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Wang, Yin Tien, Chen Tung Chi, and Ying Chieh Feng. "Robot Simultaneous Localization and Mapping Using Speeded-Up Robust Features." Applied Mechanics and Materials 284-287 (January 2013): 2142–46. http://dx.doi.org/10.4028/www.scientific.net/amm.284-287.2142.
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An algorithm for robot mapping is proposed in this paper using the method of speeded-up robust features (SURF). Since SURFs are scale- and orientation-invariant features, they have higher repeatability than that of the features obtained by other detection methods. Even in the cases of using moving camera, the SURF method can robustly extract the features from image sequences. Therefore, SURFs are suitable to be utilized as the map features in visual simultaneous localization and mapping (SLAM). In this article, the procedures of detection and matching of the SURF method are modified to improve the image processing speed and feature recognition rate. The sparse representation of SURF is also utilized to describe the environmental map in SLAM tasks. The purpose is to reduce the computation complexity in state estimation using extended Kalman filter (EKF). The EKF SLAM with SURF-based map is developed and implemented on a binocular vision system. The integrated system has been successfully validated to fulfill the basic capabilities of SLAM system.
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G, Kiran Kumar, Malathi Rani D, and Guru Mohan Rao E. "Fast palmprint retrieval using speed up robust features." Indian Journal of Science and Technology 13, no. 31 (2020): 3204–12. https://doi.org/10.17485/IJST/v13i31.404.
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Abstract <strong>Background/Objective:</strong> Biometric usage is increasing in exponential series in all organisations for multiple purposes like employee attendance, Aadhaar based authentication and secure login using finger print etc. This biometric process should be as quick as possible without making much delay to retrieve the respective finger print. So an efficient quick retrieval procedure is required, in this regards a fast retrieval method for palm prints is proposed in this article. <strong>Method:</strong> This method uses Speed up Robust Features (SURF) and an efficient look up table for fast retrieval of palm prints. A key is computed for each palmprint by matching with a pre-selected palmprint called representative. This key is used, to place the palmprint into the look up table like traditional database record. To identify a query palmprint, key is computed and selects a set of palm prints from the look up table which are having similar key as possible matches. <strong>Findings:</strong> This proposed solution is experimented with multiple representative images to check the improved performance. As an outcome we could achieve better hit rate by comparing with existing system<strong>Novelty:</strong>. This proposed method makes the new palm prints dynamically without disturbing the current records in the system. The entire solution is experimented on benchmark PolyU palmprint database of 7,753 images and significant performance is shown in results. This proposed solution shows better results with respect to hit rate and miss rate. <strong>Keywords:</strong> Palmprint; index key; SURF; similarity score; representative
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Bay, Herbert, Andreas Ess, Tinne Tuytelaars, and Luc Van Gool. "Speeded-Up Robust Features (SURF)." Computer Vision and Image Understanding 110, no. 3 (2008): 346–59. http://dx.doi.org/10.1016/j.cviu.2007.09.014.
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Wu, Shu Guang, Shu He, and Xia Yang. "Study on Image Matching Based on Speed up Robust Features Method." Advanced Materials Research 1044-1045 (October 2014): 1352–56. http://dx.doi.org/10.4028/www.scientific.net/amr.1044-1045.1352.
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Image registration is one of the fundamental problems in digital image processing, which is a prerequisite and key step for further comprehensive analysis,considering the advantages of the algorithm in speed and its disadvantage of more false matching points,a image matching method based on RANSAC and surf isproposed.The experiments results show that compared with the other algorithms,the surf algorithm improves the matching speed，as well as the matching accuracy，and exhibits good performance in terms of resisting rotation，noise,and brightness changes．
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Pandey, Ramesh Chand, Sanjay Kumar Singh, and K. K. Shukla. "Passive Copy- Move Forgery Detection Using Speed-Up Robust Features, Histogram Oriented Gradients and Scale Invariant Feature Transform." International Journal of System Dynamics Applications 4, no. 3 (2015): 70–89. http://dx.doi.org/10.4018/ijsda.2015070104.
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Copy-Move is one of the most common technique for digital image tampering or forgery. Copy-Move in an image might be done to duplicate something or to hide an undesirable region. In some cases where these images are used for important purposes such as evidence in court of law, it is important to verify their authenticity. In this paper the authors propose a novel method to detect single region Copy-Move Forgery Detection (CMFD) using Speed-Up Robust Features (SURF), Histogram Oriented Gradient (HOG), Scale Invariant Features Transform (SIFT), and hybrid features such as SURF-HOG and SIFT-HOG. SIFT and SURF image features are immune to various transformations like rotation, scaling, translation, so SIFT and SURF image features help in detecting Copy-Move regions more accurately in compared to other image features. Further the authors have detected multiple regions COPY-MOVE forgery using SURF and SIFT image features. Experimental results demonstrate commendable performance of proposed methods.
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Wang, Yin-Tien, and Guan-Yu Lin. "Improvement of speeded-up robust features for robot visual simultaneous localization and mapping." Robotica 32, no. 4 (2013): 533–49. http://dx.doi.org/10.1017/s0263574713000830.
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SUMMARYA robot mapping procedure using a modified speeded-up robust feature (SURF) is proposed for building persistent maps with visual landmarks in robot simultaneous localization and mapping (SLAM). SURFs are scale-invariant features that automatically recover the scale and orientation of image features in different scenes. However, the SURF method is not originally designed for applications in dynamic environments. The repeatability of the detected SURFs will be reduced owing to the dynamic effect. This study investigated and modified SURF algorithms to improve robustness in representing visual landmarks in robot SLAM systems. Many modifications of the SURF algorithms are proposed in this study including the orientation representation of features, the vector dimension of feature description, and the number of detected features in an image. The concept of sparse representation is also used to describe the environmental map and to reduce the computational complexity when using extended Kalman filter (EKF) for state estimation. Effective procedures of data association and map management for SURFs in SLAM are also designed to improve accuracy in robot state estimation. Experimental works were performed on an actual system with binocular vision sensors to validate the feasibility and effectiveness of the proposed algorithms. The experimental examples include the evaluation of state estimation using EKF SLAM and the implementation of indoor SLAM. In the experiments, the performance of the modified SURF algorithms was compared with the original SURF algorithms. The experimental results confirm that the modified SURF provides better repeatability and better robustness for representing the landmarks in visual SLAM systems.
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Ariel, Muhammad Baresi, Ratri Dwi Atmaja, and Azizah Azizah. "Implementasi Metode Speed Up Robust Feature dan Scale Invariant Feature Transform untuk Identifikasi Telapak Kaki Individu." JURNAL Al-AZHAR INDONESIA SERI SAINS DAN TEKNOLOGI 3, no. 4 (2017): 178. http://dx.doi.org/10.36722/sst.v3i4.232.
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<p><em>Abstrak</em><strong> - 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.</strong></p><p><em><strong>Kata Kunci</strong> – Biometric, Footprint, SURF, SIFT, K- NN</em></p><p><em>Abstract</em><strong> - 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.</strong></p><p><em><strong>Keywords</strong> – Biometric, Footprint, SURF, SIFT, K- NN</em></p>
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Lu, Kai, Junli Luo, Yueqi Zhong, and Xinyu Chai. "Identification of wool and cashmere SEM images based on SURF features." Journal of Engineered Fibers and Fabrics 14 (January 2019): 155892501986612. http://dx.doi.org/10.1177/1558925019866121.
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Pattern recognition and feature extraction methods are applied to identify cashmere and wool fibers, which are two kinds of very similar animal fibers. In this article, we proposed a new identification method based on Speed Up Robust Features of fiber images. The images of wool and cashmere fibers are obtained by scanning electron microscopy. Speed Up Robust Features of fiber images are extracted, and each fiber image is regarded as a collection of feature vectors in our logic. The vectors are fed into a support vector machine for supervised learning. The findings from scanning electron microscope images indicate that this method is effective; the recognition rate is higher than 93% for a broad range of blend proportions of the two fibers.
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Long, Xu Lin, Qiang Chen, and Jun Wei Bao. "Improvement of Image Mosaic Algorithm Based on SURF." Applied Mechanics and Materials 427-429 (September 2013): 1625–30. http://dx.doi.org/10.4028/www.scientific.net/amm.427-429.1625.
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The present study concerns about feature matching in image mosaic. In order to solve the problems of low accuracy and poor applicability in the traditional speeded up robust features algorithm, this paper presents an improved algorithm. Clustering algorithm based on density instead of random sample consensus method is used to eliminate mismatching pairs. The initial matching pairs are mapped onto a plane coordinate system, which can be regarded as points, by calculating the density of each point to extract the final matching pairs. The results show that this algorithm overcomes the limitations of the traditional speeded up robust features mosaic method, improving the matching accuracy and speed, and the mosaic effect. It has certain theoretical and practical value.
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Weimert, Achim, Xueting Tan, and Xubo Yang. "Natural Feature Detection on Mobile Phones with 3D FAST." International Journal of Virtual Reality 9, no. 4 (2010): 29–34. http://dx.doi.org/10.20870/ijvr.2010.9.4.2788.
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In this paper, we present a novel feature detection approach designed for mobile devices, showing optimized solutions for both detection and description. It is based on FAST (Features from Accelerated Segment Test) and named 3D FAST. Being robust, scale-invariant and easy to compute, it is a candidate for augmented reality (AR) applications running on low performance platforms. Using simple calculations and machine learning, FAST is a feature detection algorithm known to be efficient but not very robust in addition to its lack of scale information. Our approach relies on gradient images calculated for different scale levels on which a modified9 FAST algorithm operates to obtain the values of the corner response function. We combine the detection with an adapted version of SURF (Speed Up Robust Features) descriptors, providing a system with all means to implement feature matching and object detection. Experimental evaluation on a Symbian OS device using a standard image set and comparison with SURF using Hessian matrix-based detector is included in this paper, showing improvements in speed (compared to SURF) and robustness (compared to FAST)
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BANERJEE, Saikat, Sudhir Kumar CHATURVEDI, and Surya Prakash TIWARI. "Development of Speed Up Robust Feature Algorithm for aerial image feature extraction." INCAS BULLETIN 11, no. 4 (2019): 49–60. http://dx.doi.org/10.13111/2066-8201.2019.11.4.5.
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Speed Up Robust Feature Algorithm (SURF) has been a very useful technique in the advancement of image feature algorithm. The strategy offers an extremely decent agreement between the runtime and accuracy, especially at object borders and fine structures. It has a wide scope of applications in remote sensing like getting computerized surface models from UAV and satellite images. In this paper, SURF algorithm has been discussed in details to enhance the capability of the system for image feature extraction technique to detect and obtain the maximum feature points from aerial imagery. The algorithms are developed depending upon such phenomena in which a maximum result can be obtained in very less time.
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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 object detection is given in this abstract.
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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, 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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Shahad, Jaafar Shahbaz, Abid Dawood Al-Zuky Ali, and Ezzat Muhy Al-Dean Al-Obaidi Fatin. "Evaluation of object detectors in recognizing crossroad intersection triangle sign." Evaluation of object detectors in recognizing crossroad intersection triangle sign 29, no. 2 (2023): 890–98. https://doi.org/10.11591/ijeecs.v29.i2.pp890-898.
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Variations in perspective, illumination, occlusion, motion blur, and weatherworn degeneration of signs could all be crucial in identifying road signs. The goal of this work is to evaluate the technique's performance for image processing in detecting and recognizing triangle sign, as well as determine the optimum threshold value range for doing so. Cascade object detector and speed up robust features (SURF) are tested here to detect and recognize the triangle sign through Palestine and Al-Rubaie streets during daytime in Baghdad city. Results showed the effectiveness of cascade object detector over SURF in detecting triangle sign with precision lies in the range (0.98-0.9) and (0.54-0.46) for cascade and SURF techniques respectively. At Final, the highest precision was recorded at fifteen and twenty-five threshold values for cascade and SURF approaches respectively.
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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 are picked around feature description regions. After that, we perform one more geometric transformation which is rotation, and is used to improve the visual appearance of an 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 the image, the desired object is detected in a scene.
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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 (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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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 video frame sequences is challenging. This system proposes statistical gait features on Speeded Up Robust Features SURF to represent the biometric gait feature for human identification. This system chooses the most suitable gait features to diminish the effects of "covariate factors" so human identification accuracy is effectiveness. Support Vector Machine SVM classifier evaluated the discriminatory ability of gait pattern classification on CASIA B Multi view Gait Dataset . Khaing Zarchi Htun | Sai Maung Maung Zaw "Gait Recognition for Person Identification using Statistics of SURF" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-3 | Issue-5 , August 2019, URL: https://www.ijtsrd.com/papers/ijtsrd26609.pdf
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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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Dong, Ao Shuang, Ben Qiang Yang, Dan Yang Zhao, et al. "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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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 HOG feature is improved by introducing direction controllable filter to enhance the local edge information. At the same time, the K-means clustering is performed on SURF to obtain the word bag model. Finally, the fused motion features are input to support vector machine (SVM) to classify and recognize the motion features. We make comparison with the state-of-the-art methods on KTH, UCF Sports and SBU Kinect Interaction data sets. The results show that the recognition accuracy of the proposed algorithm is greatly improved.</p> <p>&nbsp;</p>
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Chen, Wan Mi, and Sheng Guo. "Person Following of a Mobile Robot Using Kinect through Features Detection Based on SURF." Advanced Materials Research 542-543 (June 2012): 779–84. http://dx.doi.org/10.4028/www.scientific.net/amr.542-543.779.
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Following a person is an important task for domestic service robots in applications in which human-robot interaction is a primary requirement. Two steps will be completed if the robot needs to achieves this task. It includes detecting the target person and following it. Thus, the robot needs applicable algorithm and specific sensor. In this paper features detection and following technology based on SURF (Speed Up Robust Features) algorithm is used for person following in domestic environments. The vision system of robot obtains very good features of the target person through the RGB camera of kinect (Kinect sensor device) using SURF algorithm. And the depth camera of kinect helps the robot obtain the accurate information about the position of the target person in the environment. It uses SURF algorithm to extract the features of the target person, and match them in following frames. The proposed method is programmed in high speed hardware system and using small zone person following method in order to meet the real time requirement. Experimental results are provided to demonstrate the effectiveness of the proposed approach.
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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 experimental results show that compared with the SURF algorithm, SIFT (Scale Invariant Feature Transform) algorithm and ORB (Oriented FAST and Rotated BRIEF) algorithm, the proposed algorithm is superior to other algorithms in terms of matching accuracy and matching speed, and the robustness is higher.
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Ihsan, Ahmad, Liza Fitria, Mursyidah Mursyidah, Herri Mahyar, Suryati Suryati, and Misriana Misriana. "Implementation of Augmented Reality at Interactive Food Menu Using the Speed Up Robust Features (SURF) Algorithm." Jurnal Infomedia 8, no. 1 (2023): 1. http://dx.doi.org/10.30811/jim.v8i1.4074.
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— Promotion is an attempt to notify or offer a product or service with the aim of attracting potential customers to buy or consume it, with the promotion, producers or distributors expect an increase in sales figures. In this study researchers used Augmented Reality technology for interactive media promotion of food menus by adding 3D multimedia elements. The method used in this study uses the Natural Feature Tracking method with the Speed Up Robust Features (SURF) algorithm, which detects local features in marker images that are resistant to rotation, scale and blurring. The results showed that the keypoint functions to render 3D objects. Search for keypoints is interrupted due to distance, light intensity and slope of the marker. Test results to see the distance between the camera and the marker as far as 60 cm. Medium light intensity that can detect markers, the average time of object speed can be displayed is 3.026 seconds and the marker slope limit is 30 °. This is because the keypoint readings at the position and time limit from keypoint readings clearly, keypoint readings clearly produce 3D objects can be displayed. This research uses the Android platform as the foundation of this Augmented Reality technology application. So that by displaying 3D food menu items in restaurants it is expected to be a means of promotion to attract consumers.
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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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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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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 results show that the shift error can be reduced by more than two times, and the parallel algorithm can achieve nearly ten times the acceleration ratio.
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Shahad J. Shahbaz, Ali A. D. Al-Zuky, and Fatin E. M. Al-Obaidi1. "Detection of Traffic Signs using feature based on of Speed Up Robust method." Journal of the College of Basic Education 29, no. 119 (2023): 9–1. http://dx.doi.org/10.35950/cbej.v29i119.10602.
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Variations in perspective, illumination, occlusion, motion blur, and weatherworn degeneration of signs could all be crucial in identifying road signs. The goal of this project is to evaluate the image processing technique's performance in detecting and recognizing road signs, as well as determine the optimum threshold value range for doing so. The Speed Up Robust Features (SURF) detector was tested in the current project to detect and recognize road signs through Bagdad’s streets under various speeds and threshold values. The importance of the threshold’s value was highlighted here to occupy an accurate detection and hence recognize road sign at final. The optimum threshold value for best detection resulted usually in the range (20-25) for all speed signs. The latter recorded its highest precision value at five threshold value while the highest precision value (i.e. 0.5) resulted for speed sign 40 followed by 60 and 80-speed signs
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Jaya, Kumari, Patidar Kailash, Saxena Gourav, and Kushwaha Rishi. "A Hybrid Enhanced Real-Time Face Recognition Model using Machine Learning Method with Dimension Reduction." Indian Journal of Artificial Intelligence and Neural Networking (IJAINN) 1, no. 3 (2021): 12–16. https://doi.org/10.54105/ijainn.B1027.061321.
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Face recognition techniques play a crucial role in numerous disciplines of data security, verification, and authentication. The face recognition algorithm selects a face attribute from an image datasets. Recognize identification is an authentication device for verification as well as validation having both data analysis and feasible significance. The facerecognizing centered authentication framework can further be considered an AI technology implementation for instantly identifying a particular image. In this research, we are presenting a hybrid face recognition model (HFRM) using machine learning methods with “Speed Up Robust Features” (SURF), “scale-invariant feature transform” (SIFT), Locality Preserving Projections (LPP) &Principal component analysis (PCA) method. In the proposed HFRM model SURF method mainly detects the local feature efficiently. SIFT method mainly utilizes to detect the local features and recognize them. LPP retains the local framework of facial feature area which is generally quite meaningful than on the sequence kept by a 'principal component analysis (PCA) as well as “linear discriminate analysis” (LDA). The proposed HFRM method is compared with the existing (H. Zaaraoui et al., 2020) method and the experimental result clearly shows the outstanding performance in terms of detection rate and accuracy % over existing methods.
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Wang, Yin-Tien, Chen-Tung Chi, and Ying-Chieh Feng. "Robot mapping using local invariant feature detectors." Engineering Computations 31, no. 2 (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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Abdul Hassan, Alia Karim, Bashar Saadoon Mahdi, and Asmaa Abdullah Mohammed. "Writer Identification Based on Arabic Handwriting Recognition by using Speed Up Robust Feature and K- Nearest Neighbor Classification." JOURNAL OF UNIVERSITY OF BABYLON for Pure and Applied Sciences 27, no. 1 (2019): 1–10. http://dx.doi.org/10.29196/jubpas.v27i1.2060.
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In a writer recognition system, the system performs a “one-to-many” search in a large database with handwriting samples of known authors and returns a possible candidate list. This paper proposes method for writer identification handwritten Arabic word without segmentation to sub letters based on feature extraction speed up robust feature transform (SURF) and K nearest neighbor classification (KNN) to enhance the writer's identification accuracy. After feature extraction, it can be cluster by K-means algorithm to standardize the number of features. The feature extraction and feature clustering called to gather Bag of Word (BOW); it converts arbitrary number of image feature to uniform length feature vector. The proposed method experimented using (IFN/ENIT) database. The recognition rate of experiment result is (96.666).
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Gunawan, Chicha Rizka, Nurdin Nurdin, and Fajriana Fajriana. "Pengenalan Pakaian Adat Aceh Berbasis Augmented Reality Menggunakan Metode Speed Up Robust Featured (SURF)." Jurnal Komtika (Komputasi dan Informatika) 7, no. 2 (2023): 106–13. http://dx.doi.org/10.31603/komtika.v7i2.9124.
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Aceh Province, especially Langsa City, has a tourist attraction, namely the Langsa City Forest Park House (RTH). One of the most interesting rides in Langsa City Forest Park is Rumoh Aceh. Based on the results of visits and interviews by Rumoh Aceh officers, the large number of visitors from outside Aceh with one officer sometimes made it difficult for the officers to explain the information available about Rumoh Aceh, especially Acehnese traditional clothes, which were only displayed from a printed image, and provided no other information about these traditional clothes, so that many visitors did not know the diversity of designs and motifs of traditional clothes in Aceh. So, a medium was formed that could display Acehnese traditional clothing. The media uses augmented reality technology so that users can add virtual objects to the real environment to make it easier to use. This application uses the Speed Up Robust Featured (SURF) algorithm, which can process marker tracking quickly so that it can obtain better tracking speed times. The shortest distance from the marker to the camera that can show 3D objects is 20 cm, whereas the farthest distance that cannot show 3D objects is 100 cm. The best distance at which a marker can be detected is 20–80 cm. The best average detection time is 0.00049 s, and the average speed obtained is 1261.22 m/s at a distance of 60 cm. The Speed Up Robust Featured (SURF) algorithm can be used in the Augmented Reality-based Aceh Traditional Clothing Recognition application.
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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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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 while SURF and SIFT produce more accurate and robust results than ORB, they require longer processing time. The study evaluates the approach on a real-world dataset and demonstrates its effectiveness in creating seamless and visually appealing panoramas. This study provides valuable insights into the trade-offs between different feature extraction algorithms and presents a practical solution for panorama stitching applications.
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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 performance of the improved SURF algorithm. The experimental results show that the detection accuracy of the new system for surface defects of printed fabrics reaches 98%. The algorithm has higher detection rate and faster detection speed, which can meet the needs of practical industrial applications.
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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 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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S., Indhumathi, and Christopher Clement J. "Convex-based lightweight feature descriptor for Augmented Reality Tracking." PLOS ONE 19, no. 7 (2024): e0305199. http://dx.doi.org/10.1371/journal.pone.0305199.
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Feature description is a critical task in Augmented Reality Tracking. This article introduces a Convex Based Feature Descriptor (CBFD) system designed to withstand rotation, lighting, and blur variations while remaining computationally efficient. We have developed two filters capable of computing pixel intensity variations, followed by the covariance matrix of the polynomial to describe the features. The superiority of CBFD is validated through precision, recall, computation time, and feature location distance. Additionally, we provide a solution to determine the optimal block size for describing nonlinear regions, thereby enhancing resolution. The results demonstrate that CBFD achieves a average precision of 0.97 for the test image, outperforming Superpoint, Directional Intensified Tertiary Filtering (DITF), Binary Robust Independent Elementary Features (BRIEF), Binary Robust Invariant Scalable Keypoints (BRISK), Speeded Up Robust Features (SURF), and Scale Invariant Feature Transform (SIFT), which achieve scores of 0.95, 0.92, 0.72, 0.66, 0.63 and 0.50 respectively. Noteworthy is CBFD’s recall value of 0.87 representing at the maximum of a 13.6% improvement over Superpoint, DITF, BRIEF, BRISK, SURF, and SIFT. Furthermore, the matching score for the test image is 0.975. The computation time for CBFD is 2.8 ms, which is at least 6.7% lower than that of other algorithms. Finally, the plot of location feature distance illustrates that CBFD exhibits minimal distance compared to DITF and Histogram of Oriented Gradients (HOG). These results highlight the speed and robustness of CBFD across various transformations.
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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 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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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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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 detector to locate interest points and haar wavelet for descriptions. Despite the fast computation of SURF extraction, the detector seems ineffective as it obviously detects quite a small volume of interest points on the food objects with monotonous appearance. It occurs due to 1) food has texture-less surface 2) image has small pixel dimensions, and 3) image has low contrast and brightness. As a result, the characteristics of these images that were captured are clueless and lead to low classification performance. This problem has been manifested through low production of interest points. In this paper, we propose a technique to detect denser interest points on monotonous food by increasing the density of blobs in fast hessian detector in SURF. We measured the effect of this technique by performing a comparison on SURF interest points detection by using different density of blobs detection. SURF is encoded by using Bag of Features (BoF) model and Support Vector Machine (SVM) with linear kernel adopted for classification. The findings has shown the density of interest point detection has prominent effect on the interest points detection and classification performance on the respective food categories with 86% classification accuracy on UEC100-Food dataset. Â
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Ajayi, O. G. "PERFORMANCE ANALYSIS OF SELECTED FEATURE DESCRIPTORS USED FOR AUTOMATIC IMAGE REGISTRATION." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLIII-B3-2020 (August 21, 2020): 559–66. http://dx.doi.org/10.5194/isprs-archives-xliii-b3-2020-559-2020.
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Abstract. Automatic detection and extraction of corresponding features is very crucial in the development of an automatic image registration algorithm. Different feature descriptors have been developed and implemented in image registration and other disciplines. These descriptors affect the speed of feature extraction and the measure of extracted conjugate features, which affects the processing speed and overall accuracy of the registration scheme. This article is aimed at reviewing the performance of most-widely implemented feature descriptors in an automatic image registration scheme. Ten (10) descriptors were selected and analysed under seven (7) conditions viz: Invariance to rotation, scale and zoom, their robustness, repeatability, localization and efficiency using UAV acquired images. The analysis shows that though four (4) descriptors performed better than the other Six (6), no single feature descriptor can be affirmed to be the best, as different descriptors perform differently under different conditions. The Modified Harris and Stephen Corner Detector (MHCD) proved to be invariant to scale and zoom while it is excellent in robustness, repeatability, localization and efficiency, but it is variant to rotation. Also, the Scale Invariant feature Transform (SIFT), Speeded Up Robust Features (SURF) and the Maximally Stable Extremal Region (MSER) algorithms proved to be invariant to scale, zoom and rotation, and very good in terms of repeatability, localization and efficiency, though MSER proved to be not as robust as SIFT and SURF. The implication of the findings of this research is that the choice of feature descriptors must be informed by the imaging conditions of the image registration analysts.
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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), 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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Shahbaz, Shahad Jaafar, Ali Abid Dawood Al-Zuky, and Fatin Ezzat Muhy Al-Dean Al-Obaidi. "Evaluation of object detectors in recognizing crossroad intersection triangle sign." Indonesian Journal of Electrical Engineering and Computer Science 29, no. 2 (2023): 890. http://dx.doi.org/10.11591/ijeecs.v29.i2.pp890-898.
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<p>Variations in perspective, illumination, occlusion, motion blur, and weatherworn degeneration of signs could all be crucial in identifying road signs. The goal of this work is to evaluate cascade object detector and Speed Up Robust Features in detecting, recognizing crossroad intersection triangle sign, and determining the optimum threshold value. The current work is executed in Baghdad's streets during the daytime. Results showed the effectiveness of cascade detector in detecting the triangle sign than SURF with precision lies in the range (0.9-0.98). Finally, the highest precision was recorded at fifteen and twenty-five threshold values for cascade and SURF approaches respectively.</p>
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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 it with other popular feature descriptors. Through this review, the aim is to provide a clear understanding of the SURF feature descriptor and its significance in the area of computer vision.
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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 it with other popular feature descriptors. Through this review, the aim is to provide a clear understanding of the SURF feature descriptor and its significance in the area of computer vision.
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Vaishnavi, D., D. Mahalakshmi, and Venkata Siva Rao Alapati. "Visual Feature Based Image Forgery Detection." International Journal of Engineering & Technology 7, no. 4.6 (2018): 86. http://dx.doi.org/10.14419/ijet.v7i4.6.20436.
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In present days, the images are building up in digital form and which may hold essential information. Such images can be voluntarily forged or manipulated using the image processing tools to abuse it. It is very complicated to notice the forgery by naked eyes. In particular, the copy move forgery is enormously demanding one to expose. Hence, this paper put forwards a method to determine the copy move forgery by extracting the visual feature called speed up robust features (SURF). In the direction to quantitatively analyze the performance, the metrics namely false positive rate and true positive rate are estimated and also comparative study is carried out by previous existing methods.
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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 (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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Tang, Bo, and Li Jiang. "Binocular stereovision omnidirectional motion handling robot." International Journal of Advanced Robotic Systems 17, no. 3 (2020): 172988142092685. http://dx.doi.org/10.1177/1729881420926852.
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Binocular stereovision has become one of the development trends of machine vision and has been widely used in robot recognition and positioning. However, the current research on omnidirectional motion handling robots at home and abroad is too limited, and many problems cannot be solved well, such as single operating systems, complex algorithms, and low recognition rates. To make a high-efficiency handling robot with high recognition rate, this article studies the problem of robot image feature extraction and matching and proposes an improved speeded up robust features (SURF) algorithm that combines the advantages of both SURF and Binary Robust Independent Elementary Features. The algorithm greatly simplifies the complexity of the algorithm. Experiments show that the improved algorithm greatly improves the speed of matching and ensures the real-time and robustness of the algorithm. In this article, the problem of positioning the target workpiece of the robot is studied. The three-dimensional (3-D) reconstruction of the target workpiece position is performed to obtain the 3-D coordinates of the target workpiece position, thereby completing the positioning work. This article designs a software framework for real-time 3-D object reconstruction. A Bayesian-based matching algorithm combined with Delaunay triangulation is used to obtain the relationship between supported and nonsupported points, and 3-D reconstruction of target objects from sparse to dense matches is achieved.
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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 practice.
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Lee, Hee-Jae, and Sang-Goog Lee. "Improvement Method of Tracking Speed for Color Object using Kalman Filter and SURF." Journal of Korea Multimedia Society 15, no. 3 (2012): 336–44. http://dx.doi.org/10.9717/kmms.2012.15.3.336.
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Li, Jun Fei, Geng Wang, and Qiang Li. "Improved SURF Detection Combined with Dual FLANN Matching and Clustering Analysis." Applied Mechanics and Materials 556-562 (May 2014): 2792–96. http://dx.doi.org/10.4028/www.scientific.net/amm.556-562.2792.
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In this paper, an improved object detection method based on SURF (Speed-Up Robust Feature) is presented. SURF is a widely used method in computer vision. But it’s still not efficient enough to apply in real-time applications, such as real time object tracking. To reduce the time cost, the traditional descriptor of SURF is altered. Triangle and diagonal descriptor is adopted to replace the Haar wavelet calculation. Then dual matching approach based on FLANN is employed. Thus matching errors can be cut down. Besides, the traditional SURF does not give the accurate region of the target. To restrict the area, clustering analysis is used which is promoted from K-WMeans. Experimental work demonstrates the proposed approach achieve better effect than traditional SURF in real scenarios.