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Journal articles on the topic 'SIFT Feature Descriptor'

Author: Grafiati

Published: 3 June 2025

Last updated: 31 July 2025

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1

Ming-liang Gao, Ming-liang Gao, Xiaomin Yang Xiaomin Yang, Yanmei Yu Yanmei Yu, and Daisheng Luo Daisheng Luo. "Photometric invariant feature descriptor based on SIFT." Chinese Optics Letters 10, s1 (2012): S11003–311008. http://dx.doi.org/10.3788/col201210.s11003.

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D, Hema, and Kannan S. "Patch-SIFT: Enhanced feature descriptor to learn human facial emotions using an Ensemble approach." Indian Journal of Science and Technology 14, no. 21 (2021): 1740–47. https://doi.org/10.17485/IJST/v14i21.2261.

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Abstract <strong>Background:</strong> Having experienced more than a year of pandemic, a variety of applications such as online classrooms, virtual office meetings, conferences, online games, Social media & Networks, Mobile applications, and many other infotainment areas have made humans live with gadgets and respond to them. However, all these applications have an impact on human behavioral transformation. It is very significant for employers to understand the emotions of their employees in the era of online office & work from home concept to increase productivity. Learning and i

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Zhang, Wanyuan, Tian Zhou, Chao Xu, and Meiqin Liu. "A SIFT-Like Feature Detector and Descriptor for Multibeam Sonar Imaging." Journal of Sensors 2021 (July 15, 2021): 1–14. http://dx.doi.org/10.1155/2021/8845814.

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Multibeam imaging sonar has become an increasingly important tool in the field of underwater object detection and description. In recent years, the scale-invariant feature transform (SIFT) algorithm has been widely adopted to obtain stable features of objects in sonar images but does not perform well on multibeam sonar images due to its sensitivity to speckle noise. In this paper, we introduce MBS-SIFT, a SIFT-like feature detector and descriptor for multibeam sonar images. This algorithm contains a feature detector followed by a local feature descriptor. A new gradient definition robust to sp

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Anbarasu, B., and G. Anitha. "Indoor Scene recognition for Micro Aerial Vehicles Navigation using Enhanced SIFT-ScSPM Descriptors." Journal of Navigation 73, no. 1 (2019): 37–55. http://dx.doi.org/10.1017/s0373463319000420.

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In this paper, a new scene recognition visual descriptor called Enhanced Scale Invariant Feature Transform-based Sparse coding Spatial Pyramid Matching (Enhanced SIFT-ScSPM) descriptor is proposed by combining a Bag of Words (BOW)-based visual descriptor (SIFT-ScSPM) and Gist-based descriptors (Enhanced Gist-Enhanced multichannel Gist (Enhanced mGist)). Indoor scene classification is carried out by multi-class linear and non-linear Support Vector Machine (SVM) classifiers. Feature extraction methodology and critical review of several visual descriptors used for indoor scene recognition in term

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Cao, Lei, Di Liao, and Bin Dang Xue. "Reference Point-Based SIFT Feature Matching." Applied Mechanics and Materials 543-547 (March 2014): 2670–73. http://dx.doi.org/10.4028/www.scientific.net/amm.543-547.2670.

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Aiming to solve the high computational and time consuming problem in SIFT feature matching, this paper presents an improved SIFT feature matching algorithm based on reference point. The algorithm starts from selecting a suitable reference point in the feature descriptor space when SIFT features are extracted. In the feature matching stage, this paper uses the Euclidean distance between descriptor vectors of the feature point to be matched and the reference point to make a fast filtration which removes most of the features that could not be matched. For the remaining SIFT features, Best-bin-fir

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Yang, Yao, Jinkang Wei, Ximing Zhan, and Xikui Miao. "A novel method for SIFT features matching based on feature dimension matching degree." MATEC Web of Conferences 277 (2019): 02027. http://dx.doi.org/10.1051/matecconf/201927702027.

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We proposes a method for fast matching SIFT feature points based on SIFT feature descriptor vector element matching. First, we discretize each dimensional feature element into an array address based on a fixed threshold value and store the corresponding feature point labels in an address. If the same dimensional feature element of the descriptor vector has the same discrete value, their feature point labels may fall into the same address. Secondly, we search the mapping address of the feature descriptor vector element to obtain the matching state of the corresponding dimensions of the feature

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Yuan, Wenan, Sai Raghavendra Prasad Poosa, and Rutger Francisco Dirks. "Comparative Analysis of Color Space and Channel, Detector, and Descriptor for Feature-Based Image Registration." Journal of Imaging 10, no. 5 (2024): 105. http://dx.doi.org/10.3390/jimaging10050105.

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The current study aimed to quantify the value of color spaces and channels as a potential superior replacement for standard grayscale images, as well as the relative performance of open-source detectors and descriptors for general feature-based image registration purposes, based on a large benchmark dataset. The public dataset UDIS-D, with 1106 diverse image pairs, was selected. In total, 21 color spaces or channels including RGB, XYZ, Y′CrCb, HLS, L*a*b* and their corresponding channels in addition to grayscale, nine feature detectors including AKAZE, BRISK, CSE, FAST, HL, KAZE, ORB, SIFT, an

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

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

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Yu, Yang, Yong Ma, Xiaoguang Mei, Fan Fan, Jun Huang, and Jiayi Ma. "A Spatial-Spectral Feature Descriptor for Hyperspectral Image Matching." Remote Sensing 13, no. 23 (2021): 4912. http://dx.doi.org/10.3390/rs13234912.

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Hyperspectral Images (HSIs) have been utilized in many fields which contain spatial and spectral features of objects simultaneously. Hyperspectral image matching is a fundamental and critical problem in a wide range of HSI applications. Feature descriptors for grayscale image matching are well studied, but few descriptors are elaborately designed for HSI matching. HSI descriptors, which should have made good use of the spectral feature, are essential in HSI matching tasks. Therefore, this paper presents a descriptor for HSI matching, called HOSG-SIFT, which ensembles spectral features with spa

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Saleem, Sajid, and Abdul Bais. "Visible Spectrum and Infra-Red Image Matching: A New Method." Applied Sciences 10, no. 3 (2020): 1162. http://dx.doi.org/10.3390/app10031162.

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Textural and intensity changes between Visible Spectrum (VS) and Infra-Red (IR) images degrade the performance of feature points. We propose a new method based on a regression technique to overcome this problem. The proposed method consists of three main steps. In the first step, feature points are detected from VS-IR images and Modified Normalized (MN)-Scale Invariant Feature Transform (SIFT) descriptors are computed. In the second step, correct MN-SIFT descriptor matches are identified between VS-IR images with projection error. A regression model is trained on correct MN-SIFT descriptors. I

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Liu, Cuiyin, Jishang Xu, and Feng Wang. "A Review of Keypoints’ Detection and Feature Description in Image Registration." Scientific Programming 2021 (December 1, 2021): 1–25. http://dx.doi.org/10.1155/2021/8509164.

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For image registration, feature detection and description are critical steps that identify the keypoints and describe them for the subsequent matching to estimate the geometric transformation parameters between two images. Recently, there has been a large increase in the research methods of detection operators and description operators, from traditional methods to deep learning methods. To solve the problem, that is, which operator is suitable for specific application problems under different imaging conditions, the paper systematically reviewed commonly used descriptors and detectors from art

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Ding, Can, Chang Wen Qu, and Feng Su. "An Improved SIFT Matching Algorithm." Applied Mechanics and Materials 239-240 (December 2012): 1232–37. http://dx.doi.org/10.4028/www.scientific.net/amm.239-240.1232.

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The high dimension and complexity of feature descriptor of Scale Invariant Feature Transform (SIFT), not only occupy the memory spaces, but also influence the speed of feature matching. We adopt the statistic feature point’s neighbor gradient method, the local statistic area is constructed by 8 concentric square ring feature of points-centered, compute gradient of these pixels, and statistic gradient accumulated value of 8 directions, and then descending sort them, at last normalize them. The new feature descriptor descend dimension of feature from 128 to 64, the proposed method can improve ma

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Chen, L., F. Rottensteiner, and C. Heipke. "FEATURE DESCRIPTOR BY CONVOLUTION AND POOLING AUTOENCODERS." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XL-3/W2 (March 10, 2015): 31–38. http://dx.doi.org/10.5194/isprsarchives-xl-3-w2-31-2015.

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In this paper we present several descriptors for feature-based matching based on autoencoders, and we evaluate the performance of these descriptors. In a training phase, we learn autoencoders from image patches extracted in local windows surrounding key points determined by the Difference of Gaussian extractor. In the matching phase, we construct key point descriptors based on the learned autoencoders, and we use these descriptors as the basis for local keypoint descriptor matching. Three types of descriptors based on autoencoders are presented. To evaluate the performance of these descriptors

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Giveki, Davar, Mohammad Ali Soltanshahi, and Gholam Ali Montazer. "A new image feature descriptor for content based image retrieval using scale invariant feature transform and local derivative pattern." Optik - International Journal for Light and Electron Optics 131 (June 7, 2016): 242–54. https://doi.org/10.5281/zenodo.13998082.

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This paper presents a new methodology to retrieve images of different scenes by introducing a novel image descriptor.&lrm; The proposed descriptor works with Scale Invariant Feature Transform (SIFT), Histogram of Oriented Gradients (HOG), Local Binary Patterns (LBP), Local Derivative Pattern (LDP), Local Ternary Pattern (LTP) and any other feature descriptor that can be applied on the image pixels.&lrm; As the proposed descriptor considers a group of pixels together, higher level of semantic is achieved.&lrm; In this work, a new image descriptor using SIFT and LDP is introduced that is able to

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Fan, Xiao Hu, Wei Ting Lin, Juan Cao, Ben Ling Li, and Yan Si. "A Description Method for MSER with SIFT Descriptor." Applied Mechanics and Materials 127 (October 2011): 115–20. http://dx.doi.org/10.4028/www.scientific.net/amm.127.115.

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Maximally Stable Extremal Regions are robust to complex affine distortion and illumination changes between reference image and real-time image. On the basis of deeply research on the SIFT descriptor, this paper propose a description algorithm for MSER using SIFT descriptor. The Second central moment is used in the algorithm to make ellipse adjustment for each irregular MSER. Then a self-adaptable rectangle area, whose side is proportional to the minor axis of the ellipse, is constructed encircling each ellipse centre. Finally, a SIFT feature vector is formed to express the MSER, after processi

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Wang, Hua Bin, and Liang Tao. "Novel Algorithm for Hand Vein Feature Extraction and Recognition with Biometrics Material Based on DEFD-SIFT Method." Advanced Materials Research 459 (January 2012): 428–31. http://dx.doi.org/10.4028/www.scientific.net/amr.459.428.

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Based on the DEFD-SIFT feature analysis, this paper presents a novel algorithm for hand vein feature extraction and recognition. First of all, the principle of the near-infrared hand vein image acquisition is introduced. Secondly, the SIFT feature analysis algorithm is used to extract the feature of hand vein. We designed a novel neighborhood descriptor, which is called “Double Ellipses Feature Descriptor”. The local texture feature is extracted effectively, while reducing the interference of skin region. Finally, the SIFT feature matching algorithm based on similarity measure is given and the

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Fowers, Spencer, Alok Desai, Dah-Jye Lee, Dan Ventura, and James Archibald. "TreeBASIS Feature Descriptor and Its Hardware Implementation." International Journal of Reconfigurable Computing 2014 (2014): 1–12. http://dx.doi.org/10.1155/2014/606210.

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This paper presents a novel feature descriptor called TreeBASIS that provides improvements in descriptor size, computation time, matching speed, and accuracy. This new descriptor uses a binary vocabulary tree that is computed using basis dictionary images and a test set of feature region images. To facilitate real-time implementation, a feature region image is binary quantized and the resulting quantized vector is passed into the BASIS vocabulary tree. A Hamming distance is then computed between the feature region image and theeffectively descriptive basis dictionary imageat a node to determin

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Wang, Yan Wei, and Hui Li Yu. "Image Registration Method Based on PCA-SIFT Feature Detection." Advanced Materials Research 712-715 (June 2013): 2395–98. http://dx.doi.org/10.4028/www.scientific.net/amr.712-715.2395.

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SIFT (scale invariant key points) which can well handle images with varying orientation and zoom, is widely used in image registration, but the algorithm is complexity and the processing time is too long. Therefore we used the PCA-SIFT (Principle Components Analysis-scale invariant key points) in image registration. Compared the SIFT descriptor, the PCA-SIFT reduced the dimensions of SIFT feature, enhanced the matching accuracy and reduce the elapsed time. Then the mutual information method used in this paper to estimate the best points. Experimental results show that PCA-SIFT algorithm is sim

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Yan, Zhiqiang, Hongyuan Wang, Qianhao Ning, and Yinxi Lu. "Robust Image Matching Based on Image Feature and Depth Information Fusion." Machines 10, no. 6 (2022): 456. http://dx.doi.org/10.3390/machines10060456.

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In this paper, we propose a robust image feature extraction and fusion method to effectively fuse image feature and depth information and improve the registration accuracy of RGB-D images. The proposed method directly splices the image feature point descriptors with the corresponding point cloud feature descriptors to obtain the fusion descriptor of the feature points. The fusion feature descriptor is constructed based on the SIFT, SURF, and ORB feature descriptors and the PFH and FPFH point cloud feature descriptors. Furthermore, the registration performance based on fusion features is tested

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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

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Zhuang, Lei, Jiyan Yu, and Yang Song. "Panoramic image mosaic method based on image segmentation and Improved SIFT algorithm." Journal of Physics: Conference Series 2113, no. 1 (2021): 012066. http://dx.doi.org/10.1088/1742-6596/2113/1/012066.

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Abstract Aiming at the problem of large amount of calculation in extracting image feature points in panoramic image mosaic by SIFT algorithm, a panoramic image mosaic algorithm based on image segmentation and Improved SIFT is proposed in this paper. The algorithm fully considers the characteristics of panoramic image stitching. Firstly, the stitched image is divided into blocks, and the maximum overlapping block of image pairs is extracted by using mutual information. The SIFT key points are extracted by SIFT algorithm, and the dog is filtered before the spatial extreme value detection of SIFT

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Xia, Xiaohua, Haoming Xiang, Yusong Cao, Zhaokai Ge, and Zainan Jiang. "Feature Extraction and Matching of Humanoid-Eye Binocular Images Based on SUSAN-SIFT Algorithm." Biomimetics 8, no. 2 (2023): 139. http://dx.doi.org/10.3390/biomimetics8020139.

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Imitating the visual characteristics of human eyes is one of the important tasks of digital image processing and computer vision. Feature correspondence of humanoid-eye binocular images is a prerequisite for obtaining the fused image. Human eyes are more sensitive to edge, because it contains much information. However, existing matching methods usually fail in producing enough edge corresponding pairs for humanoid-eye images because of viewpoint and view direction differences. To this end, we propose a novel and effective feature matching algorithm based on edge points. The proposed method con

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ZENG, Luan, and Da-Long GU. "A SIFT Feature Descriptor Based on Sector Area Partitioning." Acta Automatica Sinica 38, no. 9 (2012): 1513. http://dx.doi.org/10.3724/sp.j.1004.2012.01513.

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Li, Canlin, and Lizhuang Ma. "A new framework for feature descriptor based on SIFT." Pattern Recognition Letters 30, no. 5 (2009): 544–57. http://dx.doi.org/10.1016/j.patrec.2008.12.004.

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Kuo, Chien-Hung, Erh-Hsu Huang, Chiang-Heng Chien, and Chen-Chien Hsu. "FPGA Design of Enhanced Scale-Invariant Feature Transform with Finite-Area Parallel Feature Matching for Stereo Vision." Electronics 10, no. 14 (2021): 1632. http://dx.doi.org/10.3390/electronics10141632.

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In this paper, we propose an FPGA-based enhanced-SIFT with feature matching for stereo vision. Gaussian blur and difference of Gaussian pyramids are realized in parallel to accelerate the processing time required for multiple convolutions. As for the feature descriptor, a simple triangular identification approach with a look-up table is proposed to efficiently determine the direction and gradient of the feature points. Thus, the dimension of the feature descriptor in this paper is reduced by half compared to conventional approaches. As far as feature detection is concerned, the condition for h

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Zhang, Hua-Zhen, Dong-Won Kim, Tae-Koo Kang, and Myo-Taeg Lim. "MIFT: A Moment-Based Local Feature Extraction Algorithm." Applied Sciences 9, no. 7 (2019): 1503. http://dx.doi.org/10.3390/app9071503.

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We propose a local feature descriptor based on moment. Although conventional scale invariant feature transform (SIFT)-based algorithms generally use difference of Gaussian (DoG) for feature extraction, they remain sensitive to more complicated deformations. To solve this problem, we propose MIFT, an invariant feature transform algorithm based on the modified discrete Gaussian-Hermite moment (MDGHM). Taking advantage of MDGHM’s high performance to represent image information, MIFT uses an MDGHM-based pyramid for feature extraction, which can extract more distinctive extrema than the DoG, and MD

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Tian, Feng, and Yu Bo Yan. "A SIFT Feature Matching Algorithm Based on Semi-Variance Function." Advanced Materials Research 647 (January 2013): 896–900. http://dx.doi.org/10.4028/www.scientific.net/amr.647.896.

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For solving the low matching efficiency problem due to high dimension of eigenvector in SIFT, a SIFT feature matching algorithm based on semi-variance function is proposed. For each feature point in image SIFT feature point zone, m beams are generated by using the position of the feature point as center and the orientation of the feature point as start direction. The image semi-variance function value of each beam, which is treated as SIFT value of eigenvector descriptor, is used in the algorithm aiming at reducing the dimension of eigenvector and improving image matching efficiency. The exper

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FERRAZ, CAROLINA TOLEDO, OSMANDO PEREIRA, MARCOS VERDINI ROSA, and ADILSON GONZAGA. "OBJECT RECOGNITION BASED ON BAG OF FEATURES AND A NEW LOCAL PATTERN DESCRIPTOR." International Journal of Pattern Recognition and Artificial Intelligence 28, no. 08 (2014): 1455010. http://dx.doi.org/10.1142/s0218001414550106.

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Bag of Features (BoF) has gained a lot of interest in computer vision. Visual codebook based on robust appearance descriptors extracted from local image patches is an effective means of texture analysis and scene classification. This paper presents a new method for local feature description based on gray-level difference mapping called Mean Local Mapped Pattern (M-LMP). The proposed descriptor is robust to image scaling, rotation, illumination and partial viewpoint changes. The training set is composed of rotated and scaled images, with changes in illumination and view points. The test set is

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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 describ

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Sahan, Ali Sahan, Nisreen Jabr, Ahmed Bahaaulddin, and Ali Al-Itb. "Human identification using finger knuckle features." International Journal of Advances in Soft Computing and its Applications 14, no. 1 (2022): 88–101. http://dx.doi.org/10.15849/ijasca.220328.07.

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Abstract Many studies refer that the figure knuckle comprises unique features. Therefore, it can be utilized in a biometric system to distinguishing between the peoples. In this paper, a combined global and local features technique has been proposed based on two descriptors, namely: Chebyshev Fourier moments (CHFMs) and Scale Invariant Feature Transform (SIFT) descriptors. The CHFMs descriptor is used to gaining the global features, while the scale invariant feature transform descriptor is utilized to extract local features. Each one of these descriptors has its advantages; therefore, combinin

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Khan, Sumeer Ahmad, Yonis Gulzar, Sherzod Turaev, and Young Suet Peng. "A Modified HSIFT Descriptor for Medical Image Classification of Anatomy Objects." Symmetry 13, no. 11 (2021): 1987. http://dx.doi.org/10.3390/sym13111987.

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Modeling low level features to high level semantics in medical imaging is an important aspect in filtering anatomy objects. Bag of Visual Words (BOVW) representations have been proven effective to model these low level features to mid level representations. Convolutional neural nets are learning systems that can automatically extract high-quality representations from raw images. However, their deployment in the medical field is still a bit challenging due to the lack of training data. In this paper, learned features that are obtained by training convolutional neural networks are compared with

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González-Aguilera, D., E. Ruiz de Oña, L. López-Fernandez, et al. "PHOTOMATCH: AN OPEN-SOURCE MULTI-VIEW AND MULTI-MODAL FEATURE MATCHING TOOL FOR PHOTOGRAMMETRIC APPLICATIONS." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLIII-B5-2020 (August 24, 2020): 213–19. http://dx.doi.org/10.5194/isprs-archives-xliii-b5-2020-213-2020.

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Abstract. Automatic feature matching is a crucial step in Structure-from-Motion (SfM) applications for 3D reconstruction purposes. From an historical perspective we can say now that SIFT was the enabling technology that made SfM a successful and fully automated pipeline. SIFT was the ancestor of a wealth of detector/descriptor methods that are now available. Various research activities have tried to benchmark detector/descriptors operators, but a clear outcome is difficult to be drawn. This paper presents an ISPRS Scientific Initiative aimed at providing the community with an educational open-

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

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

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Bindu, Hima, and Manjunathachari K. "Hybrid feature descriptor and probabilistic neuro-fuzzy system for face recognition." Sensor Review 38, no. 3 (2018): 269–81. http://dx.doi.org/10.1108/sr-06-2017-0115.

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Purpose This paper aims to develop the Hybrid feature descriptor and probabilistic neuro-fuzzy system for attaining the high accuracy in face recognition system. In recent days, facial recognition (FR) systems play a vital part in several applications such as surveillance, access control and image understanding. Accordingly, various face recognition methods have been developed in the literature, but the applicability of these algorithms is restricted because of unsatisfied accuracy. So, the improvement of face recognition is significantly important for the current trend. Design/methodology/app

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Oad, Ammar, Karishma Kumari, Imtiaz Hussain, Feng Dong, Bacha Hammad, and Rajkumari Oad. "Performance comparison of ORB, SURF and SIFT using Intracranial Haemorrhage CTScan Brain images." International Journal of Artificial Intelligence & Mathematical Sciences 1, no. 2 (2023): 26–34. http://dx.doi.org/10.58921/ijaims.v1i2.41.

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Medical images are crucial for both the doctor's accurate diagnosis and the patient's subsequent therapy. It is feasible to swiftly identify lesions in medical photos by using clever algorithms, and it is crucial to extract information from images. Feature extraction is an important step in image classification. It allows the representation of the content of images as perfectly as possible. The intention of this study is to certain overall performance assessment among the feature detector and the descriptor method, especially while there are numerous combos for assessment. Three techniques wer

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Xiang, Zejun, Ronghua Yang, Chang Deng, Mingxing Teng, Mengkun She, and Degui Teng. "An Illumination Insensitive Descriptor Combining the CSLBP Features for Street View Images in Augmented Reality: Experimental Studies." ISPRS International Journal of Geo-Information 9, no. 6 (2020): 362. http://dx.doi.org/10.3390/ijgi9060362.

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The common feature matching algorithms for street view images are sensitive to the illumination changes in augmented reality (AR), this may cause low accuracy of matching between street view images. This paper proposes a novel illumination insensitive feature descriptor by integrating the center-symmetric local binary pattern (CS-LBP) into a common feature description framework. This proposed descriptor can be used to improve the performance of eight commonly used feature-matching algorithms, e.g., SIFT, SURF, DAISY, BRISK, ORB, FREAK, KAZE, and AKAZE. We perform the experiments on five street

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Wang, Xu Guang, Li Jun Lin, and Hai Yan Cheng. "A New Method for Feature Point Matching: Inner and Exterior Product." Applied Mechanics and Materials 48-49 (February 2011): 79–83. http://dx.doi.org/10.4028/www.scientific.net/amm.48-49.79.

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In this paper, a novel feature descriptor called gradient correlation descriptor (GCD) is proposed. The GCD descriptor uses the gradient correlation measure defined by the inner and exterior product to characterize the gradient distributions in neighborhoods of feature points, and it has the following advantages: Its construction is very simple because of only the inner and exterior product operations are used; Its distinctive performance is better than the region-based SIFT descriptors since the gradient correlation measure can effectively characterize the gradient distributions in neighborho

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Damarsiwi, Dyah Kartika, Elindra Ambar Pambudi, Maulida Ayu Fitriani, and Feri Wibowo. "Face Detection in Complex Background using Scale Invariant Feature Transform and Haar Cascade Classifier Methods." Sinkron 8, no. 2 (2024): 852–60. http://dx.doi.org/10.33395/sinkron.v8i2.13556.

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Face detection is a process by a computer system that can find and identify human faces in digital images or videos. One of the main challenges faced in the face detection process is the complex background. Complex backgrounds, such as many color combinations in the image, can interfere with the detection process. To overcome this challenge, this research uses a combination of two methods: Scale Invariant Feature Transform (SIFT) and Haar Cascade Classifier. Scale Invariant Feature Transform (SIFT) is a method used in image processing to identify and describe unique features in an image. The S

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Ahmed, Chater, and Lasfar Abdelali. "New approach to the identification of the easy expression recognition system by robust techniques (SIFT, PCA-SIFT, ASIFT and SURF)." TELKOMNIKA Telecommunication, Computing, Electronics and Control 18, no. 2 (2020): 695–704. https://doi.org/10.12928/TELKOMNIKA.v18i2.13726.

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In recent years, facial recognition has been a major problem in the field of computer vision, which has attracted lots of interest in previous years because of its use in different applications by different domains and image analysis. Which is based on the extraction of facial descriptors, it is a very important step in facial recognition. In this article, we compared robust methods (SIFT, PCA-SIFT, ASIFT and SURF) to extract relevant facial information with different facial posture variations (open and unopened mouth, glasses and no glasses, open and closed eyes). The simulation results show

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Prof. Dr. P. C. Senthilmahesh and Ms. S. Annapoorani. "An Efficient Search on Cloud Images with Blockchain Technology." International Journal of Scientific Research in Computer Science, Engineering and Information Technology 10, no. 2 (2024): 806–15. http://dx.doi.org/10.32628/cseit124102119.

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A key task in computer vision is image retrieval, which has wide-ranging applications across multiple domains. Using query picture features, this abstract proposes an image retrieval approach that focuses on the widely used Scale-Invariant Feature Transform (SIFT) algorithm for feature extraction and distance calculation. The suggested method starts by extracting SIFT features from a set of photos, building a keypoints and descriptor database. By capturing the unique qualities of nearby image regions, these features enable reliable matching and retrieval. The security of private cloud data, wh

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Zhang, Jiaming, Xuejuan Hu, Tan Zhang, et al. "Binary Neighborhood Coordinate Descriptor for Circuit Board Defect Detection." Electronics 12, no. 6 (2023): 1435. http://dx.doi.org/10.3390/electronics12061435.

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Due to the periodicity of circuit boards, the registration algorithm based on keypoints is less robust in circuit board detection and is prone to misregistration problems. In this paper, the binary neighborhood coordinate descriptor (BNCD) is proposed and applied to circuit board image registration. The BNCD consists of three parts: neighborhood description, coordinate description, and brightness description. The neighborhood description contains the grayscale information of the neighborhood, which is the main part of BNCD. The coordinate description introduces the actual position of the keypo

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Urban, S., and M. Weinmann. "FINDING A GOOD FEATURE DETECTOR-DESCRIPTOR COMBINATION FOR THE 2D KEYPOINT-BASED REGISTRATION OF TLS POINT CLOUDS." ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences II-3/W5 (August 19, 2015): 121–28. http://dx.doi.org/10.5194/isprsannals-ii-3-w5-121-2015.

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The automatic and accurate registration of terrestrial laser scanning (TLS) data is a topic of great interest in the domains of city modeling, construction surveying or cultural heritage. While numerous of the most recent approaches focus on keypoint-based point cloud registration relying on forward-projected 2D keypoints detected in panoramic intensity images, little attention has been paid to the selection of appropriate keypoint detector-descriptor combinations. Instead, keypoints are commonly detected and described by applying well-known methods such as the Scale Invariant Feature Transfor

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Yohanes, Banu Wirawan. "Images Similarity based on Bags of SIFT Descriptor and K-Means Clustering." Techné : Jurnal Ilmiah Elektroteknika 18, no. 02 (2019): 137–46. http://dx.doi.org/10.31358/techne.v18i02.217.

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The content based image retrieval is developed and receives many attention from computer vision, supported by the ubiquity of Internet and digital devices. Bag-of-words method from text-based image retrieval trains images’ local features to build visual vocabulary. These visual words are used to represent local features, then quantized before clustering into number of bags. Here, the scale invariant feature transform descriptor is used as local features of images that will be compared each other to find their similarity. It is robust to clutter and partial visibility compared to global feature

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Kniaz, V. V., V. V. Fedorenko, and N. A. Fomin. "DEEP LEARNING FOR LOWTEXTURED IMAGE MATCHING." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-2 (May 30, 2018): 513–18. http://dx.doi.org/10.5194/isprs-archives-xlii-2-513-2018.

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Low-textured objects pose challenges for an automatic 3D model reconstruction. Such objects are common in archeological applications of photogrammetry. Most of the common feature point descriptors fail to match local patches in featureless regions of an object. Hence, automatic documentation of the archeological process using Structure from Motion (SfM) methods is challenging. Nevertheless, such documentation is possible with the aid of a human operator. Deep learning-based descriptors have outperformed most of common feature point descriptors recently. This paper is focused on the development

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Lu, Ying, Hui Qin Wang, Fei Xu, and Wei Guang Liu. "The Feature Extraction and Matching Algorithm Based on the Fire Video Image Orientation." Applied Mechanics and Materials 380-384 (August 2013): 3986–89. http://dx.doi.org/10.4028/www.scientific.net/amm.380-384.3986.

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Because the SIFT (scale invariant feature transform) algorithm can not accurately locate the flame shape features and computationally intensive, this article proposed a stereo video image fire flame matching method which is a combination of Harris corner and SIFT algorithm. Firstly, the algorithm extracts image feature points using Harris operator in Gaussian scale space and defines the main directions for each feature point, and then calculates the 32-dimensional feature vectors of each feature point descriptor and the Euclidean distance to match two images. Experimental results of image matc

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Tang, Liang, Shuhua Ma, Xianchun Ma, and Hairong You. "Research on Image Matching of Improved SIFT Algorithm Based on Stability Factor and Feature Descriptor Simplification." Applied Sciences 12, no. 17 (2022): 8448. http://dx.doi.org/10.3390/app12178448.

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In view of the problems of long matching time and the high-dimension and high-matching rate errors of traditional scale-invariant feature transformation (SIFT) feature descriptors, this paper proposes an improved SIFT algorithm with an added stability factor for image feature matching. First of all, the stability factor was increased during construction of the scale space to eliminate matching points of unstable points, speed up image processing and reduce the dimension and the amount of calculation. Finally, the algorithm was experimentally verified and showed excellent results in experiments

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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 (1

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Zhu, Zun Shang, Yue Qiang Zhang, Xiang Zhou, and Yang Shang. "An Affine SIFT Matching Algorithm Based on Local Patch Shape Estimation." Applied Mechanics and Materials 519-520 (February 2014): 553–56. http://dx.doi.org/10.4028/www.scientific.net/amm.519-520.553.

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In this paper we present an affine SIFT matching method to achieve reliable correspondence points in stereo matching with large viewpoint changes. We extended the affine invariant of the conventional SIFT approach by estimating the shape of the local patch around the interest point. Since we can obtain the scale information by SIFT detector, a second moment matrix (SMM) descriptor was employed to describe the shape. Furthermore, by comparing the shapes of the potential matches, we can normalize the template of SIFT descriptor and obtain the initial affine transformation. At last, we applied th

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Liao, B., and H. F. Wang. "The Optimization of SIFT Feature Matching Algorithm on Face Recognition Based on BP Neural Network." Applied Mechanics and Materials 743 (March 2015): 359–64. http://dx.doi.org/10.4028/www.scientific.net/amm.743.359.

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In the field of object recognition, the SIFT feature is known to be a very successful local invariant descriptor and has wide application in different domains. However it also has some limitations, for example, in the case of facial illumination variation or under large tilt angle, the identification rate of the SIFT algorithm drops quickly. In order to reduce the probability of mismatching pairs, and improve the matching efficiency of SIFT algorithm, this paper proposes a novel feature matching algorithm. The basic idea is taking the successful-matched SIFT feature points as the training samp

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Xu, Yun Xi, and Fang Chen. "Real-Time and Robust Stereo Visual Navigation Localization Algorithm Based on ORB." Applied Mechanics and Materials 241-244 (December 2012): 478–82. http://dx.doi.org/10.4028/www.scientific.net/amm.241-244.478.

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The biggest challenge of visual navigation localization is feature extraction and association. Currently, the most widely used method is simple corner feature and simple matching strategy based on SAD or NCC. Another option is scale invariant feature and rotation invariant descriptor, typically as SIFT, SURF. Feature extraction and matching methods based on the SIFT or SURF are accurate and robust. However, its computational complexity is too high and not suitable for the real-time navigation localization task. This paper presents a new fast, accurate, robust stereo vision navigation localizat

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