Статті в журналах: "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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3

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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4

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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7

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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8

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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Abstract. 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&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&ndash;0.6 pixels.

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10

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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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.

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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 (March 1, 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 picked around feature description regions. After that we perform one more geometric transformation which is rotation, and is used to improve visual appearance of image. By using this, we perform Speeded Up Robust Features (SURF) feature which highlights the high pixel value of the image. After that we compare two different images and by comparing all features of that object from image, the desired object detected in a scene.

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ZHANG Hao-su, 张昊骕, 朱晓龙 ZHU Xiao-long, 胡新洲 HU Xin-zhou, and 任洪娥 REN Hong-e. "Shot segmentation technology based on SURF features and SIFT features." Chinese Journal of Liquid Crystals and Displays 34, no. 5 (2019): 521–29. http://dx.doi.org/10.3788/yjyxs20193405.0521.

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13

Wang, Yin-Tien, Chen-Tung Chi, and Ying-Chieh Feng. "Robot mapping using local invariant feature detectors." Engineering Computations 31, no. 2 (February 25, 2014): 297–316. http://dx.doi.org/10.1108/ec-01-2013-0024.

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Purpose – To build a persistent map with visual landmarks is one of the most important steps for implementing the visual simultaneous localization and mapping (SLAM). The corner detector is a common method utilized to detect visual landmarks for constructing a map of the environment. However, due to the scale-variant characteristic of corner detection, extensive computational cost is needed to recover the scale and orientation of corner features in SLAM tasks. The purpose of this paper is to build the map using a local invariant feature detector, namely speeded-up robust features (SURF), to detect scale- and orientation-invariant features as well as provide a robust representation of visual landmarks for SLAM. Design/methodology/approach – SURF are scale- and orientation-invariant features which have higher repeatability than that obtained by other detection methods. Furthermore, SURF algorithms have better processing speed than other scale-invariant detection method. The procedures of detection, description and matching of regular SURF algorithms are modified in this paper in order to provide a robust representation of visual landmarks in SLAM. The sparse representation is also used to describe the environmental map and to reduce the computational complexity in state estimation using extended Kalman filter (EKF). Furthermore, the effective procedures of data association and map management for SURF features in SLAM are also designed to improve the accuracy of robot state estimation. Findings – Experimental works were carried out on an actual system with binocular vision sensors to prove the feasibility and effectiveness of the proposed algorithms. EKF SLAM with the modified SURF algorithms was applied in the experiments including the evaluation of accurate state estimation as well as the implementation of large-area SLAM. The performance of the modified SURF algorithms was compared with those obtained by regular SURF algorithms. The results show that the SURF with less-dimensional descriptors is the most suitable representation of visual landmarks. Meanwhile, the integrated system is successfully validated to fulfill the capabilities of visual SLAM system. Originality/value – The contribution of this paper is the novel approach to overcome the problem of recovering the scale and orientation of visual landmarks in SLAM tasks. This research also extends the usability of local invariant feature detectors in SLAM tasks by utilizing its robust representation of visual landmarks. Furthermore, data association and map management designed for SURF-based mapping in this paper also give another perspective for improving the robustness of SLAM systems.

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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 (September 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, we introduce a descriptor, called MoSURF (Motion Speeded-Up Robust Features), based on SURF (Speeded-Up Robust Features), HOG (Histogram of Oriented Gradient), HOF (Histograms of Optical Flow), MBH(Motion Boundary Histograms), and trajectory information, which can effectively describe motion information and are complementary to each other. We evaluate our video representation under action classification on three motion video datasets namely KTH, YouTube, and UCF50. Compared with state-of-the-art methods, the proposed method shows advanced results on all datasets.

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15

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 (October 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 range, can effectively reduce the computational cost of image features, and maintain the high retrieval performance as well

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16

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 practice.

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Yi, Ou Yang. "Human Pose Tracking For Video Using SURF features." Applied Mechanics and Materials 39 (November 2010): 203–9. http://dx.doi.org/10.4028/www.scientific.net/amm.39.203.

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In this paper, a novel method based on SURF features method for tracking human motion in monocular videos is proposed. With a initial human skeleton joint point template, we use the probability density propagation of the particle filers through the model. This algorithm can automatically achieve right human motion figure from tracking failures, such as occlusion and auto-occlusion problem. Experimental results from 20 classes monocular videos show that the new Based on SURF method is robust and the tracking results are good.

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Ma, Zhen Yuan, Shi Xu Shi, Li Xian Yuan, Pei Chang Gu, and Han Huang. "Image Matching Application Based on Modified SURF Algorithm." Applied Mechanics and Materials 423-426 (September 2013): 2591–96. http://dx.doi.org/10.4028/www.scientific.net/amm.423-426.2591.

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The key technique to increase the accuracy of electronic marking is the technique of image matching, namely to match two doubtfully duplicate images. Currently there are few technologies aiming for features on test paper images with high performance on matching accuracy. The research is based on SURF algorithm and specific to the features of test paper images. Thus the research is to put forward the modified algorithm with constraints among feature spots of orientation angles on their geometrical positions, including differential constraints on critical points from approximate blank test papers with less individual features at the same time. After processing and analyzing 2,000 test paper gathered from one actual examination, the results show that the modified detection algorithm has 100% false rejection rate and 100% accuracy when it is used to detect the test paper matching.

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Yang, Zhanlong, Dinggang Shen, and Pew-Thian Yap. "Image mosaicking using SURF features of line segments." PLOS ONE 12, no. 3 (March 15, 2017): e0173627. http://dx.doi.org/10.1371/journal.pone.0173627.

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Ajao, Jumoke F., Rafiu M. Isiaka, and Ronke S. Babatunde. "A Hybridized Feature Extraction Model for Offline Yorùbá Document Recognition." Asian Journal of Research in Computer Science 15, no. 4 (May 16, 2023): 42–59. http://dx.doi.org/10.9734/ajrcos/2023/v15i4329.

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Document recognition is required to convert handwritten and text documents into digital equivalents, making them more easily accessible and convenient to store. This study combined feature extraction techniques for recognizing Yorùbá documents in an effort to preserve the cultural values and heritages of the Yorùbá people. Ten Yorùbá documents were acquired from Kwara State University’s Library, and ten indigenous literate writers wrote the handwritten version of the documents. These were digitized using HP Scanjet300 and pre-processed. The pre-processed image served as input to the Local Binary Pattern, Speeded-Up-Robust-Features and Histogram of Gradient. The combined extracted feature vectors were input into the Genetic Algorithm. The reduced feature vector was fed into Support Vector Machine. A 10-folds cross-validation was used to train the model: LBP-GA, SURF-GA, HOG-GA, LBP-SURF-GA, HOG-SURF-GA, LBP-HOG-GA and LBP-HOG-SURF-GA. LBP-HOG-SURF-GA for Yorùbá printed text gave 90.0% precision, 90.3% accuracy and 15.5% FPR. LBP-HOG-SURF-GA for Handwritten Yorùbá document showed 80.9% precision, 82.6% accuracy and 20.4% (FPR) LBP-HOG-SURF-GA for CEDAR gave 98.0% precision, 98.4% accuracy and 2.6% FPR. LBP-HOG-SURF-GA for MNIST gave 99% precision, 99.5% accuracy, 99.0% and 1.1% FPR. The results of the hybridized feature extractions (LBP-HOG-SURF) demonstrated that the proposed work improves significantly on the various classification metrics.

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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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Dong, Ao Shuang, Ben Qiang Yang, Dan Yang Zhao, Bao Chun He, Li Bo Zhang, Guang Ming Yang, Qiang Tong, Tian Han Gao, and Hui Yan Jiang. "Research of Medical Image Non-Rigid Registration Based on TPS-SEMISURF Algorithm." Advanced Materials Research 791-793 (September 2013): 2112–17. http://dx.doi.org/10.4028/www.scientific.net/amr.791-793.2112.

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Aiming at avoiding misregistration in complicated medical image registration based on SURF (Speed-Up Robust Features)-TPS (Thin-Plate Spline), we propose a novel algorithm. This method is based on SURF and human interaction method for feature extraction. Then we improve SURF-TPS and propose an algorithm named TPS-SEMISURF which obtains the deformation field by calculating the Thin-plate spline of the feature points, and finally does the medical image non-rigid registration according to the parameters. Experimental results showed that the proposed method can register medical images effectively. It has a good robustness and owns better precision and rate than traditional algorithm.

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Kok, Kai Yit, and Parvathy Rajendran. "A Descriptor-Based Advanced Feature Detector for Improved Visual Tracking." Symmetry 13, no. 8 (July 24, 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), Minimum Eigenvalue with FREAK, Features from Accelerated Segment Test (FAST) with FREAK, and Binary Robust Invariant Scalable Keypoints (BRISK) with FREAK. A visual tracking dataset is used in this performance evaluation in terms of accuracy and computational cost. The results have shown that combining the SRF detector with the SRF descriptor is preferable, as it has on average the highest accuracy. Additionally, the computational cost of SRF with SRF is much lower than the others.

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Awad, Ali Ismail, and M. Hassaballah. "Bag-of-Visual-Words for Cattle Identification from Muzzle Print Images." Applied Sciences 9, no. 22 (November 15, 2019): 4914. http://dx.doi.org/10.3390/app9224914.

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Cattle, buffalo and cow identification plays an influential role in cattle traceability from birth to slaughter, understanding disease trajectories and large-scale cattle ownership management. Muzzle print images are considered discriminating cattle biometric identifiers for biometric-based cattle identification and traceability. This paper presents an exploration of the performance of the bag-of-visual-words (BoVW) approach in cattle identification using local invariant features extracted from a database of muzzle print images. Two local invariant feature detectors—namely, speeded-up robust features (SURF) and maximally stable extremal regions (MSER)—are used as feature extraction engines in the BoVW model. The performance evaluation criteria include several factors, namely, the identification accuracy, processing time and the number of features. The experimental work measures the performance of the BoVW model under a variable number of input muzzle print images in the training, validation, and testing phases. The identification accuracy values when utilizing the SURF feature detector and descriptor were 75%, 83%, 91%, and 93% for when 30%, 45%, 60%, and 75% of the database was used in the training phase, respectively. However, using MSER as a points-of-interest detector combined with the SURF descriptor achieved accuracies of 52%, 60%, 67%, and 67%, respectively, when applying the same training sizes. The research findings have proven the feasibility of deploying the BoVW paradigm in cattle identification using local invariant features extracted from muzzle print images.

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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 (December 28, 2017): 178. http://dx.doi.org/10.36722/sst.v3i4.232.

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Abstrak - 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.Kata Kunci – Biometric, Footprint, SURF, SIFT, K- NNAbstract - 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.Keywords – Biometric, Footprint, SURF, SIFT, K- NN

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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 (January 1, 2010): 29–34. http://dx.doi.org/10.20870/ijvr.2010.9.4.2788.

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Анотація:

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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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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28

Liang, Buyun, Na Li, Zheng He, Zhongyuan Wang, Youming Fu, and Tao Lu. "News Video Summarization Combining SURF and Color Histogram Features." Entropy 23, no. 8 (July 30, 2021): 982. http://dx.doi.org/10.3390/e23080982.

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Because the data volume of news videos is increasing exponentially, a way to quickly browse a sketch of the video is important in various applications, such as news media, archives and publicity. This paper proposes a news video summarization method based on SURF features and an improved clustering algorithm, to overcome the defects in existing algorithms that fail to account for changes in shot complexity. Firstly, we extracted SURF features from the video sequences and matched the features between adjacent frames, and then detected the abrupt and gradual boundaries of the shot by calculating similarity scores between adjacent frames with the help of double thresholds. Secondly, we used an improved clustering algorithm to cluster the color histogram of the video frames within the shot, which merged the smaller clusters and then selected the frame closest to the cluster center as the key frame. The experimental results on both the public and self-built datasets show the superiority of our method over the alternatives in terms of accuracy and speed. Additionally, the extracted key frames demonstrate low redundancy and can credibly represent a sketch of news videos.

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29

Liu, Jinliang, and FanLiang Bu. "Improved RANSAC features image-matching method based on SURF." Journal of Engineering 2019, no. 23 (December 1, 2019): 9118–22. http://dx.doi.org/10.1049/joe.2018.9198.

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30

Zhao, Yibing, Feng Ding, Xuecai Yu, Ronghui Zhang, and Xiumei Xiang. "A New Waters Hole Detection and Tracking Method for UGV in Cross-Country Environment." International Journal of Pattern Recognition and Artificial Intelligence 30, no. 08 (July 17, 2016): 1655024. http://dx.doi.org/10.1142/s0218001416550247.

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Environment perception is one of the important issues for unmanned ground vehicle (UGV). It is necessary to develop waters hole detection and tracking method in cross-country environment. This paper is related to the waters hole detection and tracking by using visual information. Image processing strategies based on support vector machine (SVM) and speeded up robust feature (SURF) methods are employed to detect and track waters hole. It focuses on how to extract the waters feature descriptor by exploring the machine learning algorithm. Based on the S/V color features and Gray Level Co-occurrence Matrix, the waters feature descriptor is extracted. The radial basis function (RBF) kernel function and the sampling-window size are determined by using the SVM classifier. The optimal parameters are obtained under the cross-validation conditions by the grid method. In terms of waters tracking, SURF feature matching method is applied to extract the remarkable feature points, then to observe the relation between feature point movement of adjacent frames and scale change ratio. Experiments show that SURF algorithm can still be effective to detect and match the remarkable feature points, against the negative effects of waters scale transformation and affine transform. The conclusion is that the computing speed of SURF algorithm is about three times faster than that of scale-invariant feature transform (SIFT) algorithm, and the comprehensive performance of SURF algorithm is better.

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31

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 (October 31, 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 pencarian matrix homography. Metode warpPerspective digunakan untuk penggabungan kedua input yang memiliki kecocokan keypoint. Pengujian terdiri dari beberapa jenis variasi antara lain variasi minimal perpotongan, variasi skala dan variasi rotasi. Variasi minimal perpotongan yang menghasilkan nilai minimal perpotongan sebesar 15% dan jumlah minimal keypoint berkesesuaian antar kedua citra yang dapat digabungkan adalah 5. Variasi rotasi untuk berapapun perbedaan sudut antara kedua citra tetap dapat digabungkan. Variasi skala minimal citra yang dapat digabungkan adalah skala citra yang diperkecil hingga 75% dari ukuran citra aslinya, dan skala citra yang diperbesar hingga 600% dari ukuran aslinya untuk maksimal variasi perbesaran skala. Kata kunci—Foto udara, Penggabungan gambar, SURF, FLANN, RANSAC AbstractPrototype of stitching aerial photograph UAV using SURF algorithm is a system that is designed to stitch the image. The image is generated imagery UAV fixed-wings. The output of this system is a display image with a wider object. This system is designed to be able to merge aerial images by using SURF algorithm, Flann, RANSAC, and warpPerspective. SURF algorithm is used as a keypoint detector from each of the input images. Flann method to perform keypoint matching is found. RANSAC homography matrix used for the search. WarpPerspective method used for merging the two inputs that have a match keypoint. The test consists of several types of variations such as the intersection of the minimal variation, variation in scale and rotation variations. Variation that produces intersection minimum value of 15% and a minimum number of keypoint accords between the two images can be combined is 5. Variation of rotation to any angle difference between the two images can still be combined. Minimum scale variations which can be combined image is the image scale is reduced to 75% of the size of the original image, and the image scale is enlarged to 600% of its original size to a maximum variation of magnification scale. Keywords—Aerial Photograph, Stitching Images, SURF, FLANN, RANSAC

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32

Heidari, Hossein, and Mohammad Mardani. "Applying Artificial Bee Colony Algorithm for feature optimization in UAV Navigation." INTERNATIONAL JOURNAL OF COMPUTERS & TECHNOLOGY 15, no. 7 (May 18, 2016): 6923–32. http://dx.doi.org/10.24297/ijct.v15i7.1535.

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In this paper, the bee colony algorithm is used for optimizing features which have been extracted from UAVâ€™s digital camera by SURF algorithm. To do that, the local map is stored in UAV memory before flight. During flight, images will be captured by a digital camera and the features in successive image will be extracted using SURF algorithm because SURF algorithm is considered highly insensitive to environmental light, scale changes and noise. Then, the extracted features will be optimized using the bee colony algorithm and will be compared with the original map features to find the location and direction of UAV. Simulations show that proposed algorithm has good precision and is robust to scale changes, light intensity variations, and noise.

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33

BABER, JUNAID, NITIN AFZULPURKAR, and SHIN'ICHI SATOH. "A FRAMEWORK FOR VIDEO SEGMENTATION USING GLOBAL AND LOCAL FEATURES." International Journal of Pattern Recognition and Artificial Intelligence 27, no. 05 (August 2013): 1355007. http://dx.doi.org/10.1142/s0218001413550070.

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Rapid increase in video databases has forced the industry to have efficient and effective frameworks for video retrieval and indexing. Video segmentation into scenes is widely used for video summarization, partitioning, indexing and retrieval. In this paper, we propose a framework for scene detection mainly based on entropy and Speeded Up Robust Features (SURF) features. First, we detect the fade and abrupt boundaries based on frame entropy analysis and SURF features matching. Fade boundaries are smart indication of scenes beginning or ending in many videos and dramas, and are detected by frame entropy analysis. Before abrupt boundary detection, unnecessary frames which are obviously not abrupt boundaries, such as blank screens, high intensity influenced images, sliding credits, are removed. Candidate boundaries are detected to make SURF features efficient for abrupt boundary detection, and SURF features between candidate boundaries and their adjacent frames are used to detect the abrupt boundaries. Second, key frames are extracted from abrupt shots. We evaluate our key frame extraction with other famous algorithms and show the effectiveness of the key frames. Finally, scene boundaries are detected using sliding window of size K over the key frames in temporal order. In experimental evaluation on the TRECVID-2007 shot boundary test set, the algorithm for shot boundary achieves substantial improvements over state-of-the-art methods with the precision of 99% and the recall of 97.8%. Experimental results for video segmentation into scenes are also promising, compared to famous state-of-the-art techniques.

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34

Hang, Yuan. "Thyroid Nodule Classification in Ultrasound Images by Fusion of Conventional Features and Res-GAN Deep Features." Journal of Healthcare Engineering 2021 (July 22, 2021): 1–7. http://dx.doi.org/10.1155/2021/9917538.

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In spite of the gargantuan number of patients affected by the thyroid nodule, the detection at an early stage is still a challenging task. Thyroid ultrasonography (US) is a noninvasive, inexpensive procedure widely used to detect and evaluate the thyroid nodules. The ultrasonography method for image classification is a computer-aided diagnostic technology based on image features. In this paper, we illustrate a method which involves the combination of the deep features with the conventional features together to form a hybrid feature space. Several image enhancement techniques, such as histogram equalization, Laplacian operator, logarithm transform, and Gamma correction, are undertaken to improve the quality and characteristics of the image before feature extraction. Among these methods, applying histogram equalization not only improves the brightness and contrast of the image but also achieves the highest classification accuracy at 69.8%. We extract features such as histograms of oriented gradients, local binary pattern, SIFT, and SURF and combine them with deep features of residual generative adversarial network. We compare the ResNet18, a residual convolutional neural network with 18 layers, with the Res-GAN, a residual generative adversarial network. The experimental result shows that Res-GAN outperforms the former model. Besides, we fuse SURF with deep features with a random forest model as a classifier, which achieves 95% accuracy.

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35

Zhao, Yun Ji, and Hai Long Pei. "Improved Vision-Based Algorithm for Unmanned Aerial Vehicles Autonomous Landing." Applied Mechanics and Materials 273 (January 2013): 560–65. http://dx.doi.org/10.4028/www.scientific.net/amm.273.560.

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In vision-based autonomous landing system of UAV (Unmanned Aerial Vehicle), the efficiency of object detection and tracking will directly affect the control system. An improved algorithm of SURF (Speed Up Robust Features) will resolve the problem which is inefficiency of the SURF algorithm in the autonomous landing system of UAV. The improved algorithm is composed of three steps: first, detect the region of the target using the Camshift algorithm; second, detect the feature points in the region of the above acquired using the SURF algorithm; third, do the matching between the template target and the region of target in frame. The results of experiments and theoretical analysis testify the efficiency of the algorithm.

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36

Mahum, Rabbia, Saeed Ur Rehman, Ofonime Dominic Okon, Amerah Alabrah, Talha Meraj, and Hafiz Tayyab Rauf. "A Novel Hybrid Approach Based on Deep CNN to Detect Glaucoma Using Fundus Imaging." Electronics 11, no. 1 (December 22, 2021): 26. http://dx.doi.org/10.3390/electronics11010026.

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Glaucoma is one of the eye diseases stimulated by the fluid pressure that increases in the eyes, damaging the optic nerves and causing partial or complete vision loss. As Glaucoma appears in later stages and it is a slow disease, detailed screening and detection of the retinal images is required to avoid vision forfeiture. This study aims to detect glaucoma at early stages with the help of deep learning-based feature extraction. Retinal fundus images are utilized for the training and testing of our proposed model. In the first step, images are pre-processed, before the region of interest (ROI) is extracted employing segmentation. Then, features of the optic disc (OD) are extracted from the images containing optic cup (OC) utilizing the hybrid features descriptors, i.e., convolutional neural network (CNN), local binary patterns (LBP), histogram of oriented gradients (HOG), and speeded up robust features (SURF). Moreover, low-level features are extracted using HOG, whereas texture features are extracted using the LBP and SURF descriptors. Furthermore, high-level features are computed using CNN. Additionally, we have employed a feature selection and ranking technique, i.e., the MR-MR method, to select the most representative features. In the end, multi-class classifiers, i.e., support vector machine (SVM), random forest (RF), and K-nearest neighbor (KNN), are employed for the classification of fundus images as healthy or diseased. To assess the performance of the proposed system, various experiments have been performed using combinations of the aforementioned algorithms that show the proposed model based on the RF algorithm with HOG, CNN, LBP, and SURF feature descriptors, providing ≤99% accuracy on benchmark datasets and 98.8% on k-fold cross-validation for the early detection of glaucoma.

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37

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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38

Bao, Wei, Li Xin Ji, Shi Lin Gao, Xing Li, and Li Xiong Liu. "Video Copy Detection Based on Fusion of Spatio-Temporal Features." Applied Mechanics and Materials 347-350 (August 2013): 3653–61. http://dx.doi.org/10.4028/www.scientific.net/amm.347-350.3653.

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A video copy detection method based on fusion of spatio-temporal features is proposed in this paper. Firstly, trajectories are built and lens boundaries are detected by SURF features analyzing, then normalized histogram is used to describe spatio-temporal behavior of trajectories, the bag of visual words is constructed by trajectories behavior clustering, word frequency vectors and SURF features with behavior labels are extracted to express spatio-temporal content of lens, finally, duplicates are detected efficiently based on grade-match. The experimental results show the performance of this method is improved greatly compared with other similar methods.

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39

Wu, Yun-Hua, Lin-Lin Ge, Feng Wang, Bing Hua, Zhi-Ming Chen, and Feng Yu. "Fast Image Registration for Spacecraft Autonomous Navigation Using Natural Landmarks." International Journal of Aerospace Engineering 2018 (August 12, 2018): 1–12. http://dx.doi.org/10.1155/2018/8324298.

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In order to satisfy the real-time requirement of spacecraft autonomous navigation using natural landmarks, a novel algorithm called CSA-SURF (chessboard segmentation algorithm and speeded up robust features) is proposed to improve the speed without loss of repeatability performance of image registration progress. It is a combination of chessboard segmentation algorithm and SURF. Here, SURF is used to extract the features from satellite images because of its scale- and rotation-invariant properties and low computational cost. CSA is based on image segmentation technology, aiming to find representative blocks, which will be allocated to different tasks to speed up the image registration progress. To illustrate the advantages of the proposed algorithm, PCA-SURF, which is the combination of principle component analysis and SURF, is also analyzed in this paper for comparison. Furthermore, random sample consensus (RANSAC) algorithm is applied to eliminate the false matches for further accuracy improvement. The simulation results show that the proposed strategy obtains good results, especially in scaling and rotation variation. Besides, CSA-SURF decreased 50% of the time in extraction and 90% of the time in matching without losing the repeatability performance by comparing with SURF algorithm. The proposed method has been demonstrated as an alternative way for image registration of spacecraft autonomous navigation using natural landmarks.

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40

Jeyapal, Akilandeswari, Jothi Ganesan, Sabeenian Royappan Savarimuthu, Iyyanar Perumal, Paramasivam Muthan Eswaran, Lakshmanan Subramanian, and Naveenkumar Anbalagan. "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 (January 1, 2020): 21–26. http://dx.doi.org/10.1166/jctn.2020.8623.

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Анотація:

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 of an image and retrieve the best matched image. Based on our experiments, we compare those algorithms on parameters such as sum of square difference (SDD), precision, recall, number of detected, matched features and processing time. Empirically, we have found that SURF algorithm produce minimum SSD score to achieve best matching. The MSER and MinEign algorithm extracts high and low number of features respectively. In respect of processing time, BRISK takes maximum and FAST method takes minimum time when compared to the other algorithms.

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41

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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Анотація:

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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42

Zhou, Guodong, Huailiang Zhang, and Raquel Martínez Lucas. "Compressed sensing image restoration algorithm based on improved SURF operator." Open Physics 16, no. 1 (December 31, 2018): 1033–45. http://dx.doi.org/10.1515/phys-2018-0124.

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Анотація:

Abstract Aiming at the excellent descriptive ability of SURF operator for local features of images, except for the shortcoming of global feature description ability, a compressed sensing image restoration algorithm based on improved SURF operator is proposed. The SURF feature vector set of the image is extracted, and the vector set data is reduced into a single high-dimensional feature vector by using a histogram algorithm, and then the image HSV color histogram is extracted.MSA image decomposition algorithm is used to obtain sparse representation of image feature vectors. Total variation curvature diffusion method and Bayesian weighting method perform image restoration for data smoothing feature and local similarity feature of texture part respectively. A compressed sensing image restoration model is obtained by using Schatten-p norm, and image color supplement is performed on the model. The compressed sensing image is iteratively solved by alternating optimization method, and the compressed sensing image is restored. The experimental results show that the proposed algorithm has good restoration performance, and the restored image has finer edge and texture structure and better visual effect.

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43

Kumari, S. Sandhya, and K. Sandhya Rani. "Big Data Classification of Ultrasound Doppler Scan Images Using a Decision Tree Classifier Based on Maximally Stable Region Feature Points." International Journal on Recent and Innovation Trends in Computing and Communication 10, no. 8 (August 31, 2022): 76–87. http://dx.doi.org/10.17762/ijritcc.v10i8.5679.

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Анотація:

The classification of ultrasound scan images is important in monitoring the development of prenatal and maternal structures. This paper proposes a big data classification system for ultrasound Doppler scan images that combines the residual of maximally stable extreme regions and speeded up robust features (SURF) with a decision tree classifier. The algorithm first preprocesses the ultrasound scan images before detecting the maximally stable extremal regions (MSER). A few essential regions are chosen from the MSER regions, along with the residual region that provides the best Region of Interest (ROI). SURF features points that best represent the region are detected using the gradient of the estimated cumulative region of interest. To extract the feature from the pixels that surround the SURF feature points, the Triangular Vertex Transform (TVT) transform is used. A decision tree classifier is used to train the extracted TVT features. The proposed ultrasound scan image classification system is validated using performance parameters such as accuracy, specificity, precision, sensitivity, and F1 score. For validation, a large dataset of 12,400 scan images collected from 1792 patients is used. The proposed method has an F1score of 94.12%, sensitivity, specificity, precision, and accuracy of 93.57%, 93.57%, and 97.96%, respectively. The evaluation results show that the proposed algorithm for classifying Doppler scan images is better than other algorithms that have been used in the past.

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44

Rachapudi, Venubabu, and Golagani Lavanya Devi. "Feature Selection for Histopathological Image Classification using levy Flight Salp Swarm Optimizer." Recent Patents on Computer Science 12, no. 4 (August 19, 2019): 329–37. http://dx.doi.org/10.2174/2213275912666181210165129.

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Background: An efficient feature selection method for Histopathological image classification plays an important role to eliminate irrelevant and redundant features. Therefore, this paper proposes a new levy flight salp swarm optimizer based feature selection method. Methods: The proposed levy flight salp swarm optimizer based feature selection method uses the levy flight steps for each follower salp to deviate them from local optima. The best solution returns the relevant and non-redundant features, which are fed to different classifiers for efficient and robust image classification. Results: The efficiency of the proposed levy flight salp swarm optimizer has been verified on 20 benchmark functions. The anticipated scheme beats the other considered meta-heuristic approaches. Furthermore, the anticipated feature selection method has shown better reduction in SURF features than other considered methods and performed well for histopathological image classification. Conclusion: This paper proposes an efficient levy flight salp Swarm Optimizer by modifying the step size of follower salp. The proposed modification reduces the chances of sticking into local optima. Furthermore, levy flight salp Swarm Optimizer has been utilized in the selection of optimum features from SURF features for the histopathological image classification. The simulation results validate that proposed method provides optimal values and high classification performance in comparison to other methods.

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45

Chen, Wen Yu, Wen Zhi Xie, Yan Li Zhao, and Zhong Bo Hao. "Research on Method of Items Recognition Based on SURF Algorithm." Applied Mechanics and Materials 121-126 (October 2011): 4630–34. http://dx.doi.org/10.4028/www.scientific.net/amm.121-126.4630.

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Items detection and recognition have become one of hotspots in the field of computer vision research. Based on image features method has the advantage of low amount of information, fast running speed, high precision, and SIFT algorithm is one of them. But traditional SIFI algorithm have large amount of calculation data and spend long time to compute in terms of items recognition. Therefore, this paper come up with a method of items recognition based on SURF. This article elaborates the basic principle of SURF algorithm that firstly use SURF algorithm to extract feature points of item image, secondly adopt Euclidean distance method to find corresponding interest points of image, and finally get the image after items recognition combination with mapping relation of item image using RANSAC(Random Sample Consesus). Experimental results show that the system of item recognition based on SURF algorithm have better effect on matching recognition, higher instantaneity, better robustness.

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46

Jiang, Haili, Panpan Liu, Qingqing Yang, Liang Xu, and Shuai Zhang. "A Fast Image Matching Method Based on Improved SURF." Journal of Physics: Conference Series 2575, no. 1 (August 1, 2023): 012002. http://dx.doi.org/10.1088/1742-6596/2575/1/012002.

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Abstract In order to solve the problems of low matching accuracy, slow speed and high system overhead in image matching methods, a rotation binary descriptor construction method based on Speed Up Robust Features (SURF) feature point detection is designed by using different Fast Library for Approximate Nearest Neighbors (FLANN) parameters and the filtering mechanism to screen out wrong matches according to the types of feature descriptors constructed in different feature extraction algorithms. This method ensures scale and rotation invariant while simplifying the representation of feature descriptors and speeding up the calculation speed in the initial stage of matching by combining the binary characteristics of descriptors. Finally, the Hamming distance is used as the filtering mechanism to improve the success rate of the final matching. The experimental results show that the accuracy of image matching is improved by 1.5% and the matching time is improved by 0.116s, while the robustness of the image to noise and rotation is ensured.

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47

Ahmed, Tanzia, Tanvir Rahman, Bir Ballav Roy, and Jia Uddin. "Drone Detection by Neural Network Using GLCM and SURF Features." Journal of Information Systems and Telecommunication 9, no. 33 (April 12, 2021): 15–24. http://dx.doi.org/10.52547/jist.9.33.15.

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48

Neubert, Peer, Niko Sünderhauf, and Peter Protzel. "FASTSLAM USING SURF FEATURES: AN EFFICIENT IMPLEMENTATION AND PRACTICAL EXPERIENCES." IFAC Proceedings Volumes 40, no. 15 (2007): 487–92. http://dx.doi.org/10.3182/20070903-3-fr-2921.00083.

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49

Mehta, Tejas, and Chandu Bhensdadia. "Adaptive Near Duplicate Image Retrieval Using SURF and CNN Features." International Journal of Intelligent Engineering and Systems 12, no. 5 (October 31, 2019): 104–15. http://dx.doi.org/10.22266/ijies2019.1031.11.

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50

Dhar, Prashengit. "A New Flower Classification System Using LBP and SURF Features." International Journal of Image, Graphics and Signal Processing 11, no. 5 (May 8, 2019): 13–20. http://dx.doi.org/10.5815/ijigsp.2019.05.02.

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