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1
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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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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Zhang, Bao Feng, Ying Kui Jiao, Zhi Jun Ma, Yong Chen Li, and Jun Chao Zhu. "A Method of Features Extraction Based on Fisheye Image." Applied Mechanics and Materials 668-669 (October 2014): 1029–32. http://dx.doi.org/10.4028/www.scientific.net/amm.668-669.1029.
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In this paper, feature extraction algorithm based on spherical perspective projection model for the matching fisheye image is proposed. The fisheye image is mapped to the image plane through spherical mapping. Then the diffusion equation is formed by convolution of the image projection and spherical Gaussian function. The feature points of image are extracted based on the SIFT at the scale of spherical correlation function. Compared with SURF(Speeded Up Robust Features), more feature points in a shorter time are obtained.
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PL., Chithra, and Janes Pushparani S. "SURF Points Versus SIFT Points in Identification of Medicinal Plants." International Journal of Engineering and Advanced Technology (IJEAT) 9, no. 2 (2019): 602–7. https://doi.org/10.35940/ijeat.A9466.129219.
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Today, digital image processing is used in diverse fields; this paper attempts to compare the outcome of two commonly used techniques namely Speeded Up Robust Feature (SURF) points and Scale Invariant Feature Transform (SIFT) points in image processing operations. This study focuses on leaf veins for identification of plants. An algorithm sequence has been utilized for the purpose of recognition of leaves. SURF and SIFT extractions are applied to define and distinguish the limited structures of the documented vein image of the leaf separately and Support Vector Machine (SVM) is integrated to classify and identify the correct plant. The results prove that the SURF algorithm is the fastest and an efficient one. The results of the study can be extrapolated to authenticate medicinal plants which is the starting step to standardize herbs and carryout research.
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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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MISS., NAMRATA N. RADE. "IMAGE TEXTURE CLASSIFICATION: SURF WITH SVM." IJIERT - International Journal of Innovations in Engineering Research and Technology 4, no. 7 (2017): 43–47. https://doi.org/10.5281/zenodo.1459102.
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<strong>Nowadays,various approaches of texture classification have been developed which works on acquired image features and separate them into different classes by using a specific classifier . This paper gives a state - of - the - art texture classification technique called Speeded up Robust Features (SURF) with SVM (Support Vector Machine) classifier. In this concept,image data representation is accomplished by capturing feature s in the form of key - points. SURF uses determinant of Hessian matrix to achieve point of interests on which description and classification is carried out. This method gives superior performance over already established methods in terms of processing time,accuracy and robustness . In this paper,we have taken UMD dataset for processing and calculated different performance parameters which gives excellent results.</strong> <strong>https://www.ijiert.org/paper-details?paper_id=141068</strong>
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Xu, Bin, Xiang Na Li, and Wei Ning Xue. "Study on a Rapid Real-Time Feature Extraction Algorithm." Advanced Materials Research 709 (June 2013): 575–78. http://dx.doi.org/10.4028/www.scientific.net/amr.709.575.
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A fast feature extraction algorithm is presented in this paper based on the color and point feature, With the aim of required feature points, the location of the object includes object region detection and feature point location. The area of object detection is used to look for the centre of gravity point from the scaling image. The feature point location is based on the object region detection, cuts a picture from the image, and extracts speeded up robust feature (SURF) points of the target within the cut picture. The target position is calculated according to the value of the feature points, it provides a basis for object tracking. The experimental results verify the effectiveness of the proposed method.
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Wang, Lu, and Xiao Ning Fu. "FPGA-Based Image Processing System for Target Locating." Applied Mechanics and Materials 226-228 (November 2012): 1878–81. http://dx.doi.org/10.4028/www.scientific.net/amm.226-228.1878.
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In this paper, we proposed the design of an FPGA-based image processing system for target locating. The locating mechanism is based on the feature line segments of target’s image. The system processes the target’s image sequence, finds and matches feature points on each image, and uses the feature points to calculate the length of feature line segments for target locating. We implemented the Speeded Up Robust Features (SURF) algorithm on FPGA hardware to extract feature points. The system has a core CPU for control and part of the mathematical computation. Custom-designed logic circuit modules are used to accelerate the feature point extraction. The system’s software is designed to work with parallel and pipeline operation. The performance test shows that the system is capable of real-time processing.
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Tayyab, Muhammad, Sulaiman Abdullah Alateyah, Mohammed Alnusayri, et al. "A Hybrid Approach for Sports Activity Recognition Using Key Body Descriptors and Hybrid Deep Learning Classifier." Sensors 25, no. 2 (2025): 441. https://doi.org/10.3390/s25020441.
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This paper presents an approach for event recognition in sequential images using human body part features and their surrounding context. Key body points were approximated to track and monitor their presence in complex scenarios. Various feature descriptors, including MSER (Maximally Stable Extremal Regions), SURF (Speeded-Up Robust Features), distance transform, and DOF (Degrees of Freedom), were applied to skeleton points, while BRIEF (Binary Robust Independent Elementary Features), HOG (Histogram of Oriented Gradients), FAST (Features from Accelerated Segment Test), and Optical Flow were used on silhouettes or full-body points to capture both geometric and motion-based features. Feature fusion was employed to enhance the discriminative power of the extracted data and the physical parameters calculated by different feature extraction techniques. The system utilized a hybrid CNN (Convolutional Neural Network) + RNN (Recurrent Neural Network) classifier for event recognition, with Grey Wolf Optimization (GWO) for feature selection. Experimental results showed significant accuracy, achieving 98.5% on the UCF-101 dataset and 99.2% on the YouTube dataset. Compared to state-of-the-art methods, our approach achieved better performance in event recognition.
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Chunxian, Gao, Zeng Zhe, and Liu Hui. "Hybrid Video Stabilization for Mobile Vehicle Detection on SURF in Aerial Surveillance." Discrete Dynamics in Nature and Society 2015 (2015): 1–12. http://dx.doi.org/10.1155/2015/357191.
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Detection of moving vehicles in aerial video sequences is of great importance with many promising applications in surveillance, intelligence transportation, or public service applications such as emergency evacuation and policy security. However, vehicle detection is a challenging task due to global camera motion, low resolution of vehicles, and low contrast between vehicles and background. In this paper, we present a hybrid method to efficiently detect moving vehicle in aerial videos. Firstly, local feature extraction and matching were performed to estimate the global motion. It was demonstrated that the Speeded Up Robust Feature (SURF) key points were more suitable for the stabilization task. Then, a list of dynamic pixels was obtained and grouped for different moving vehicles by comparing the different optical flow normal. To enhance the precision of detection, some preprocessing methods were applied to the surveillance system, such as road extraction and other features. A quantitative evaluation on real video sequences indicated that the proposed method improved the detection performance significantly.
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Hegde, Chitra, Shakti Singh Chundawat, and Divya S N. "Unusual Event Detection using Mean Feature Point Matching Algorithm." International Journal of Electrical and Computer Engineering (IJECE) 6, no. 4 (2016): 1595. http://dx.doi.org/10.11591/ijece.v6i4.10179.
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Analysis and detection of unusual events in public and private surveillance system is a complex task. Detecting unusual events in surveillance video requires the appropriate definition of similarity between events. The key goal of the proposed system is to detect behaviours or actions that can be considered as anomalies. Since suspicious events differ from domain to domain, it remains a challenge to detect those events in major domains such as airport, super malls, educational institutions etc. The proposed Mean Feature Point Matching (MFPM) algorithm is used for detecting unusual events. The Speeded-Up Robust Features (SURF) method is used for feature extraction. The MFPM algorithm compares the feature points of the input image with the mean feature points of trained dataset. The experimental result shows that the proposed system is efficient and accurate for wide variety of surveillance videos.
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Hegde, Chitra, Shakti Singh Chundawat, and Divya S N. "Unusual Event Detection using Mean Feature Point Matching Algorithm." International Journal of Electrical and Computer Engineering (IJECE) 6, no. 4 (2016): 1595. http://dx.doi.org/10.11591/ijece.v6i4.pp1595-1601.
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Analysis and detection of unusual events in public and private surveillance system is a complex task. Detecting unusual events in surveillance video requires the appropriate definition of similarity between events. The key goal of the proposed system is to detect behaviours or actions that can be considered as anomalies. Since suspicious events differ from domain to domain, it remains a challenge to detect those events in major domains such as airport, super malls, educational institutions etc. The proposed Mean Feature Point Matching (MFPM) algorithm is used for detecting unusual events. The Speeded-Up Robust Features (SURF) method is used for feature extraction. The MFPM algorithm compares the feature points of the input image with the mean feature points of trained dataset. The experimental result shows that the proposed system is efficient and accurate for wide variety of surveillance videos.
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Ahmed, Chater, Benradi Hicham, and Lasfar Abdelali. "Method of optimization of the fundamental matrix by technique speeded up robust features application of different stress images." International Journal of Electrical and Computer Engineering (IJECE) 12, no. 2 (2022): 1429–36. https://doi.org/10.11591/ijece.v12i2.pp1429-1436.
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The purpose of determining the fundamental matrix (F) is to define the epipolar geometry and to relate two 2D images of the same scene or video series to find the 3D scenes. The problem we address in this work is the estimation of the localization error and the processing time. We start by comparing the following feature extraction techniques: Harris, features from accelerated segment test (FAST), scale invariant feature transform (SIFT) and speed-up robust features (SURF) with respect to the number of detected points and correct matches by different changes in images. Then, we merged the best chosen by the objective function, which groups the descriptors by different regions in order to calculate F. Then, we applied the standardized eight-point algorithm which also automatically eliminates the outliers to find the optimal solution F. The test of our optimization approach is applied on the real images with different scene variations. Our simulation results provided good results in terms of accuracy and the computation time of F does not exceed 900 ms, as well as the projection error of maximum 1 pixel, regardless of the modification.
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Chater, Ahmed, Hicham Benradi, and Abdelali Lasfar. "Method of optimization of the fundamental matrix by technique speeded up robust features application of different stress images." International Journal of Electrical and Computer Engineering (IJECE) 12, no. 2 (2022): 1429. http://dx.doi.org/10.11591/ijece.v12i2.pp1429-1436.
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<span>The purpose of determining the fundamental matrix (F) is to define the epipolar geometry and to relate two 2D images of the same scene or video series to find the 3D scenes. The problem we address in this work is the estimation of the localization error and the processing time. We start by comparing the following feature extraction techniques: Harris, features from accelerated segment test (FAST), scale invariant feature transform (SIFT) and speed-up robust features (SURF) with respect to the number of detected points and correct matches by different changes in images. Then, we merged the best chosen by the objective function, which groups the descriptors by different regions in order to calculate ‘F’. Then, we applied the standardized eight-point algorithm which also automatically eliminates the outliers to find the optimal solution ‘F’. The test of our optimization approach is applied on the real images with different scene variations. Our simulation results provided good results in terms of accuracy and the computation time of ‘F’ does not exceed 900 ms, as well as the projection error of maximum 1 pixel, regardless of the modification.</span>
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Zhang, Lishuo, Zhuxing Ma, Hao Gu, Zizhong Xin, and Pengcheng Han. "Condition Monitoring and Analysis Method of Smart Substation Equipment Based on Deep Learning in Power Internet of Things." International Journal of Information Technologies and Systems Approach 16, no. 3 (2023): 1–16. http://dx.doi.org/10.4018/ijitsa.324519.
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An accurate perception of the state of smart substation equipment is a strong guarantee for the reliable operation of the large power grid. This article proposes using deep learning for the device condition monitoring and analysis method in a power internet of things cloud edge collaboration mode. The speeded up robust features (SURF) feature detector is used at the edge of the network to accurately collect the interest points from the image data set, providing a reliable and complete sample data set support for the cloud-based deep learning network. Adding the attention mechanism module to the cloud improves the Yolov5 network model, enhance feature extraction, and increase the monitoring and analysis capabilities of the equipment. The simulation results show that the proposed method has achieved a recall rate of 91.21% and an accuracy rate of 90.54% for insulator fault evaluation indicators.
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Nayak, Jithendra P. R., and Parameshachari B. D. "Defect Detection in Printed Circuit Boards Using Leaky-LeNet 5." International Journal of Software Innovation 10, no. 1 (2022): 1–13. http://dx.doi.org/10.4018/ijsi.309726.
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Each electronic device includes printed circuit boards (PCBs), where defect detection is an important process to enhance the quality of PCB production. To accomplish error-free PCBs, the researchers and experts converted traditional manual inspections into automated systems. The manual inspection results are ineffective, where the non-defective PCBs are classified as defective PCBs. A subsequent study added a technique called LeNetwork-5 (LeNet-5) and speeded up robust feature extraction (SURF) techniques to identify defects. The existing method was unreliable, so further research was conducted in this area using Leaky techniques. Two objectives are achieved using SURF technique: (1) registration of the PCB to be checked against a reference PCB and (2) identification of feature points on the PCB helps to find missing components. Additionally, the Leaky LeNet-5 classifier is employed to classify the defects in PCBs. The proposed method achieved accuracy of 99.83%, sensitivity of 96.26%, specificity of 99.15%, and F1-Score of 98.45%.
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Yang, Shuangzhan, Yu Han, Lei Li, et al. "Geometric Parameter Self-Calibration Based on Projection Feature Matching for X-Ray Nanotomography." Applied Sciences 12, no. 22 (2022): 11675. http://dx.doi.org/10.3390/app122211675.
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The mismatch of geometric parameters in a nanotomography system bears a significant impact on the reconstructed images. Moreover, projection image noise is increased due to limitations of the X-ray power source. The accuracy of the existing self-calibration method, which uses only the grayscale information of the projected image, is easily affected by noise and leads to reduced accuracy. This paper proposes a geometric parameter self-calibration method based on feature matching of mirror projection images. Firstly, the fast extraction and matching feature points in the mirror projection image are performed by speeded-up robust features (SURF). The feature triangle is then designed according to the stable position of the system’s rotation axis to further filter the feature points. In turn, the influence of the mismatched points on the calculation accuracy is reduced. Finally, the straight line where the rotation axis is located is fitted by the midpoint coordinates of the filtered feature points, thereby realizing geometric parameter calibration of the system. Simulation and actual data from the experimental results show that the proposed method effectively realizes the calibration of geometric parameters, and the blurring and ghosting caused by geometric artifacts are corrected. Compared with existing methods, the image clarity can be improved by up to 14.4%.
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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 (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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Liu, Qiong, JiZhuang Hui, Li Luo, and YanPu Yang. "Target Identification and Location Algorithm Based on SURF-BRISK Operator." International Journal of Pattern Recognition and Artificial Intelligence 30, no. 06 (2016): 1655016. http://dx.doi.org/10.1142/s0218001416550168.
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Accurate and fast target image recognition is an important function of applications such as remote sensing imaging and medical imaging. However, an operator such as speeded up robust feature (SURF) cannot be accurately matched in the recognition process of a target image. This led us to propose the use of a method capable of matching identification, i.e. binary robust invariant scalable keypoints (BRISK) operators, in combination with SURF operators. The proposed algorithm combines the accuracy of SURF operators and the rapidity of BRISK operators to obtain a quick and accurate way of matching. The initial matching of image feature extraction for targets is performed using the SURF-BRISK algorithm, and similarity measurements of feature matching are performed for the feature points of initial matching using the Hamming distance. Then, secondary fine matching is performed using the M-estimator Sample and Consensus (MSAC) algorithm to eliminate mismatched point pairs in order to achieve recognition of target images. Then, the three-dimensional coordinates of the work piece are obtained by using a binocular stereo vision system to provide location coordinates for the robots to grasp the work pieces accurately. In the experiment, stereo vision matching is conducted for targets obtained using the SURF-BRISK algorithm, and the location coordinates of targets are passed to the robot controller. The experimental results show that if the special geometric distortion is neglected, this method can be adapted for accurate positioning of the target; hence, it can identify the target in complex environments, access the location coordinates of the target, and achieve accurate robotic grasping of the work piece in real time.
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Retissin, Aditya, and Mohammed Jasim J S. "Performance Analysis of SIFT/SURF Algorithms in Neural Networks for Optimized Feature Detection." International Journal of Engineering and Advanced Technology 8, no. 4s2 (2020): 51–56. http://dx.doi.org/10.35940/ijeat.d1004.0484s219.
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This paper is an experiment on the implementation of scale-invariant feature transform (SIFT) and speeded up robust features (SURF) algorithms into multi-dimensional neural networks. We are attempting to perform a comparative performance evaluation by using different scale factors of the SIFT algorithm in multi-layered neural networks. This method will help us to understand the best way of implementing the above algorithms in neural networks and from a given sample, extracting distinctive invariant features and finding points of interests. Hence performing a large data set computation would be made much easier because of the neural network implementation. The conventional method of performing SIFT has computational limitations and we aim to achieve best possible way of performing the feature detection when using SIFT and neural network combined, hence transcending computational limitations that SIFT previously had. This approach to recognition of features can robustly find results much faster on bigger dataset and at the same time have the benefits of SIFT algorithm.
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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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Meskine, Fatiha, and Oussama Mezouar. "A Rigid Image Registration by Combined Local Features and Genetic Algorithms." Applied Computer Systems 28, no. 2 (2023): 252–57. http://dx.doi.org/10.2478/acss-2023-0025.
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Abstract Image registration is an essential pre-processing step required for many image processing applications such as medical imaging and computer vision. The aim is to geometrically align two or more images of the same scene by establishing a mapping that relies on each point from one image to its corresponding point of another image. Scale invariant feature transform (SIFT) and speeded up robust features (SURF) are well-liked local features descriptors that have been extensively utilised for feature-based image registration due to their inherent properties such as invariance, changes in illumination, and noise. Moreover, the task of registration can be viewed as an optimization problem that can be solved by applying genetic algorithms (GAs). This paper presents an efficient feature image registration method based on combined local features and GAs. Firstly, the procedure consists of extracting the local features from the images by combining SIFT and SURF algorithms and matching them to refine the feature set data. Therefore, an adaptive GA based on fitness sharing and elitism techniques is employed to find the optimal rigid transformation parameters that best align the feature points by minimizing a distance metric. The suggested method is applied for registering medical images and the obtained results are significant compared to other feature-based approaches with reasonable computation time.
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Wang, Han, Kai Zong, Dongfeng Gao, Xuerui Xu, and Yanwei Wang. "Research on Two-Dimensional Digital Map Modeling Method Based on UAV Aerial Images." Applied Sciences 15, no. 7 (2025): 3818. https://doi.org/10.3390/app15073818.
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Accurate acquisition of two-dimensional digital maps of disaster sites is crucial for rapid and effective emergency response. The construction of two-dimensional digital maps using unmanned aerial vehicle (UAV) aerial images is not affected by factors such as signal interference, terrain, or complex building structures, which are common issues with methods like single-soldier image transmission or satellite imagery. Therefore, this paper investigates a method for modeling two-dimensional digital maps based on UAV aerial images. The proposed Canny edge-enhanced Speeded-Up Robust Features (C-SURF) algorithm in this method is designed to enhance the number of feature extractions and the accuracy of image registration. Compared to the SIFT and SURF algorithms, the number of feature points increased by approximately 44%, and the registration accuracy improved by about 16%, laying a solid foundation for feature-based image stitching. Additionally, a novel image stitching method based on the novel energy function is introduced, effectively addressing issues such as color discrepancies, ghosting, and misalignment in the fused image sequences. Experimental results demonstrate that the signal-to-noise ratio (SNR) of the fused images based on the novel energy function can reach an average of 36 dB.
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Zhang, Jiali. "Research on the algorithm of image feature detection and matching." Applied and Computational Engineering 5, no. 1 (2023): 527–35. http://dx.doi.org/10.54254/2755-2721/5/20230636.
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In-depth research on feature detection technology affects people's modern life. Modern artificial intelligence can act as the eyes of human beings and efficiently filter out effective information from complex pictures. Corner detection has now evolved into a tool for efficient image scanning. People's increasingly stringent requirements for image processing continue to promote the birth of new technologies. Corner detection methods have been improved and perfected, and have experienced detectors such as Harris, FAST, Scalriant Feature Transform (SIFT), Speeded Up Robust Feature (SURF), Binary Robust Invariant Scalable Keypoints (BRISK), KAZE, AKAZE and Oriented FAST and Rotated BRIEF (ORB). In the face of many mainstream detectors, the main research purpose of this paper is to rely on Python and Computer vision to study the detection efficiency of various detectors in different environments. In this experiment, thirteen groups of pictures were selected, and after being flipped, complicated, and blurred respectively, they were detected by different detectors to obtain the results. Finally, by comparing the feature detection points, detection time and other factors, this study found that the ORB detector can be competent in most situations and is currently the fastest and stable feature point detection and extraction algorithm. On the other hand, the BRISK detector can handle highly blurred images more efficiently.
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Khan, Sajid, Dong-Ho Lee, Asif Khan, Ahmad Waqas, Abdul Rehman Gilal, and Zahid Hussain Khand. "A Digital Camera-Based Rotation-Invariant Fingerprint Verification Method." Scientific Programming 2020 (May 15, 2020): 1–10. http://dx.doi.org/10.1155/2020/9758049.
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Fingerprint registration and verification is an active area of research in the field of image processing. Usually, fingerprints are obtained from sensors; however, there is recent interest in using images of fingers obtained from digital cameras instead of scanners. An unaddressed issue in the processing of fingerprints extracted from digital images is the angle of the finger during image capture. To match a fingerprint with 100% accuracy, the angles of the matching features should be similar. This paper proposes a rotation and scale-invariant decision-making method for the intelligent registration and recognition of fingerprints. A digital image of a finger is taken as the input and compared with a reference image for derotation. Derotation is performed by applying binary segmentation on both images, followed by the application of speeded up robust feature (SURF) extraction and then feature matching. Potential inliers are extracted from matched features by applying the M-estimator. Matched inlier points are used to form a homography matrix, the difference in the rotation angles of the finger in both the input and reference images is calculated, and finally, derotation is performed. Input fingerprint features are extracted and compared or stored based on the decision support system required for the situation.
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Sanil, Gangothri, Krishna Prakasha K, Srikanth Prabhu, Vinod Nayak, and Aparna Jayakala. "Region-wise landmarks-based feature extraction employing SIFT, SURF, and ORB feature descriptors to recognize Monozygotic twins from 2D/3D Facial Images." F1000Research 14 (April 16, 2025): 444. https://doi.org/10.12688/f1000research.162911.1.
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Background In computer vision and image processing, face recognition is increasingly popular field of research that identifies similar faces in a picture and assigns a suitable label. It is one of the desired detection techniques employed in forensics for criminal identification. Methods This study explores face recognition system for monozygotic twins utilizing three widely recognized feature descriptor algorithms: Scale-Invariant Feature Transform (SIFT), Speeded-Up Robust Features (SURF), and Oriented Fast and Rotated BRIEF (ORB)—with region-specific facial landmarks. These landmarks were extracted from 468 points detected through the Mediapipe frame-work, which enables simultaneous recognition of multiple faces. Quantitative similarity metrics t served as inputs for four classification methods: Support Vector Machine (SVM), eXtreme Gradient Boost (XGBoost), Light Gradient Boost Machine (LGBM), and Nearest Centroid (NC). The effectiveness of these algorithms was tested and validated using challenging ND Twins and 3D TEC datasets, the most difficult data sets for 2D and 3D face recognition research at Notre Dame University. Results Testing with Notre Dame University’s challenging ND Twins and 3D TEC datasets re- vealed significant performance differences. Results demonstrated that 2D facial images achieved notably higher recognition accuracy than 3D images. The 2D images produced accuracy of 88% (SVM), 83% (LGBM), 83% (XGBoost), and 79% (NC). In contrast, the 3D TEC dataset yielded a lower accuracy r of 74%, 72%, 72%, and 70%, with the same classifiers. Conclusion The hybrid feature extraction approach proved most effective, with maximum accuracy rates reaching 88% for 2D facial images and 74% for 3D facial images. This work contributes significantly to forensic science by enhancing the reliability of facial recognition systems when confronted with indistinguishable facial characteristics of monozygotic twins.
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Sanil, Gangothri, Krishna Prakasha K, Srikanth Prabhu, Vinod Nayak, and Aparna Jayakala. "Region-wise landmarks-based feature extraction employing SIFT, SURF, and ORB feature descriptors to recognize Monozygotic twins from 2D/3D Facial Images." F1000Research 14 (June 20, 2025): 444. https://doi.org/10.12688/f1000research.162911.2.
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Background In computer vision and image processing, face recognition is increasingly popular field of research that identifies similar faces in a picture and assigns a suitable label. It is one of the desired detection techniques employed in forensics for criminal identification. Methods This study explores face recognition system for monozygotic twins utilizing three widely recognized feature descriptor algorithms: Scale-Invariant Feature Transform (SIFT), Speeded-Up Robust Features (SURF), and Oriented Fast and Rotated BRIEF (ORB)—with region-specific facial landmarks. These landmarks were extracted from 468 points detected through the MediaPipe framework, which enables simultaneous recognition of multiple faces. Quantitative similarity metrics t served as inputs for four classification methods: Support Vector Machine (SVM), eXtreme Gradient Boost (XGBoost), Light Gradient Boost Machine (LGBM), and Nearest Centroid (NC). The effectiveness of these algorithms was tested and validated using challenging ND Twins and 3D TEC datasets, the most difficult data sets for 2D and 3D face recognition research at Notre Dame University. Results Testing with Notre Dame University’s challenging ND Twins and 3D TEC datasets revealed significant performance differences. Results demonstrated that 2D facial images achieved notably higher recognition accuracy than 3D images. The 2D images produced accuracy of 88% (SVM), 83% (LGBM), 83% (XGBoost), and 79% (NC). In contrast, the 3D TEC dataset yielded a lower accuracy r of 74%, 72%, 72%, and 70%, with the same classifiers. Conclusion The hybrid feature extraction approach proved most effective, with maximum accuracy rates reaching 88% for 2D facial images and 74% for 3D facial images. This work contributes significantly to forensic science by enhancing the reliability of facial recognition systems when confronted with indistinguishable facial characteristics of monozygotic twins.
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Xu, Jie, Qifeng Lai, Dongyan Wei, Xinchun Ji, Ge Shen, and Hong Yuan. "The Ground-Penetrating Radar Image Matching Method Based on Central Dense Structure Context Features." Remote Sensing 16, no. 22 (2024): 4291. http://dx.doi.org/10.3390/rs16224291.
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Subsurface structural distribution can be detected using Ground-Penetrating Radar (GPR). The distribution can be considered as road fingerprints for vehicle positioning. Similar to the principle of visual image matching for localization, the position coordinates of the vehicle can be calculated by matching real-time GPR images with pre-constructed reference GPR images. However, GPR images, due to their low resolution, cannot extract well-defined geometric features such as corners and lines. Thus, traditional visual image processing algorithms perform inadequately when applied to GPR image matching. To address this issue, this paper innovatively proposes a GPR image matching and localization method based on a novel feature descriptor, termed as central dense structure context (CDSC) features. The algorithm utilizes the strip-like elements in GPR images to improve the accuracy of GPR image matching. First, a CDSC feature descriptor is designed. By applying threshold segmentation and extremum point extraction to the GPR image, stratified strip-like elements and pseudo-corner points are obtained. The pseudo-corner points are treated as the centers, and the surrounding strip-like elements are described in context to form the GPR feature descriptors. Then, based on the feature description method, feature descriptors for both the real-time image and the reference image are calculated separately. By searching for the nearest matching point pairs and removing erroneous pairs, GPR image matching and localization are achieved. The proposed algorithm was evaluated on datasets collected from urban roads and railway tracks, achieving localization errors of 0.06 m (RMSE) and 1.22 m (RMSE), respectively. Compared to the traditional Speeded Up Robust Features (SURF) visual image matching algorithm, localization errors were reduced by 86.6% and 95.7% in urban road and railway track scenarios, respectively.
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Wang, Sen, Xiaoming Sun, Pengfei Liu, Kaige Xu, Weifeng Zhang, and Chenxu Wu. "Research on Remote Sensing Image Matching with Special Texture Background." Symmetry 13, no. 8 (2021): 1380. http://dx.doi.org/10.3390/sym13081380.
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The purpose of image registration is to find the symmetry between the reference image and the image to be registered. In order to improve the registration effect of unmanned aerial vehicle (UAV) remote sensing imagery with a special texture background, this paper proposes an improved scale-invariant feature transform (SIFT) algorithm by combining image color and exposure information based on adaptive quantization strategy (AQCE-SIFT). By using the color and exposure information of the image, this method can enhance the contrast between the textures of the image with a special texture background, which allows easier feature extraction. The algorithm descriptor was constructed through an adaptive quantization strategy, so that remote sensing images with large geometric distortion or affine changes have a higher correct matching rate during registration. The experimental results showed that the AQCE-SIFT algorithm proposed in this paper was more reasonable in the distribution of the extracted feature points compared with the traditional SIFT algorithm. In the case of 0 degree, 30 degree, and 60 degree image geometric distortion, when the remote sensing image had a texture scarcity region, the number of matching points increased by 21.3%, 45.5%, and 28.6%, respectively and the correct matching rate increased by 0%, 6.0%, and 52.4%, respectively. When the remote sensing image had a large number of similar repetitive regions of texture, the number of matching points increased by 30.4%, 30.9%, and −11.1%, respectively and the correct matching rate increased by 1.2%, 0.8%, and 20.8% respectively. When processing remote sensing images with special texture backgrounds, the AQCE-SIFT algorithm also has more advantages than the existing common algorithms such as color SIFT (CSIFT), gradient location and orientation histogram (GLOH), and speeded-up robust features (SURF) in searching for the symmetry of features between images.
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Oh, Jaehong, and Youkyung Han. "A Double Epipolar Resampling Approach to Reliable Conjugate Point Extraction for Accurate Kompsat-3/3A Stereo Data Processing." Remote Sensing 12, no. 18 (2020): 2940. http://dx.doi.org/10.3390/rs12182940.
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Kompsat-3/3A provides along-track and across-track stereo data for accurate three-dimensional (3D) topographic mapping. Stereo data preprocessing involves conjugate point extraction and acquisition of ground control points (GCPs), rational polynomial coefficient (RPC) bias compensation, and epipolar image resampling. Applications where absolute positional accuracy is not a top priority do not require GCPs, but require precise conjugate points from stereo images for subsequent RPC bias compensation, i.e., relative orientation. Conjugate points are extracted between the original stereo data using image-matching methods by a proper outlier removal process. Inaccurate matching results and potential outliers produce geometric inconsistency in the stereo data. Hence, the reliability of conjugate point extraction must be improved. For this purpose, we proposed to apply the coarse epipolar resampling using raw RPCs before the conjugate point matching. We expect epipolar images with even inaccurate RPCs to show better stereo similarity than the original images, providing better conjugate point extraction. To this end, we carried out the quantitative analysis of the conjugate point extraction performance by comparing the proposed approach using the coarsely epipolar resampled images to the traditional approach using the original stereo images. We tested along-track Kompsat-3 stereo and across-track Kompsat-3A stereo data with four well-known image-matching methods: phase correlation (PC), mutual information (MI), speeded up robust features (SURF), and Harris detector combined with fast retina keypoint (FREAK) descriptor (i.e., Harris). These matching methods were applied to the original stereo images and coarsely resampled epipolar images, and the conjugate point extraction performance was investigated. Experimental results showed that the coarse epipolar image approach was very helpful for accurate conjugate point extraction, realizing highly accurate RPC refinement and sub-pixel y-parallax through fine epipolar image resampling, which was not achievable through the traditional approach. MI and PC provided the most stable results for both along-track and across-track test data with larger patch sizes of more than 400 pixels.
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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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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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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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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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Qin, Si Yuan, and Jian Guo Yan. "Vision-Based Aerial Refueling Docking Based on SURF Algorithm." Applied Mechanics and Materials 130-134 (October 2011): 3102–6. http://dx.doi.org/10.4028/www.scientific.net/amm.130-134.3102.
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For the Boom and Receptacle Air Refueling, in order to locate the spatial position of the refueling receptacle, an object locating method is developed based on Speeded-up Robust Feature (SURF) algorithm. Firstly, SURF feature vector matching algorithm is used to detect and collect suitable SURF feature points in left and right images produced by binocular stereo vision system separately. Then the point that shows the same spatial position in both left and right images can be located through process such as deleting wrong matching points and calculating the image coordinate of the target point. Finally, the three-dimensional coordinates of the target points could be rebuilt in the camera’s coordinate system. According to the results of experiments, this method has good robustness and practicability.
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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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Yang, Yu Han, and Yao Qin Xie. "Feature-Based GDLOH Deformable Registration for CT Lung Image." Applied Mechanics and Materials 333-335 (July 2013): 969–73. http://dx.doi.org/10.4028/www.scientific.net/amm.333-335.969.
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To improve the efficiency and accuracy of the conventional SIFT-TPS (Scale-invariant feature transform and Thin-Plate Spline) method in deformable registration for CT lung image, we develop a novel approach by using combining SURF(Speeded up Robust Features) and GDLOH(Gradient distance-location-orientation histogram) to detect matching feature points. First, we employ SURF as feature detection to find the stable feature points of the two CT images rapidly. Then GDLOH is taken as feature descriptor to describe each detected points characteristic, in order to supply measurement tool for matching process. In our experiment, five couples of clinical images are simulated using our algorithm above, result in an obvious improvement in run-time and registration quality, compared with the conventional methods. It is demonstrated that the proposed method may create a new window in performing a good robust and adaptively for deformable registration for CT lung tomography.
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Zhou, Mu, Xia Hong, Zengshan Tian, Huining Dong, Mingchun Wang, and Kunjie Xu. "Maximum Entropy Threshold Segmentation for Target Matching Using Speeded-Up Robust Features." Journal of Electrical and Computer Engineering 2014 (2014): 1–12. http://dx.doi.org/10.1155/2014/768519.
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This paper proposes a 2-dimensional (2D) maximum entropy threshold segmentation (2DMETS) based speeded-up robust features (SURF) approach for image target matching. First of all, based on the gray level of each pixel and the average gray level of its neighboring pixels, we construct a 2D gray histogram. Second, by the target and background segmentation, we localize the feature points at the interest points which have the local extremum of box filter responses. Third, from the 2D Haar wavelet responses, we generate the 64-dimensional (64D) feature point descriptor vectors. Finally, we perform the target matching according to the comparisons of the 64D feature point descriptor vectors. Experimental results show that our proposed approach can effectively enhance the target matching performance, as well as preserving the real-time capacity.
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Nawaz, Saqib Ali, Jingbing Li, Uzair Aslam Bhatti, et al. "A Novel Hybrid Discrete Cosine Transform Speeded Up Robust Feature-Based Secure Medical Image Watermarking Algorithm." Journal of Medical Imaging and Health Informatics 10, no. 11 (2020): 2588–99. http://dx.doi.org/10.1166/jmihi.2020.3220.
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With the advancement of networks and multimedia, digital watermarking technology has received worldwide attention as an effective method of copyright protection. Improving the anti-geometric attack ability of digital watermarking algorithms using image feature-based algorithms have received extensive attention. This paper proposes a novel robust watermarking algorithm based on SURF-DCT perceptual hashing (Speeded Up Robust Features and Discrete Cosine Transform), namely blind watermarking. We design and implement a meaningful binary watermark embedding and extraction algorithm based on the SURF feature descriptor and discrete-cosine transform domain digital image watermarking algorithm. The algorithm firstly uses the affine transformation with a feature matrix and chaotic encryption technology to preprocess the watermark image, enhance the confidentiality of the watermark, and perform block and DCT coefficients extraction on the carrier image, and then uses the positive and negative quantization rules to modify the DCT coefficients. The embedding of the watermark is completed, and the blind extraction of the watermark realized. Correlation values are more than 90% in most of the attacks. It provides better results against different noise attacks and also better performance against rotation. Transparency and high computational efficiency, coupled with dual functions of copyright protection and content authentication, is the advantage of the proposed algorithm.
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Huang, Xu, Xue Wan, and Daifeng Peng. "Robust Feature Matching with Spatial Smoothness Constraints." Remote Sensing 12, no. 19 (2020): 3158. http://dx.doi.org/10.3390/rs12193158.
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Feature matching is to detect and match corresponding feature points in stereo pairs, which is one of the key techniques in accurate camera orientations. However, several factors limit the feature matching accuracy, e.g., image textures, viewing angles of stereo cameras, and resolutions of stereo pairs. To improve the feature matching accuracy against these limiting factors, this paper imposes spatial smoothness constraints over the whole feature point sets with the underlying assumption that feature points should have similar matching results with their surrounding high-confidence points and proposes a robust feature matching method with the spatial smoothness constraints (RMSS). The core algorithm constructs a graph structure from the feature point sets and then formulates the feature matching problem as the optimization of a global energy function with first-order, spatial smoothness constraints based on the graph. For computational purposes, the global optimization of the energy function is then broken into sub-optimizations of each feature point, and an approximate solution of the energy function is iteratively derived as the matching results of the whole feature point sets. Experiments on close-range datasets with some above limiting factors show that the proposed method was capable of greatly improving the matching robustness and matching accuracy of some feature descriptors (e.g., scale-invariant feature transform (SIFT) and Speeded Up Robust Features (SURF)). After the optimization of the proposed method, the inlier number of SIFT and SURF was increased by average 131.9% and 113.5%, the inlier percentages between the inlier number and the total matches number of SIFT and SURF were increased by average 259.0% and 307.2%, and the absolute matching accuracy of SIFT and SURF was improved by average 80.6% and 70.2%.
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Amalina, Neneng Nur, Kurniawan Nur Ramadhani, and Febryanti Sthevanie. "Nuclei Detection and Classification System Based On Speeded Up Robust Feature (SURF)." EMITTER International Journal of Engineering Technology 7, no. 1 (2019): 1–13. http://dx.doi.org/10.24003/emitter.v7i1.288.
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Tumors contain a high degree of cellular heterogeneity. Various type of cells infiltrate the organs rapidly due to uncontrollable cell division and the evolution of those cells. The heterogeneous cell type and its quantity in infiltrated organs determine the level maglinancy of the tumor. Therefore, the analysis of those cells through their nuclei is needed for better understanding of tumor and also specify its proper treatment. In this paper, Speeded Up Robust Feature (SURF) is implemented to build a system that can detect the centroid position of nuclei on histopathology image of colon cancer. Feature extraction of each nuclei is also generated by system to classify the nuclei into two types, inflammatory nuclei and non-inflammatory nuclei. There are three classifiers that are used to classify the nuclei as performance comparison, those are k-Nearest Neighbor (k-NN), Random Forest (RF), and State Vector Machine (SVM). Based on the experimental result, the highest F1 score for nuclei detection is 0.722 with Determinant of Hessian (DoH) thresholding = 50 as parameter. For classification of nuclei, Random Forest classifier produces F1 score of 0.527, it is the highest score as compared to the other classifier.
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Dou, Yiwen, Kuangrong Hao, Yongsheng Ding, and Min Mao. "A Mean-Shift-Based Feature Descriptor for Wide Baseline Stereo Matching." Mathematical Problems in Engineering 2015 (2015): 1–14. http://dx.doi.org/10.1155/2015/398756.
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We propose a novel Mean-Shift-based building approach in wide baseline. Initially, scale-invariance feature transform (SIFT) approach is used to extract relatively stable feature points. As to each matching SIFT feature point, it needs a reasonable neighborhood range so as to choose feature points set. Subsequently, in view of selecting repeatable and high robust feature points, Mean-Shift controls corresponding feature scale. At last, our approach is employed to depth image acquirement in wide baseline and Graph Cut algorithm optimizes disparity information. Compared with the existing methods such as SIFT, speeded up robust feature (SURF), and normalized cross-correlation (NCC), the presented approach has the advantages of higher robustness and accuracy rate. Experimental results on low resolution image and weak feature description in wide baseline confirm the validity of our approach.
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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 (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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Xie, Botao, Jinke Li, and Xuefeng Zhao. "Strain Measurement Based on Speeded-up Robust Feature Algorithm Applied to Microimages from a Smartphone-Based Microscope." Sensors 20, no. 10 (2020): 2805. http://dx.doi.org/10.3390/s20102805.
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The objective of this study is to evaluate and improve the accuracy and stability of a strain measurement method that uses the speeded-up robust feature (SURF) method to trace the displacement of feature points in microimages and obtain the strain in objects. The microimages were acquired using a smartphone with a portable microscope, which has a broad prospect of application. An experiment was performed using an unpacked optical fiber as the experimental carrier. The matching effect of the SURF method was analyzed in the microimage, and the M-estimator sample consensus (MSAC) algorithm was used to reject outliers generated by SURF. The results indicated that the accuracy of strain measurement using the proposed method is improved by modifying the feature point tracking method and measurement method. When compared with the fiber Bragg grating (FBG) data, the maximum standard error corresponded to 2.5 με, which satisfies the requirement of structural health monitoring (SHM) in practical engineering.
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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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Luo, Haifeng ,., Yue Han, and Jiangming Kan. "Improved SURF in Color Difference Scale Space for Color Image Matching." International Journal of Circuits, Systems and Signal Processing 16 (July 26, 2022): 1055–63. http://dx.doi.org/10.46300/9106.2022.16.128.
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This paper presents an improved SURF (Speeded Up Robust Features) for image matching which considers color information. Firstly, a new color difference scale space is constructed based on color information to detect feature point. Then we extracted a 192-dimensional vector to describe feature point, which includes a 64-dimensional vector representing the brightness information and a 128-dimensional vector representing the color information in a color image. Finally, in the process images matching, a new weighted Murkovski distance is used to measure the distance between two descriptors. From the experiment results, we can know that, compared the other methods, the feature points detection method proposed is more robust. The matching scores and precision of our method are dominant among different methods of color image matching. Compared with SURF, the number of feature points detected by the proposed method increases by 163%, the average matching scores and matching precision increase by 16% and 15.81% respectively.
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H. AL-Abboodi, Rana, and Ayad A. Al-Ani. "An Efficient and Robust Combined Feature Extraction Technique for Face Recognition Systems." Iraqi Journal of Information and Communication Technology 7, no. 3 (2024): 43–54. https://doi.org/10.31987/ijict.7.3.257.
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Face recognition has long attracted a lot of interest from the research and market communities due toits many possibilities across numerous sectors, but it has proven to be exceedingly difficult to deploy in real-time applications. Over the years, several face recognition algorithms and their variations have been created. In this paper, an integrating STIP and SURF for a robust feature extraction approach is proposed. This approach consists of four steps: In the first step, researchers are collecting the input images. In the next step, image preprocessing using a Gaussian filter is used. Then, image segmentation is applied using Region of Interest (ROI). The Spatial-Temporal Interest Point (STIP) is employed to extract the features related to facial behaviors from Facial Action Units (FAUs). The most effective approach for object recognition in image processing that applies feature descriptors is a Histogram of Oriented Gradients (HOG). In the last phase, use the feature selection process using SURF (Speeded-up Robust Features). This proposed approach achieved (0.25 ms) better performance than the traditional approach.
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Li, Zuojin, Jun Peng, Liukui Chen, Ying Wu, and Jinliang Shi. "A Method for Identifying Fatigue State of Driver's Face Based on Improved AAM Algorithm." International Journal of Software Science and Computational Intelligence 9, no. 2 (2017): 31–49. http://dx.doi.org/10.4018/ijssci.2017040103.
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The change of lighting conditions and facial pose often affects the driver's face's video registration greatly, which affects the recognition accuracy of the driver's fatigue state. In this paper, the authors first analyze the reasons for the failure of the driver's face registration in the light conditions and the changes of facial gestures, and propose an adaptive AAM (Active Appearance Model) algorithm of adaptive illumination and attitude change. Then, the SURF (speeded up robust feature) feature extraction is performed on the registered driver's face video images, and finally the authors input the extracted SURF feature into the designed artificial neural network to realize the recognition of driver's fatigue state. The experimental results show that the improved AAM method can better adapt to the driver's face under the illumination and attitude changes, and the driver's facial image's SURF feature is more obvious. The average correct recognition rate of the driver's fatigue states is 92.43%.
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Li, Licheng. "Research on target feature extraction and location positioning with machine learning algorithm." Journal of Intelligent Systems 30, no. 1 (2020): 429–37. http://dx.doi.org/10.1515/jisys-2020-0072.
Full textAbstract:
Abstract The accurate positioning of target is an important link in robot technology. Based on machine learning algorithm, this study firstly analyzed the location positioning principle of binocular vision of robot, then extracted features of the target using speeded-up robust features (SURF) method, positioned the location using Back Propagation Neural Networks (BPNN) method, and tested the method through experiments. The experimental results showed that the feature extraction of SURF method was fast, about 0.2 s, and was less affected by noise. It was found from the positioning results that the output position of the BPNN method was basically consistent with the actual position, and errors in X, Y and Z directions were very small, which could meet the positioning needs of the robot. The experimental results verify the effectiveness of machine learning method and provide some theoretical support for its further promotion and application in practice.