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Journal articles 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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1

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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Seo, Jung-Jin, and Kyoung-Ro Yoona. "Modified Speeded Up Robust Features(SURF) for Performance Enhancement of Mobile Visual Search System." Journal of Broadcast Engineering 17, no. 2 (2012): 388–99. http://dx.doi.org/10.5909/jeb.2012.17.2.388.

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Norhisham Razali, Mohd, Noridayu Manshor, Alfian Abdul Halin, Razali Yaakob, and Norwati Mustapha. "Recognition of Food with Monotonous Appearance using Speeded-Up Robust Feature (SURF)." International Journal of Engineering & Technology 7, no. 4.31 (2018): 204–8. http://dx.doi.org/10.14419/ijet.v7i4.31.23368.

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Food has become one of the most photographed objects since the inceptions of smart phones and social media services. Recently, the analysis of food images using object recognition techniques have been investigated to recognize food categories. It is a part of a framework to accomplish the tasks of estimating food nutrition and calories for health-care purposes. The initial stage of food recognition pipeline is to extract the features in order to capture the food characteristics. A local feature by using SURF is among the efficient image detector and descriptor. It is using fast hessian detecto

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Dewanti, Farida, and Raden Sumiharto. "Purwarupa Sistem Penggabungan Foto Udara Pada UAV Menggunakan Algoritma Surf (Speeded-Up Robust Features)." IJEIS (Indonesian Journal of Electronics and Instrumentation Systems) 5, no. 2 (2015): 165. http://dx.doi.org/10.22146/ijeis.7640.

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Abstrak Purwarupa penggabungan foto udara pada UAV menggunakan algoritma SURF merupakan suatu sistem yang dirancang untuk melakukan penggabungan citra. Citra tersebut adalah citra yang dihasilkan fixed-wings UAV. Keluaran dari sistem ini berupa tampilan citra dengan objek yang lebih luas. Sistem ini dirancang untuk dapat melakukan penggabungan foto udara dengan menggunakan algoritma SURF, FLANN, RANSAC, dan warpPerspective. Algoritma SURF digunakan sebagai detektor keypoint dari masing-masing input foto. Metode FLANN untuk melakukan pencocokan keypoint yang ditemukan. RANSAC digunakan untuk pe

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Wang, Hui Bai, and Lu Nan Yang. "Pattern Recognition Application of Improved SURF Algorithm in Mobile Phone." Applied Mechanics and Materials 610 (August 2014): 471–76. http://dx.doi.org/10.4028/www.scientific.net/amm.610.471.

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Directed at the defects of time-consuming feature points extracting and out-of-sync between matching feature points and processing video frames in the original SURF (Speeded Up Robust Features) algorithm in mobile pattern recognition applications. For these shortcomings, this paper proposes an improved SURF algorithm. The algorithm uses buffer mechanism. An adaptation threshold is used when extracting feature points. Experimental results show that using the improved SURF algorithm in mobile applications has achieved the purpose of real-time processing. It has certain values in both theory and

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Kumar, Ashwani. "SURF feature descriptor for image analysis." Imaging and Radiation Research 6, no. 2 (2023): 5643. http://dx.doi.org/10.24294/irr.v6i2.5643.

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This paper provides a comprehensive review of SURF (speeded up robust features) feature descriptor, commonly used technique for image feature extraction. The SURF algorithm has obtained significant popularity because to its robustness, efficiency, and invariance to various image transformations. In this paper, an in-depth analysis of the underlying principles of SURF, its key components, and its use in computer vision tasks such as object recognition, image matching, and 3D reconstruction are proposed. Furthermore, we discuss recent advancements and variations of the SURF algorithm and compare

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Kumar, Ashwani. "SURF feature descriptor for image analysis." Imaging and Radiation Research 6, no. 1 (2024): 5643. http://dx.doi.org/10.24294/irr.v6i1.5643.

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This paper provides a comprehensive review of SURF (speeded up robust features) feature descriptor, commonly used technique for image feature extraction. The SURF algorithm has obtained significant popularity because to its robustness, efficiency, and invariance to various image transformations. In this paper, an in-depth analysis of the underlying principles of SURF, its key components, and its use in computer vision tasks such as object recognition, image matching, and 3D reconstruction are proposed. Furthermore, we discuss recent advancements and variations of the SURF algorithm and compare

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Zhou, Mu, Xia Hong, Zengshan Tian, Huining Dong, Mingchun Wang, and Kunjie Xu. "Maximum Entropy Threshold Segmentation for Target Matching Using Speeded-Up Robust Features." Journal of Electrical and Computer Engineering 2014 (2014): 1–12. http://dx.doi.org/10.1155/2014/768519.

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This paper proposes a 2-dimensional (2D) maximum entropy threshold segmentation (2DMETS) based speeded-up robust features (SURF) approach for image target matching. First of all, based on the gray level of each pixel and the average gray level of its neighboring pixels, we construct a 2D gray histogram. Second, by the target and background segmentation, we localize the feature points at the interest points which have the local extremum of box filter responses. Third, from the 2D Haar wavelet responses, we generate the 64-dimensional (64D) feature point descriptor vectors. Finally, we perform t

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Zhang, Nan. "Computing Optimised Parallel Speeded-Up Robust Features (P-SURF) on Multi-Core Processors." International Journal of Parallel Programming 38, no. 2 (2009): 138–58. http://dx.doi.org/10.1007/s10766-009-0122-9.

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Belarbi, Mohammed Amin, Saïd Mahmoudi, and Ghalem Belalem. "PCA as Dimensionality Reduction for Large-Scale Image Retrieval Systems." International Journal of Ambient Computing and Intelligence 8, no. 4 (2017): 45–58. http://dx.doi.org/10.4018/ijaci.2017100104.

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Dimensionality reduction in large-scale image research plays an important role for their performance in different applications. In this paper, we explore Principal Component Analysis (PCA) as a dimensionality reduction method. For this purpose, first, the Scale Invariant Feature Transform (SIFT) features and Speeded Up Robust Features (SURF) are extracted as image features. Second, the PCA is applied to reduce the dimensions of SIFT and SURF feature descriptors. By comparing multiple sets of experimental data with different image databases, we have concluded that PCA with a reduction in the ra

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Xiong, Xing, and Byung Jae Choi. "A Solution for Image Matching Error in SURF." Advanced Materials Research 717 (July 2013): 523–28. http://dx.doi.org/10.4028/www.scientific.net/amr.717.523.

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SURF (Speeded Up Robust Features) is known to be a famous and strong but computationally still expensive.It has not attained real-time performance yet. In this paper we analysis the SURF in orientation and descriptors extraction method forresolvingsome problems. For example, matching images through the SURF algorithm spends too much time and causes some errors by integral images. We propose a novel orientation and descriptor algorithm to improve the conventional SURF. Theproposed method shows some advantages such as a faster speed.

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Kok, Kai Yit, and Parvathy Rajendran. "A Descriptor-Based Advanced Feature Detector for Improved Visual Tracking." Symmetry 13, no. 8 (2021): 1337. http://dx.doi.org/10.3390/sym13081337.

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Despite years of work, a robust, widely applicable generic “symmetry detector” that can paral-lel other kinds of computer vision/image processing tools for the more basic structural charac-teristics, such as a “edge” or “corner” detector, remains a computational challenge. A new symmetry feature detector with a descriptor is proposed in this paper, namely the Simple Robust Features (SRF) algorithm. A performance comparison is made among SRF with SRF, Speeded-up Robust Features (SURF) with SURF, Maximally Stable Extremal Regions (MSER) with SURF, Harris with Fast Retina Keypoint (FREAK), Minimu

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Kelen, Yoseph P. K., and Budiman Baso. "Klasifikasi Tenun Timor Menggunakan Metode SVM Berdasarkan Speeded Up Robust Features." Jurnal Teknologi Informasi dan Ilmu Komputer 10, no. 6 (2023): 1353–60. http://dx.doi.org/10.25126/jtiik.1067625.

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Penelitian ini dilakukan sebagai upaya untuk melestarikan kain tenun Timor di bidang teknologi informasi, kususnya bidang pengolahan citra digital, yaitu pengenalan pola yang merupakan solusi untuk mengenali citra tenun secara otomatis. Dalam penelitian ini, klasifikasi citra tenun Timor mengaplikasikan metode SURF (Speeded Up Robust Feature) sebagai ekstraksi fitur dengan representasi BoVW (Bag of Visual Words) sedangkan SVM (Support Vector Machine) digunakan sebagai metode classifier. Agar kinerja BoVW lebih baik, digunakan pendekatan untuk menentukan jumlah cluster yang tepat untuk mengelom

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Kelen, Yoseph P. K., and Budiman Baso. "Klasifikasi Tenun Timor Menggunakan Metode SVM Berdasarkan Speeded Up Robust Features." Jurnal Teknologi Informasi dan Ilmu Komputer 10, no. 6 (2023): 1353–60. https://doi.org/10.25126/jtiik.2023107625.

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Penelitian ini dilakukan sebagai upaya untuk melestarikan kain tenun Timor di bidang teknologi informasi, kususnya bidang pengolahan citra digital, yaitu pengenalan pola yang merupakan solusi untuk mengenali citra tenun secara otomatis. Dalam penelitian ini, klasifikasi citra tenun Timor mengaplikasikan metode SURF (Speeded Up Robust Feature) sebagai ekstraksi fitur dengan representasi BoVW (Bag of Visual Words) sedangkan SVM (Support Vector Machine) digunakan sebagai metode classifier. Agar kinerja BoVW lebih baik, digunakan pendekatan untuk menentukan jumlah cluster yang tepat untuk mengelom

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Wen, Linxiong, Xiaohan Mei, Yi Tan, et al. "Cross-Correlation Algorithm Based on Speeded-Up Robust Features Parallel Acceleration for Shack–Hartmann Wavefront Sensing." Photonics 11, no. 9 (2024): 844. http://dx.doi.org/10.3390/photonics11090844.

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A cross-correlation algorithm to obtain the sub-aperture shifts that occur is a crucial aspect of scene-based SHWS (Shack–Hartmann wavefront sensing). However, when the sub-image is partially absent within the atmosphere, the traditional cross-correlation algorithm can easily obtain the wrong shift results. To overcome this drawback, we propose an algorithm based on SURFs (speeded-up-robust features) matching. In addition, to meet the speed required by wavefront sensing, CUDA parallel optimization of SURF matching is carried out using a GPU thread execution model and a programming model. The r

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MISS., NAMRATA N. RADE. "IMAGE TEXTURE CLASSIFICATION: SURF WITH SVM." IJIERT - International Journal of Innovations in Engineering Research and Technology 4, no. 7 (2017): 43–47. https://doi.org/10.5281/zenodo.1459102.

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<strong>Nowadays,various approaches of texture classification have been developed which works on acquired image features and separate them into different classes by using a specific classifier . This paper gives a state - of - the - art texture classification technique called Speeded up Robust Features (SURF) with SVM (Support Vector Machine) classifier. In this concept,image data representation is accomplished by capturing feature s in the form of key - points. SURF uses determinant of Hessian matrix to achieve point of interests on which description and classification is carried out. This me

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Mohammad, Awad Alfawair Khalid Alkaabneh. "Secure Image Indexing Using Speeded Up Robust Features (SURF) Key Points and SHAKE256 Hashing." Multicultural Education 7, no. 8 (2021): 108. https://doi.org/10.5281/zenodo.5168797.

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<em>Due to the rapid development of the Internet and storing data, several security attacks have taken place, causing deep fears to computer users. On the other hand, many computer users mainly depend on different techniques to protect their data and keep them safe and secure, including their private images, since they are important and sensitive. Some currently applied procedures do not provide enough protection for the privacy of the stored images. Consequently, researchers are exerting great effort to search for images in a more secure way using different codes. The primary purpose of this

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Zhang, Bao Feng, Ying Kui Jiao, Zhi Jun Ma, Yong Chen Li, and Jun Chao Zhu. "A Method of Features Extraction Based on Fisheye Image." Applied Mechanics and Materials 668-669 (October 2014): 1029–32. http://dx.doi.org/10.4028/www.scientific.net/amm.668-669.1029.

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In this paper, feature extraction algorithm based on spherical perspective projection model for the matching fisheye image is proposed. The fisheye image is mapped to the image plane through spherical mapping. Then the diffusion equation is formed by convolution of the image projection and spherical Gaussian function. The feature points of image are extracted based on the SIFT at the scale of spherical correlation function. Compared with SURF(Speeded Up Robust Features), more feature points in a shorter time are obtained.

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Amalina, Neneng Nur, Kurniawan Nur Ramadhani, and Febryanti Sthevanie. "Nuclei Detection and Classification System Based On Speeded Up Robust Feature (SURF)." EMITTER International Journal of Engineering Technology 7, no. 1 (2019): 1–13. http://dx.doi.org/10.24003/emitter.v7i1.288.

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Tumors contain a high degree of cellular heterogeneity. Various type of cells infiltrate the organs rapidly due to uncontrollable cell division and the evolution of those cells. The heterogeneous cell type and its quantity in infiltrated organs determine the level maglinancy of the tumor. Therefore, the analysis of those cells through their nuclei is needed for better understanding of tumor and also specify its proper treatment. In this paper, Speeded Up Robust Feature (SURF) is implemented to build a system that can detect the centroid position of nuclei on histopathology image of colon cance

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Li, Dong Mei, and Jing Lei Zhang. "A Improved Infrared and Visible Images Matching Based on SURF." Applied Mechanics and Materials 325-326 (June 2013): 1637–40. http://dx.doi.org/10.4028/www.scientific.net/amm.325-326.1637.

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Images matching is the basis of image registration. For their difference, a improved SURF(speeded up robust features) algorithm was proposed for the infrared and visible images matching. Firstly, edges were extracted from the images to improve the similarity of infrared and visible images. Then SURF algorithm was used to detect interest points, and the dimension of the point descriptor was 64. Finally, found the matching points by Euclidean distance. Experimental results show that some invalid data points were eliminated.

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Rajab, Maher I. "Classification of COVID-19 Chest X-Ray Images Based on Speeded Up Robust Features and Clustering-Based Support Vector Machines." Applied Computer Systems 28, no. 1 (2023): 163–69. http://dx.doi.org/10.2478/acss-2023-0016.

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Abstract Due to the worldwide deficiency of medical test kits and the significant time required by radiology experts to identify the new COVID-19, it is essential to develop fast, robust, and intelligent chest X-ray (CXR) image classification system. The proposed method consists of two major components: feature extraction and classification. The Bag of image features algorithm creates visual vocabulary from two training data categories of chest X-ray images: Normal and COVID-19 patients’ datasets. The algorithm extracts salient features and descriptors from CXR images using the Speeded Up Robu

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Khaing, Zarchi Htun, and Maung Maung Zaw Sai. "Gait Recognition for Person Identification using Statistics of SURF." International Journal of Trend in Scientific Research and Development 3, no. 5 (2019): 1415–22. https://doi.org/10.5281/zenodo.3590865.

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In recent years, the use of gait for human identification is a new biometric technology intended to play an increasingly important role in visual surveillance applications. Gait is a less unobtrusive biometric recognition that it identifies people from a distance without any interaction or cooperation with the subject. However, the effects of "covariates factors" such as changes in viewing angles, shoe styles, walking surfaces, carrying conditions, and elapsed time make gait recognition problems more challenging for research. Therefore, discriminative features extraction process from

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Srujana, S., K. Sripal Reddy, and D. Praveen Kumar. "Speeded Up Robust Features Registration based Efficient Multi Row Panorama Generation." Revista Gestão Inovação e Tecnologias 11, no. 2 (2021): 596–606. http://dx.doi.org/10.47059/revistageintec.v11i2.1696.

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Most of the current panorama generation tools need an input to be provided along a single axis, which means that only a small portion of the scene is recorded. To achieve a wide area of viewers, this paper suggests a multi-row panoramic technique (multi-panorama method). A pan/tilt camera allows the automatic or manual scanning to occur over large horizontal and vertical perspectives. Frame pictures in horizontal and vertical perspectives to adjust to their coordinates and projections will need separate projection marks. And the picture should be continually updated over long time periods but

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Wang, Wei, Wen Hui Li, Cheng Xi Wang, and Peng Wang. "A Novel Watermarking Algorithm Based on SURF and SVD." Applied Mechanics and Materials 303-306 (February 2013): 2117–21. http://dx.doi.org/10.4028/www.scientific.net/amm.303-306.2117.

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A novel watermarking algorithm based on SURF (Speeded Up Robust Features) and Singular Value Decomposition (SVD) is presented for copyright protection. By introducing the SURF’s key points matching and geometric distortion estimation, the attacked watermarked image can be corrected and the watermark synchronization is realized. In this way, the feature regions of the host image, which are used for information hiding, can be correctly detected by the improved Harris-Laplace corner detector even after signal processing and geometric attacks. The application of the proposed scheme makes the water

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Jeyapal, Akilandeswari, Jothi Ganesan, Sabeenian Royappan Savarimuthu, et al. "A Comparative Study of Feature Detection Techniques for Navigation of Visually Impaired Person in an Indoor Environment." Journal of Computational and Theoretical Nanoscience 17, no. 1 (2020): 21–26. http://dx.doi.org/10.1166/jctn.2020.8623.

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A development of automatic location identification and tracking system for visually impaired/ challenged person is a very challenging task in an indoor environment. In this paper, the comprehensive study of different feature detection and matching techniques namely, Minimum Eigenvalue (MinEigen) algorithm, Harris–Stephens (Harris) algorithm, Speeded Up Robust Features (SURF), Features from Accelerated Segment Test (FAST), Binary Robust Invariant Scalable Keypoints (BRISK) and Maximally Stable Extremal Regions (MSER) is presented. These algorithms are employed to detect and match the features o

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Sheta, Bassem. "Assessments Of Different Speeded Up Robust Features (SURF) Algorithm Resolution For Pose Estimation Of UAV." International Journal of Computer Science & Engineering Survey 3, no. 5 (2012): 15–41. http://dx.doi.org/10.5121/ijcses.2012.3502.

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Hidayat, Muhamad Arief, Windy Eka yulia Retnani, Windy Eka yulia Retnani, Diksy Media Firmansyah, Gayatri Dwi Santika, and Muhammad ‘Ariful Furqon. "Segmentasi Citra Tanda Tangan Menggunakan Fitur Titik SURF (Speeded Up Robust Features) dan Klasifikasi Jaringan Syaraf Tiruan." INFORMAL: Informatics Journal 9, no. 3 (2024): 224. https://doi.org/10.19184/isj.v9i3.53514.

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Signature image classification is an important field of image processing. One of the stages of signature classification is segmentation. The segmentation process aims to detect image pixels that are part of the signature and separate them from text or logo pixels in a document image. There is a signature segmentation technique using interest points extracted using the SURF (Speeded Up Robust Features) algorithm [1] In this technique, a connected component pixel will be considered part of the signature if it has more SURF points in common with the database connected component pixel signature. C

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Nawaz, Saqib Ali, Jingbing Li, Uzair Aslam Bhatti, et al. "A Novel Hybrid Discrete Cosine Transform Speeded Up Robust Feature-Based Secure Medical Image Watermarking Algorithm." Journal of Medical Imaging and Health Informatics 10, no. 11 (2020): 2588–99. http://dx.doi.org/10.1166/jmihi.2020.3220.

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With the advancement of networks and multimedia, digital watermarking technology has received worldwide attention as an effective method of copyright protection. Improving the anti-geometric attack ability of digital watermarking algorithms using image feature-based algorithms have received extensive attention. This paper proposes a novel robust watermarking algorithm based on SURF-DCT perceptual hashing (Speeded Up Robust Features and Discrete Cosine Transform), namely blind watermarking. We design and implement a meaningful binary watermark embedding and extraction algorithm based on the SUR

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Schaeferling, Michael, Ulrich Hornung, and Gundolf Kiefer. "Object Recognition and Pose Estimation on Embedded Hardware: SURF-Based System Designs Accelerated by FPGA Logic." International Journal of Reconfigurable Computing 2012 (2012): 1–16. http://dx.doi.org/10.1155/2012/368351.

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State-of-the-art object recognition and pose estimation systems often utilize point feature algorithms, which in turn usually require the computing power of conventional PC hardware. In this paper, we describe two embedded systems for object detection and pose estimation using sophisticated point features. The feature detection step of the “Speeded-up Robust Features (SURF)” algorithm is accelerated by a special IP core. The first system performs object detection and is completely implemented in a single medium-size Virtex-5 FPGA. The second system is an augmented reality platform, which consi

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B., V. Subbayamma, Durga Srihitha U., and Mohini Sekunthala S. "Vehicle Starter using Face Recognition." International Journal of Engineering and Advanced Technology (IJEAT) 9, no. 5 (2020): 711–13. https://doi.org/10.35940/ijeat.E9621.069520.

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Face acknowledgment is an interesting exploration subject as of late. The scientists proposed different strategies. The factors are similar to an assortment of lighting up, outward appearance, leveling, and perspective turn of events influences the precision of the face affirmation procedure. The fundamental requirement is the separation of the facial picture and the SURF (Speeded up Robust Features). Notwithstanding that they are additionally halfway invariable to brightening and relative change. This undertaking recommends a facial acknowledgment procedure utilizing SURF highlights and Suppo

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H. AL-Abboodi, Rana, and Ayad A. Al-Ani. "An Efficient and Robust Combined Feature Extraction Technique for Face Recognition Systems." Iraqi Journal of Information and Communication Technology 7, no. 3 (2024): 43–54. https://doi.org/10.31987/ijict.7.3.257.

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Face recognition has long attracted a lot of interest from the research and market communities due toits many possibilities across numerous sectors, but it has proven to be exceedingly difficult to deploy in real-time applications. Over the years, several face recognition algorithms and their variations have been created. In this paper, an integrating STIP and SURF for a robust feature extraction approach is proposed. This approach consists of four steps: In the first step, researchers are collecting the input images. In the next step, image preprocessing using a Gaussian filter is used. Then,

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Yang, Yu Han, and Yao Qin Xie. "Feature-Based GDLOH Deformable Registration for CT Lung Image." Applied Mechanics and Materials 333-335 (July 2013): 969–73. http://dx.doi.org/10.4028/www.scientific.net/amm.333-335.969.

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To improve the efficiency and accuracy of the conventional SIFT-TPS (Scale-invariant feature transform and Thin-Plate Spline) method in deformable registration for CT lung image, we develop a novel approach by using combining SURF(Speeded up Robust Features) and GDLOH(Gradient distance-location-orientation histogram) to detect matching feature points. First, we employ SURF as feature detection to find the stable feature points of the two CT images rapidly. Then GDLOH is taken as feature descriptor to describe each detected points characteristic, in order to supply measurement tool for matching

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Luo, Haifeng ,., Yue Han, and Jiangming Kan. "Improved SURF in Color Difference Scale Space for Color Image Matching." International Journal of Circuits, Systems and Signal Processing 16 (July 26, 2022): 1055–63. http://dx.doi.org/10.46300/9106.2022.16.128.

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This paper presents an improved SURF (Speeded Up Robust Features) for image matching which considers color information. Firstly, a new color difference scale space is constructed based on color information to detect feature point. Then we extracted a 192-dimensional vector to describe feature point, which includes a 64-dimensional vector representing the brightness information and a 128-dimensional vector representing the color information in a color image. Finally, in the process images matching, a new weighted Murkovski distance is used to measure the distance between two descriptors. From t

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Li, X. G., C. Ren, T. X. Zhang, Z. L. Zhu, and Z. G. Zhang. "UNMANNED AERIAL VEHICLE IMAGE MATCHING BASED ON IMPROVED RANSAC ALGORITHM AND SURF ALGORITHM." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-3/W10 (February 7, 2020): 67–70. http://dx.doi.org/10.5194/isprs-archives-xlii-3-w10-67-2020.

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Abstract. A UAV image matching method based on RANSAC (Random Sample Consensus) algorithm and SURF (speeded up robust features) algorithm is proposed. The SURF algorithm is integrated with fast operation and good rotation invariance, scale invariance and illumination. The brightness is invariant and the robustness is good. The RANSAC algorithm can effectively eliminate the characteristics of mismatched point pairs. The pre-verification experiment and basic verification experiment are added to the RANSAC algorithm, which improves the rejection and running speed of the algorithm. The experimenta

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Ding, Tianxingjian. "Comparative study of feature extraction algorithms for panorama stitching." Applied and Computational Engineering 16, no. 1 (2023): 249–56. http://dx.doi.org/10.54254/2755-2721/16/20230900.

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Panorama stitching is a fascinating and rapidly advancing research field. By integrating many photographs that were taken from various angles and viewpoints, with various exposure and color settings, a seamless image is primarily the aim of panorama stitching. This paper investigates the performance of three widely used feature extraction algorithms Speeded-Up Robust Features (SURF), Scale-Invariant Feature Transform (SIFT), and Oriented FAST and Rotated BRIEF (ORB) for panorama stitching. The study compares these algorithms in terms of accuracy, robustness, and speed. Results indicate that wh

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Rasmy, Laila, Imane Sebari, and Mohamed Ettarid. "Automatic Sub-Pixel Co-Registration of Remote Sensing Images Using Phase Correlation and Harris Detector." Remote Sensing 13, no. 12 (2021): 2314. http://dx.doi.org/10.3390/rs13122314.

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In this paper, we propose a new approach for sub-pixel co-registration based on Fourier phase correlation combined with the Harris detector. Due to the limitation of the standard phase correlation method to achieve only pixel-level accuracy, another approach is required to reach sub-pixel matching precision. We first applied the Harris corner detector to extract corners from both references and sensed images. Then, we identified their corresponding points using phase correlation between the image pairs. To achieve sub-pixel registration accuracy, two optimization algorithms were used. The effe

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Sun, Ning, and Botao Cao. "Real-Time Image Defect Detection System of Cloth Digital Printing Machine." Computational Intelligence and Neuroscience 2022 (July 19, 2022): 1–6. http://dx.doi.org/10.1155/2022/5625945.

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In order to solve the surface defects such as white silk, spots, and wrinkles on the fabrics in the process of digital printing production, a surface defect detection system for printed fabrics based on the accelerated robust feature algorithm is proposed. The image registration is mainly carried out by the speeded up robust features (SURF) algorithm; the bidirectional unique matching method is used to reduce the mismatch points, realize the accurate registration of the image, and extract the defect information through the difference algorithm. The experiment uses multiple images to verify the

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Hagiwara, Hayato, Yasufumi Touma, Kenichi Asami, and Mochimitsu Komori. "FPGA-Based Stereo Vision System Using Gradient Feature Correspondence." Journal of Robotics and Mechatronics 27, no. 6 (2015): 681–90. http://dx.doi.org/10.20965/jrm.2015.p0681.

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<div class=""abs_img""><img src=""[disp_template_path]/JRM/abst-image/00270006/10.jpg"" width=""300"" /> Mobile robot with a stereo vision</div>This paper describes an autonomous mobile robot stereo vision system that uses gradient feature correspondence and local image feature computation on a field programmable gate array (FPGA). Among several studies on interest point detectors and descriptors for having a mobile robot navigate are the Harris operator and scale-invariant feature transform (SIFT). Most of these require heavy computation, however, and using them may burden s

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Wang, Sheng Ke, Lili Liu, and Xiaowei Xu. "Vehicle Logo Recognition Based on Local Feature Descriptor." Applied Mechanics and Materials 263-266 (December 2012): 2418–21. http://dx.doi.org/10.4028/www.scientific.net/amm.263-266.2418.

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In this paper, we present a comparison of the scale-invariant feature transforms (SIFT)-based feature-matching scheme and the speeded up robust features (SURF)-based feature-matching scheme in the field of vehicle logo recognition. We capture a set of logo images which are varied in illumination, blur, scale, and rotation. Six kinds of vehicle logo training set are formed using 25 images in average and the rest images are used to form the testing set. The Logo Recognition system that we programmed indicates a high recognition rate of the same kind of query images through adjusting different pa

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Sahidan, Nurul Fatihah, Ahmad Khairi Juha, and Zaidah Ibrahim. "Evaluation of basic convolutional neural network and bag of features for leaf recognition." Indonesian Journal of Electrical Engineering and Computer Science 14, no. 1 (2019): 327. http://dx.doi.org/10.11591/ijeecs.v14.i1.pp327-332.

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This paper presents the evaluation of basic Convolutional Neural Network (CNN) and Bag of Features (BoF) for Leaf Recognition. In this study, the performance of basic CNN and BoF for leaf recognition using a publicly available dataset called Folio dataset has been investigated. CNN has proven its powerful feature representation power in computer vision. The same goes with BoF where it has set new performance standards on popular image classification benchmarks and has achieved scalability breakthrough in image retrieval. The feature that is being utilized in the BoF is Speeded-Up Robust Featur

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Zhu, Yaguang, Chaoyu Jia, Chao Ma, and Qiong Liu. "SURF-BRISK–Based Image Infilling Method for Terrain Classification of a Legged Robot." Applied Sciences 9, no. 9 (2019): 1779. http://dx.doi.org/10.3390/app9091779.

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In this study, we propose adaptive locomotion for an autonomous multilegged walking robot, an image infilling method for terrain classification based on a combination of speeded up robust features, and binary robust invariant scalable keypoints (SURF-BRISK). The terrain classifier is based on the bag-of-words (BoW) model and SURF-BRISK, both of which are fast and accurate. The image infilling method is used for identifying terrain with obstacles and mixed terrain; their features are magnified to help with recognition of different complex terrains. Local image infilling is used to improve low a

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