Relevant bibliographies by topics / SURF FEATURES

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'SURF FEATURES'

Author: Grafiati
Published: 11 September 2023

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Journal articles on the topic "SURF 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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Wang, Yin-Tien, and Guan-Yu Lin. "Improvement of speeded-up robust features for robot visual simultaneous localization and mapping." Robotica 32, no. 4 (September 2, 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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Bay, Herbert, Andreas Ess, Tinne Tuytelaars, and Luc Van Gool. "Speeded-Up Robust Features (SURF)." Computer Vision and Image Understanding 110, no. 3 (June 2008): 346–59. http://dx.doi.org/10.1016/j.cviu.2007.09.014.

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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 (July 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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.M, Suresha, and Sandeep. "Recognition of Birds in Blurred and Illumination Images by Local Features." International Journal of Advanced Research in Computer Science and Software Engineering 7, no. 7 (July 30, 2017): 243. http://dx.doi.org/10.23956/ijarcsse/v7i7/0128.

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Local features are of great importance in computer vision. It performs feature detection and feature matching are two important tasks. In this paper concentrates on the problem of recognition of birds using local features. Investigation summarizes the local features SURF, FAST and HARRIS against blurred and illumination images. FAST and Harris corner algorithm have given less accuracy for blurred images. The SURF algorithm gives best result for blurred image because its identify strongest local features and time complexity is less and experimental demonstration shows that SURF algorithm is robust for blurred images and the FAST algorithms is suitable for images with illumination.
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Shukla, Tuhin, Nishchol Mishra, and Sanjeev Sharma. "Automatic Image Annotation using SURF Features." International Journal of Computer Applications 68, no. 4 (April 18, 2013): 17–24. http://dx.doi.org/10.5120/11567-6868.

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Tabuse, Masayoshi, Toshiki Kitaoka, and Dai Nakai. "Outdoor autonomous navigation using SURF features." Artificial Life and Robotics 16, no. 3 (December 2011): 356–60. http://dx.doi.org/10.1007/s10015-011-0950-8.

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Puyda, Volodymyr. "Surf Features Extraction in a Computer Vision System." Advances in Cyber-Physical Systems 2, no. 1 (March 28, 2017): 29–31. http://dx.doi.org/10.23939/acps2017.01.029.

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Jhan, J. P., and J. Y. Rau. "A NORMALIZED SURF FOR MULTISPECTRAL IMAGE MATCHING AND BAND CO-REGISTRATION." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-2/W13 (June 4, 2019): 393–99. http://dx.doi.org/10.5194/isprs-archives-xlii-2-w13-393-2019.

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<p><strong>Abstract.</strong> Due to the raw images of multi-lens multispectral (MS) camera has significant misregistration errors, performing image registration for band co-registration is necessary. Image matching is an essential step for image registration, which obtains conjugate features on the overlapped areas, and use them to estimate the coefficients of a transformation model for correcting the geometrical errors. However, due to the none-linear intensity of spectral response, performing feature-based image matching (such as SURF) can only obtain only a few conjugate features on cross-band MS images. Different to SURF that extracts local extremum in a multi-scale space and utilizes a threshold to determine a feature, we proposed a normalized SURF (N-SURF) that extracts features on single scale, calculates the cumulative distribution function (CDF) of features, and obtains consistent features from the CDF. In this study, two datasets acquired from Tetracam MiniMCA-12 and Micasense RedEdge Altum are used for evaluating the matching performance of N-SURF. Results show that N-SURF can extract approximately 2&amp;ndash;3 times number of features, match more points, and have more efficient than original SURF. On the other hand, with the successful of MS image matching, we can therefor use the conjugates to compute the coefficients of a geometric transformation model. In this study, three transformation models are used to compare the difference on MS band co-registration, i.e. affine, projective, and extended projective. Results show that extended projective model is better than the others as it can compensate the difference of lens distortion and viewpoint, and has co-registration accuracy of 0.3&amp;ndash;0.6 pixels.</p>
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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 (February 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 HOG feature is improved by introducing direction controllable filter to enhance the local edge information. At the same time, the K-means clustering is performed on SURF to obtain the word bag model. Finally, the fused motion features are input to support vector machine (SVM) to classify and recognize the motion features. We make comparison with the state-of-the-art methods on KTH, UCF Sports and SBU Kinect Interaction data sets. The results show that the recognition accuracy of the proposed algorithm is greatly improved.</p> <p>&nbsp;</p>
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Dissertations / Theses on the topic "SURF 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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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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Medeiros, Petr?cio Ricardo Tavares de. "Multifoveamento em multirresolu??o com f?veas m?veis." PROGRAMA DE P?S-GRADUA??O EM ENGENHARIA EL?TRICA E DE COMPUTA??O, 2016. https://repositorio.ufrn.br/jspui/handle/123456789/22258.

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Coordena??o de Aperfei?oamento de Pessoal de N?vel Superior (CAPES)
O foveamento ? uma t?cnica de vis?o computacional capaz de promover a redu??o da informa??o visual atrav?s de uma transforma??o da imagem, em dom?nio espacial, para o dom?nio de multirresolu??o. Entretanto, esta t?cnica se limita a uma ?nica f?vea com mobilidade dependente do contexto. Neste trabalho s?o propostas a defini??o e a constru??o de um modelo multifoveado denominado MMMF (multifoveamento em multirresolu??o com f?veas m?veis) baseado em um modelo anterior denominado MMF (multirresolu??o com f?vea m?vel). Em um contexto de m?ltiplas f?veas, a aplica??o de v?rias estruturas MMF, uma para cada f?vea, resulta em um consider?vel aumento de processamento, uma vez que h? interse??es entre regi?es de estruturas distintas, as quais s?o processadas m?ltiplas vezes. Dadas as estruturas de f?veas MMF, propomos um algoritmo para obter regi?es disjuntas que devem ser processadas, evitando regi?es redundantes e, portanto, reduzindo o tempo de processamento. Experimentos s?o propostos para validar o modelo e verificar a sua aplicabilidade no contexto de vis?o computacional. Resultados demonstram o ganho em termos de tempo de processamento do modelo proposto em rela??o ao uso de m?ltiplas f?veas do modelo MMF.
Foveation is a computer vision technique for visual information reduction obtained by applying an image transformation in the spatial domain to the multiresolution domain. However, this technique is limited to a single fovea context-dependent mobility. This work proposes the definition and the construction of a multifoveated model called MMMF (Multiresolution Multifoveation using Mobile Foveae) based on an earlier model called MMF (Multiresolution with Moving Fovea). In the context of multiple foveae, the application of various MMF structures, one for each fovea, results in an increase in processing time, since there are intersections between regions of different structures, which are processed multiple times. Given MMF structures, an algorithm in order to get disjoint regions which are to be processed is proposed, avoiding redundant regions and thereby reducing the processing time. Experiments are proposed to validate the model and to verify its applicability in the computer vision context. Results show the gain in processing time of the proposed model compared to the use of multiple MMF structures.
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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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Saad, Elhusain Salem. "Defocus Blur-Invariant Scale-Space Feature Extractions." University of Dayton / OhioLINK, 2014. http://rave.ohiolink.edu/etdc/view?acc_num=dayton1418907974.

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Veľas, Martin. "Automatické třídění fotografií podle obsahu." Master's thesis, Vysoké učení technické v Brně. Fakulta informačních technologií, 2013. http://www.nusl.cz/ntk/nusl-236399.

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This thesis deals with content based automatic photo categorization. The aim of the work is to experiment with advanced techniques of image represenatation and to create a classifier which is able to process large image dataset with sufficient accuracy and computation speed. A traditional solution based on using visual codebooks is enhanced by computing color features, soft assignment of visual words to extracted feature vectors, usage of image segmentation in process of visual codebook creation and dividing picture into cells. These cells are processed separately. Linear SVM classifier with explicit data embeding is used for its efficiency. Finally, results of experiments with above mentioned techniques of the image categorization are discussed.
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González, Valenzuela Ricardo Eugenio 1984. "Linear dimensionality reduction applied to SIFT and SURF feature descriptors." [s.n.], 2014. http://repositorio.unicamp.br/jspui/handle/REPOSIP/275499.

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Orientadores: Hélio Pedrini, William Robson Schwartz
Dissertação (mestrado) - Universidade Estadual de Campinas, Instituto de Computação
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Resumo: Descritores locais robustos normalmente compõem-se de vetores de características de alta dimensionalidade para descrever atributos discriminativos em imagens. A alta dimensionalidade de um vetor de características implica custos consideráveis em termos de tempo computacional e requisitos de armazenamento afetando o desempenho de várias tarefas que utilizam descritores de características, tais como correspondência, recuperação e classificação de imagens. Para resolver esses problemas, pode-se aplicar algumas técnicas de redução de dimensionalidade, escencialmente, construindo uma matrix de projeção que explique adequadamente a importancia dos dados em outras bases. Esta dissertação visa aplicar técnicas de redução linear de dimensionalidade aos descritores SIFT e SURF. Seu principal objetivo é demonstrar que, mesmo com o risco de diminuir a precisão dos vetores de caraterísticas, a redução de dimensionalidade pode resultar em um equilíbrio adequado entre tempo computacional e recursos de armazenamento. A redução linear de dimensionalidade é realizada por meio de técnicas como projeções aleatórias (RP), análise de componentes principais (PCA), análise linear discriminante (LDA) e mínimos quadrados parciais (PLS), a fim de criar vetores de características de menor dimensão. Este trabalho avalia os vetores de características reduzidos em aplicações de correspondência e de recuperação de imagens. O tempo computacional e o uso de memória são medidos por comparações entre os vetores de características originais e reduzidos
Abstract: Robust local descriptors usually consist of high dimensional feature vectors to describe distinctive characteristics of images. The high dimensionality of a feature vector incurs into considerable costs in terms of computational time and storage requirements, which affects the performance of several tasks that employ feature vectors, such as matching, image retrieval and classification. To address these problems, it is possible to apply some dimensionality reduction techniques, by building a projection matrix which explains adequately the importance of the data in other basis. This dissertation aims at applying linear dimensionality reduction to SIFT and SURF descriptors. Its main objective is to demonstrate that, even risking to decrease the accuracy of the feature vectors, the dimensionality reduction can result in a satisfactory trade-off between computational time and storage. We perform the linear dimensionality reduction through Random Projections (RP), Independent Component Analysis (ICA), Principal Component Analysis (PCA), Linear Discriminant Analysis (LDA) and Partial Least Squares (PLS) in order to create lower dimensional feature vectors. This work evaluates such reduced feature vectors in a matching application, as well as their distinctiveness in an image retrieval application. The computational time and memory usage are then measured by comparing the original and the reduced feature vectors. OBSERVAÇÃONa segunda folha, do arquivo em anexo, o meu nome tem dois pequenos erros
Mestrado
Ciência da Computação
Mestre em Ciência da Computação
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Grünseisen, Vojtěch. "Vyhledávání graffiti tagů podle podobnosti." Master's thesis, Vysoké učení technické v Brně. Fakulta informačních technologií, 2013. http://www.nusl.cz/ntk/nusl-236413.

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This work focuses on a possibility of using current computer vision alghoritms and methods for automatic similarity matching of so called graffiti tags. Those are such graffiti, that are used as a fast and simple signature of their authors. The process of development and implementation of CBIR system, which is created for this task, is described. For the purposes of finding images similarity, local features are used, most notably self-similarity features.
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Stefanik, Kevin Vincent. "Sequential Motion Estimation and Refinement for Applications of Real-time Reconstruction from Stereo Vision." Thesis, Virginia Tech, 2011. http://hdl.handle.net/10919/76802.

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This paper presents a new approach to the feature-matching problem for 3D reconstruction by taking advantage of GPS and IMU data, along with a prior calibrated stereo camera system. It is expected that pose estimates and calibration can be used to increase feature matching speed and accuracy. Given pose estimates of cameras and extracted features from images, the algorithm first enumerates feature matches based on stereo projection constraints in 2D and then backprojects them to 3D. Then, a grid search algorithm over potential camera poses is proposed to match the 3D features and find the largest group of 3D feature matches between pairs of stereo frames. This approach will provide pose accuracy to within the space that each grid region covers. Further refinement of relative camera poses is performed with an iteratively re-weighted least squares (IRLS) method in order to reject outliers in the 3D matches. The algorithm is shown to be capable of running in real-time correctly, where the majority of processing time is taken by feature extraction and description. The method is shown to outperform standard open source software for reconstruction from imagery.
Master of Science
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Hubený, Marek. "Koncepty strojového učení pro kategorizaci objektů v obrazu." Master's thesis, Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií, 2017. http://www.nusl.cz/ntk/nusl-316388.

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This work is focused on objects and scenes recognition using machine learning and computer vision tools. Before the solution of this problem has been studied basic phases of the machine learning concept and statistical models with accent on their division into discriminative and generative method. Further, the Bag-of-words method and its modification have been investigated and described. In the practical part of this work, the implementation of the Bag-of-words method with the SVM classifier was created in the Matlab environment and the model was tested on various sets of publicly available images.
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Books on the topic "SURF FEATURES"

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Argentina) Ventana Sur (2019 (Buenos Aires. Ventana Sur 2019: Film guide. Buenos Aires: Instituto Nacional de Cine y Artes Audiovisuales. INCAA, 2019.

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Compassion focused therapy: Distinctive features. Hove: Routledge, 2010.

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Gilbert, Paul. Compassion focused therapy: Distinctive features. Hove: Routledge, 2010.

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Gagnon, François. Données sur l'économie du scénario au Québec pour le long métrage de fiction: Répertoire 1968-2000. Montréal: Centre de recherche cinéma/réception de l'Université de Montréal, 2002.

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Canada. Feature Film Advisory Committee. The road to success : report of the Feature Film Advisory Committee =: La voie du succès : rapport du Comité consultatif sur le long métrage. Ottawa, Ont: Canadian Heritage = Patrimoine canadien, 1999.

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Williams, H. Major structural features of southeastern Canada and the Atlantic Continental Margin portrayed in regional gravity and magnetic maps =: Principaux éléments structuraux du sud-est du Canada et de la marge continentale de l'Atlantique tels que représentés sur des cartes gravimétriques et magnétiques régionales. Ottawa, Ont: Geological Survey of Canada = Commission géologique du Canada, 1994.

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Google Chrome Browser User Guide for Beginners and Seniors: Learn and Master How to Use Google Chrome Browser to Surf the Internet, Setup and Use All Modern Chrome Features from Basic to Advance. Independently Published, 2022.

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Crane, Rebecca. Mindfulness-Based Cognitive Therapy: Distinctive Features. Taylor & Francis Group, 2017.

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Mindfulness-Based Cognitive Therapy: Distinctive Features. Taylor & Francis Group, 2017.

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Crane, Rebecca. Mindfulness-Based Cognitive Therapy: Distinctive Features. Taylor & Francis Group, 2008.

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Book chapters on the topic "SURF FEATURES"

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Bay, Herbert, Tinne Tuytelaars, and Luc Van Gool. "SURF: Speeded Up Robust Features." In Computer Vision – ECCV 2006, 404–17. Berlin, Heidelberg: Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11744023_32.

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Souza, Luis, Christian Hook, João P. Papa, and Christoph Palm. "Barrett’s Esophagus Analysis Using SURF Features." In Informatik aktuell, 141–46. Berlin, Heidelberg: Springer Berlin Heidelberg, 2017. http://dx.doi.org/10.1007/978-3-662-54345-0_34.

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Srinivas, Badrinath G., and Phalguni Gupta. "Palmprint Based Verification System Using SURF Features." In Communications in Computer and Information Science, 250–62. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-03547-0_24.

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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, 203–10. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-35795-4_26.

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Shwetha, S., Sunanda Dixit, and B. I. Khondanpur. "Person Recognition Using Surf Features and Vola-Jones Algorithm." In Advances in Intelligent Systems and Computing, 537–43. Singapore: Springer Singapore, 2017. http://dx.doi.org/10.1007/978-981-10-3156-4_56.

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M’hiri, Faten, Claudia Chevrefils, and Jean-Philippe Sylvestre. "Quality Assessment of Retinal Hyperspectral Images Using SURF and Intensity Features." In Lecture Notes in Computer Science, 118–25. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-66185-8_14.

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Zhang, Nan. "Computing Parallel Speeded-Up Robust Features (P-SURF) via POSIX Threads." In Emerging Intelligent Computing Technology and Applications, 287–96. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-04070-2_33.

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Pandey, Ramesh Chand, Rishabh Agrawal, Sanjay Kumar Singh, and K. K. Shukla. "Passive Copy Move Forgery Detection Using SURF, HOG and SIFT Features." In Advances in Intelligent Systems and Computing, 659–66. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-11933-5_74.

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Batur, Aliya, Patigul Mamat, Wenjie Zhou, Yali Zhu, and Kurban Ubul. "Complex Printed Uyghur Document Image Retrieval Based on Modified SURF Features." In Pattern Recognition and Computer Vision, 99–111. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-03338-5_9.

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Shah, Munir, Jeremiah Deng, and Brendon Woodford. "Illumination Invariant Background Model Using Mixture of Gaussians and SURF Features." In Computer Vision - ACCV 2012 Workshops, 308–14. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-37410-4_27.

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Conference papers on the topic "SURF FEATURES"

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Du, Geng, Fei Su, and Anni Cai. "Face recognition using SURF features." In Sixth International Symposium on Multispectral Image Processing and Pattern Recognition, edited by Mingyue Ding, Bir Bhanu, Friedrich M. Wahl, and Jonathan Roberts. SPIE, 2009. http://dx.doi.org/10.1117/12.832636.

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Ignat, Anca, and Ioan Păvăloi. "Occluded Iris Recognition using SURF Features." In 16th International Conference on Computer Vision Theory and Applications. SCITEPRESS - Science and Technology Publications, 2021. http://dx.doi.org/10.5220/0010255405080515.

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Deshmukh, Jyoti, and Udhav Bhosle. "SURF features based classifiers for mammogram classification." In 2017 International Conference on Wireless Communications, Signal Processing and Networking (WiSPNET). IEEE, 2017. http://dx.doi.org/10.1109/wispnet.2017.8299734.

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Lv, Di, Yunfu Deng, Zhihao Li, Qujiang Lei, Bo Liang, Jie Xu, and Xiuhao Li. "Advanced SURF Features Based Flexible Object Detection." In 2019 IEEE International Conference on Robotics and Biomimetics (ROBIO). IEEE, 2019. http://dx.doi.org/10.1109/robio49542.2019.8961377.

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Pancham, Ardhisha, Daniel Withey, and Glen Bright. "Tracking image features with PCA-SURF descriptors." In 2015 14th IAPR International Conference on Machine Vision Applications (MVA). IEEE, 2015. http://dx.doi.org/10.1109/mva.2015.7153206.

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Rabbani, Golam Shams, Sharmin Sultana, Md Nazmul Hasan, Salem Quddus Fahad, and Jia Uddin. "Person identification using SURF features of dental radiograph." In the 3rd International Conference. New York, New York, USA: ACM Press, 2019. http://dx.doi.org/10.1145/3309074.3309115.

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Karthik, R., A. AnnisFathima, and V. Vaidehi. "Panoramic view creation using invariant momentsand SURF features." In 2013 Third International Conference on Recent Trends in Information Technology (ICRTIT). IEEE, 2013. http://dx.doi.org/10.1109/icrtit.2013.6844233.

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Gigaud, Guillaume, and Pierre Moulin. "Traitor-tracing aided by compressed SURF image features." In 2010 44th Annual Conference on Information Sciences and Systems (CISS). IEEE, 2010. http://dx.doi.org/10.1109/ciss.2010.5464707.

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Alfadhli, Fares Hasan Obaid, Ali Afzalian Mand, Md Shohel Sayeed, Kok Swee Sim, and Mundher Al-Shabi. "Classification of tuberculosis with SURF spatial pyramid features." In 2017 International Conference on Robotics, Automation and Sciences (ICORAS). IEEE, 2017. http://dx.doi.org/10.1109/icoras.2017.8308044.

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Cui, Kai, Hua Cai, Yao Zhang, and Huan Chen. "A face alignment method based on SURF features." In 2017 10th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI). IEEE, 2017. http://dx.doi.org/10.1109/cisp-bmei.2017.8301964.

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Reports on the topic "SURF FEATURES"

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Michaels, Michelle, Theodore Letcher, Sandra LeGrand, Nicholas Webb, and Justin Putnam. Implementation of an albedo-based drag partition into the WRF-Chem v4.1 AFWA dust emission module. Engineer Research and Development Center (U.S.), January 2021. http://dx.doi.org/10.21079/11681/42782.

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Employing numerical prediction models can be a powerful tool for forecasting air quality and visibility hazards related to dust events. However, these numerical models are sensitive to surface conditions. Roughness features (e.g., rocks, vegetation, furrows, etc.) that shelter or attenuate wind flow over the soil surface affect the magnitude and spatial distribution of dust emission. To aide in simulating the emission phase of dust transport, we used a previously published albedo-based drag partition parameterization to better represent the component of wind friction speed affecting the immediate soil sur-face. This report serves as a guide for integrating this parameterization into the Weather Research and Forecasting with Chemistry (WRF-Chem) model. We include the procedure for preprocessing the required input data, as well as the code modifications for the Air Force Weather Agency (AFWA) dust emission module. In addition, we provide an example demonstration of output data from a simulation of a dust event that occurred in the Southwestern United States, which incorporates use of the drag partition.
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LeGrand, Sandra, Theodore Letcher, Gregory Okin, Nicholas Webb, Alex Gallagher, Saroj Dhital, Taylor Hodgdon, Nancy Ziegler, and Michelle Michaels. Application of a satellite-retrieved sheltering parameterization (v1.0) for dust event simulation with WRF-Chem v4.1. Engineer Research and Development Center (U.S.), May 2023. http://dx.doi.org/10.21079/11681/47116.

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Employing numerical prediction models can be a powerful tool for forecasting air quality and visibility hazards related to dust events. However, these numerical models are sensitive to surface conditions. Roughness features (e.g., rocks, vegetation, furrows, etc.) that shelter or attenuate wind flow over the soil surface affect the magnitude and spatial distribution of dust emission. To aide in simulating the emission phase of dust transport, we used a previously published albedo-based drag partition parameterization to better represent the component of wind friction speed affecting the immediate soil sur-face. This report serves as a guide for integrating this parameterization into the Weather Research and Forecasting with Chemistry (WRF-Chem) model. We include the procedure for preprocessing the required input data, as well as the code modifications for the Air Force Weather Agency (AFWA) dust emission module. In addition, we provide an example demonstration of output data from a simulation of a dust event that occurred in the Southwestern United States, which incorporates use of the drag partition.
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Babenko, Oksana. Ідеї екуменізму в публіцистиці митрополита Андрея Шептицького: сучасне прочитання. Ivan Franko National University of Lviv, March 2023. http://dx.doi.org/10.30970/vjo.2023.52-53.11717.

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Subject of the article’s study – ecumenism of Metropolitan Andrei Sheptytskyi and reflection of this phenomenon in the works of scientists and modern Ukrainian media. Main objective of the study: analyze what Ukrainian scientists, journalists and different media are writing about Sheptytkyi’s ecumenism. Methodology: We used a bibliographic method to accumulate factual material, a qualitative content analysis to isolate the ideas of ecumenism from the journalism of Metropolitan Andrey Sheptytskyi, a cultural-historical method that made it possible to consider the ideas of ecumenism in the context of the era, the connection with the historical context, as well as methods of synthesis and generalization, induction and deduction. The study process description: In our scientific article, we analyzed the doctoral dissertation of His Beatitude Lubomyr Huzar entitled «Andrei Sheptytskyi, Metropolitan of Halytskyi (1901-1944). Herald of ecumenism». His Beatitude Lubomyr defended this fundamental work at the Pontifical Urbaniana University in Rome back in 1972. Therefore, we observed how this work reflects the historical prerequisites, features and development of Sheptytskyi’s ecumenism, who, according to His Beatitude Lubomir, was a kind of innovator in this field, a person who was ahead of his time. We also analyzed the reflections on the ecumenism of Sheptytskyi´s father, doctor Ivan Datsk, which are reflected in his book «In Search of Faithfulness and Truth». In addition, we turned to the scientific text «Ecumenism of Sheptytskyi» by professors Mykola Vegesh and Mykola Palinchak. Subsequently, it was analyzed how the scientific work became a useful basis for the coverage of Sheptytskyi’s ecumenism in the press. In particular, in the columns of the cultural and social site «Zbruch» in Diana Motruk’s article «In Search of Church Unity». We also turned to the «Spiritual Greatness of Lviv» website, where in 2020 an interview with Mykhailo Perun, who shot the film «Sheptytskyi: Relevant information», was published, illustrating the ecumenical initiatives of this figure. In addition, we analyzed the publication on Radio Svoboda for 2022, dedicated to the anniversary of Sheptytsky’s stepping into eternity. It is also mentioned there about of Sheptytskyi’s ecumenism as his landmark activity. Subsequently, we found an article on the website «Christian and the World», where in a conversation with the scientist Dr. Andrii Sorokovskyi entitled «Andrei Sheptytskyi believed that the union is a synthesis, communion and dialogue between the East and the West, – Andrii Sorokovskyi» also analyzed the phenomenon of Sheptytskyi’s ecumenism. Results: we discovered that Sheptytskyi’s ecumenism was studied not only by numerous scientists, but this meaningful legacy of his is a valuable phenomenon for media coverage. Therefore, Sheptytskyi’s ecumenism becomes the subject of interest of journalists not only of publications that write mainly on church topics, but also socio-political and artistic ones. We are sure that Sheptytskyi’s ecumenism will continue to be studied by professional scientists and representatives of the wider media community. Significance: journalism of a religious orientation, high-quality and substantiated coverage of religious processes and phenomena in the press is still something quite new for modern Ukraine. In Soviet times, journalists were afraid to write about religion in order not to incur the wrath of the authorities, so such materials could not be included in the press. That is why it is very important to study how today’s journalists cover important issues of religion, which, in addition, have a strong scientific basis. In addition, the development of ecumenism and religious unity are extremely important for building national unity, which is necessary for our state to effectively confront the enemy in full-scale war. Key words: ecumenism; Metropolitan Andrey Sheptytskyi; media; interreleigion cooperation; dialogue.
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