The proposed work judges their performance in different circumstances such as rotational effect scaling effect, illumination effect and blurring effect. Authors also investigate the speed of each algorithm in different situations. The experimental result shows that each one has some advantages as well as some drawbacks. SIFT (Scale Invariant Feature Transformation) and SURF (Speeded Up Robust Features) performs relatively better under scale and rotation change. MSER (Maximally stable extremal regions) performs better under scale change, MinEigen in affine change and illumination change while FAST (Feature from Accelerated segment test) and SURF consume less time.
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Ince, Ibrahim Furkan. "Robust Image Matching for Information Systems Using Randomly Uniform Distributed SURF Features." In Applications of Computational Science in Artificial Intelligence. IGI Global, 2022. http://dx.doi.org/10.4018/978-1-7998-9012-6.ch007.

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Detection of similar images taken in different perspectives is a big concern in digital image processing. Fast and robust methods have been proposed in this area. In this chapter, a novel image matching approach is proposed by using speeded-up robust features (SURF). SURF is a local feature detector and descriptor that can be used for tasks such as object recognition or registration or classification or 3D reconstruction. Successful detection of the images is achieved by finding and matching corresponding interest points using SURF features. The task of finding correspondences between two images is performed through using a novel brute-force method which uniformly generates random pairs for matching similarity. Experimental results show that the proposed method yields better results than conventional brute force methods in which at least 5% accuracy increment is obtained.
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Lenskiy, Artem A., and Jong-Soo Lee. "Detecting Eyes and Lips Using Neural Networks and SURF Features." In Cross-Disciplinary Applications of Artificial Intelligence and Pattern Recognition. IGI Global, 2012. http://dx.doi.org/10.4018/978-1-61350-429-1.ch018.

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In this chapter, the authors elaborate on the facial image segmentation and the detection of eyes and lips using two neural networks. The first neural network is applied to segment skin-colors and the second to detect facial features. As for input vectors, for the second network the authors apply speed-up robust features (SURF) that are not subject to scale and brightness variations. The authors carried out the detection of eyes and lips on two well-known facial feature databases, Caltech. and PICS. Caltech gave a success rate of 92.4% and 92.2% for left and right eyes and 85% for lips, whereas the PCIS database gave 96.9% and 95.3% for left and right eyes and 97.3% for lips. Using videos captured in real environment, among all videos, the authors achieved an average detection rate of 94.7% for the right eye and 95.5% for the left eye with a 86.9% rate for the lips
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Conference papers on the topic "SURF (Speed-Up Robust Features)"

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Siagian, Yorris, Muhathir, and Maqhfirah D. R. "Classification of Autism Using Feature Extraction Speed Up Robust Feature (SURF) with Boosting Algorithm." In 2023 International Conference on Information Technology Research and Innovation (ICITRI). IEEE, 2023. http://dx.doi.org/10.1109/icitri59340.2023.10250127.

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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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Ali, Nursabillilah Mohd, Mohd Safirin Karis, Munawwarah Abd Aziz, and Amar Faiz Zainal Abidin. "Analysis of frontal face detection performance by using Artificial Neural Network (ANN) and Speed-Up Robust Features (SURF) technique." In INTERNATIONAL CONFERENCE ON ADVANCED SCIENCE, ENGINEERING AND TECHNOLOGY (ICASET) 2015: Proceedings of the 1st International Conference on Advanced Science, Engineering and Technology. Author(s), 2016. http://dx.doi.org/10.1063/1.4965100.

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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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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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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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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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