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1
B.Daneshvar, M. "SCALE INVARIANT FEATURE TRANSFORM PLUS HUE FEATURE." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-2/W6 (August 23, 2017): 27–32. http://dx.doi.org/10.5194/isprs-archives-xlii-2-w6-27-2017.
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This paper presents an enhanced method for extracting invariant features from images based on Scale Invariant Feature Transform (SIFT). Although SIFT features are invariant to image scale and rotation, additive noise, and changes in illumination but we think this algorithm suffers from excess keypoints. Besides, by adding the hue feature, which is extracted from combination of hue and illumination values in HSI colour space version of the target image, the proposed algorithm can speed up the matching phase. Therefore, we proposed the Scale Invariant Feature Transform plus Hue (SIFTH) that can remove the excess keypoints based on their Euclidean distances and adding hue to feature vector to speed up the matching process which is the aim of feature extraction. In this paper we use the difference of hue features and the Mean Square Error (MSE) of orientation histograms to find the most similar keypoint to the under processing keypoint. The keypoint matching method can identify correct keypoint among clutter and occlusion robustly while achieving real-time performance and it will result a similarity factor of two keypoints. Moreover removing excess keypoint by SIFTH algorithm helps the matching algorithm to achieve this goal.
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Taha, Mohammed A., Hanaa M. Ahmed, and Saif O. Husain. "Iris Features Extraction and Recognition based on the Scale Invariant Feature Transform (SIFT)." Webology 19, no. 1 (2022): 171–84. http://dx.doi.org/10.14704/web/v19i1/web19013.
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Iris Biometric authentication is considered to be one of the most dependable biometric characteristics for identifying persons. In actuality, iris patterns have invariant, stable, and distinguishing properties for personal identification. Due to its excellent dependability in personal identification, iris recognition has received more attention. Current iris recognition methods give good results especially when NIR and specific capture conditions are used in collaboration with the user. On the other hand, values related to images captured using VW are affected by noise such as blurry images, eye skin, occlusion, and reflection, which negatively affects the overall performance of the recognition systems. In both NIR and visible spectrum iris images, this article presents an effective iris feature extraction strategy based on the scale-invariant feature transform algorithm (SIFT). The proposed method was tested on different databases such as CASIA v1 and ITTD v1, as NIR images, as well as UBIRIS v1 as visible-light color images. The proposed system gave good accuracy rates compared to existing systems, as it gave an accuracy rate of (96.2%) when using CASIA v1 and (96.4%) in ITTD v1, while the system accuracy dropped to (84.0 %) when using UBIRIS v1.
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Qu, Zhong, and Zheng Yong Wang. "The Improved Algorithm of Scale Invariant Feature Transform on Palmprint Recognition." Advanced Materials Research 186 (January 2011): 565–69. http://dx.doi.org/10.4028/www.scientific.net/amr.186.565.
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This paper presents a new method of palmprint recognition based on improved scale invariant feature transform (SIFT) algorithm which combines the Euclidean distance and weighted sub-region. It has the scale, rotation, affine, perspective, illumination invariance, and also has good robustness to the target's motion, occlusion, noise and other factors. Simulation results show that the recognition rate of the improved SIFT algorithm is higher than the recognition rate of SIFT algorithm.
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Wu, Shu Guang, Shu He, and Xia Yang. "The Application of SIFT Method towards Image Registration." Advanced Materials Research 1044-1045 (October 2014): 1392–96. http://dx.doi.org/10.4028/www.scientific.net/amr.1044-1045.1392.
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The scale invariant features transform (SIFT) is commonly used in object recognition,According to the problems of large memory consumption and low computation speed in SIFT (Scale Invariant Feature Transform) algorithm.During the image registration methods based on point features,SIFT point feature is invariant to image scale and rotation, and provides robust matching across a substantial range of affine distortion. Experiments show that on the premise that registration accuracy is stable, the proposed algorithm solves the problem of high requirement of memory and the efficiency is improved greatly, which is applicable for registering remote sensing images of large areas.
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Wang, Yan Wei, and Hui Li Yu. "Medical Image Feature Matching Based on Wavelet Transform and SIFT Algorithm." Applied Mechanics and Materials 65 (June 2011): 497–502. http://dx.doi.org/10.4028/www.scientific.net/amm.65.497.
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A feature matching algorithm based on wavelet transform and SIFT is proposed in this paper, Firstly, Biorthogonal wavelet transforms algorithm is used for medical image to delaminating, and restoration the processed image. Then the SIFT (Scale Invariant Feature Transform) applied in this paper to abstracting key point. Experimental results show that our algorithm compares favorably in high-compressive ratio, the rapid matching speed and low storage of the image, especially for the tilt and rotation conditions.
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Ariel, Muhammad Baresi, Ratri Dwi Atmaja, and Azizah Azizah. "Implementasi Metode Speed Up Robust Feature dan Scale Invariant Feature Transform untuk Identifikasi Telapak Kaki Individu." JURNAL Al-AZHAR INDONESIA SERI SAINS DAN TEKNOLOGI 3, no. 4 (2017): 178. http://dx.doi.org/10.36722/sst.v3i4.232.
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<p><em>Abstrak</em><strong> - Biometrik merupakan metode pengidentifikasian individu berdasarkan ciri fisiknya. Salah satu ciri fisik yang dapat digunakan untuk biometrik adalah telapak kaki. Ciri fisik ini dipilih karena memiliki tingkat keunikan yang tinggi, sehingga hampir tidak terdapat individu yang memiliki ciri yang sama. Metode-metode ekstraksi ciri seperti Scale Invariant Feature Transform (SIFT) dan Speed Up Robust Feature (SURF) akan sesuai jika digunakan untuk mendukung sistem identifikasi telapak kaki. Tahapan yang dilakukan untuk mendapatkan deskriptor dimulai dari scanning telapak kaki, pre-processing, ekstraksi ciri dengan menggunakan SURF dan SIFT sampai pada proses matching pada saat pengujian. Perbandingan keduanya dilihat dari aspek akurasi. Proses penentuan klasifikasi dan kelas menggunakan algoritma K-Nearest Neighbor (K- NN). Hasilnya akan menjadi data-data penelitian dalam paper ini. Diharapkan menggunakan metode SIFT dan SURF akan memberikan hasil dengan tingkat keakurasian yang tinggi.</strong></p><p><em><strong>Kata Kunci</strong> – Biometric, Footprint, SURF, SIFT, K- NN</em></p><p><em>Abstract</em><strong> - Biometric is a method used to identify indivduals using their physical features. One of the physical features that can be used for biometric is the footprint. The footprint was chosen because of having a high level of uniqueness where it is almost impossible to find two individuals that have the same footprint. Feature extraction methods such as Scale Invariant Feature Transform (SIFT) and Speed Up Robust Feature (SURF) are appropriate if used for footprint identification system. The steps used in obtaining descriptor start from scanning the footprint, pre-processing, feature extraction using SURF and SIFT and last the matching process. The comparison between the two methods will be observed by their accuracy. The K-Nearest Neighbor (K-NN) algorithm will be used for the classification process. The outputs will be used for research data in this research proposal. It will be expected that using SIFT and SURF for the feature extraction will result in high accuracy.</strong></p><p><em><strong>Keywords</strong> – Biometric, Footprint, SURF, SIFT, K- NN</em></p>
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Journal, Baghdad Science. "Scale-Invariant Feature Transform Algorithm with Fast Approximate Nearest Neighbor." Baghdad Science Journal 14, no. 3 (2017): 651–61. http://dx.doi.org/10.21123/bsj.14.3.651-661.
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There is a great deal of systems dealing with image processing that are being used and developed on a daily basis. Those systems need the deployment of some basic operations such as detecting the Regions of Interest and matching those regions, in addition to the description of their properties. Those operations play a significant role in decision making which is necessary for the next operations depending on the assigned task. In order to accomplish those tasks, various algorithms have been introduced throughout years. One of the most popular algorithms is the Scale Invariant Feature Transform (SIFT). The efficiency of this algorithm is its performance in the process of detection and property description, and that is due to the fact that it operates on a big number of key-points, the only drawback it has is that it is rather time consuming. In the suggested approach, the system deploys SIFT to perform its basic tasks of matching and description is focused on minimizing the number of key-points which is performed via applying Fast Approximate Nearest Neighbor algorithm, which will reduce the redundancy of matching leading to speeding up the process. The proposed application has been evaluated in terms of two criteria which are time and accuracy, and has accomplished a percentage of accuracy of up to 100%, in addition to speeding up the processes of matching and description.
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Xu, Mengxi, Yingshu Lu, and Xiaobin Wu. "Annular Spatial Pyramid Mapping and Feature Fusion-Based Image Coding Representation and Classification." Wireless Communications and Mobile Computing 2020 (September 11, 2020): 1–9. http://dx.doi.org/10.1155/2020/8838454.
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Conventional image classification models commonly adopt a single feature vector to represent informative contents. However, a single image feature system can hardly extract the entirety of the information contained in images, and traditional encoding methods have a large loss of feature information. Aiming to solve this problem, this paper proposes a feature fusion-based image classification model. This model combines the principal component analysis (PCA) algorithm, processed scale invariant feature transform (P-SIFT) and color naming (CN) features to generate mutually independent image representation factors. At the encoding stage of the scale-invariant feature transform (SIFT) feature, the bag-of-visual-word model (BOVW) is used for feature reconstruction. Simultaneously, in order to introduce the spatial information to our extracted features, the rotation invariant spatial pyramid mapping method is introduced for the P-SIFT and CN feature division and representation. At the stage of feature fusion, we adopt a support vector machine with two kernels (SVM-2K) algorithm, which divides the training process into two stages and finally learns the knowledge from the corresponding kernel matrix for the classification performance improvement. The experiments show that the proposed method can effectively improve the accuracy of image description and the precision of image classification.
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Li, Mao Hai, and Li Ning Sun. "Monocular Vision Based Mobile Robot 3D Map Building." Applied Mechanics and Materials 43 (December 2010): 49–52. http://dx.doi.org/10.4028/www.scientific.net/amm.43.49.
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A robust dense 3D feature map is built only with monocular vision and odometry. Monocular vision mounted on the robot front-end tracks the 3D natural landmarks, which are structured with matching Scale Invariant Feature Transform (SIFT) feature matching pairs. SIFT features are highly distinctive and invariant to image scaling, rotation, and change in 3D viewpoints. A fast SIFT feature matching algorithm is implemented with the KD-Tree based nearest search approach in the time cost of O(log2N), and matches with large error are eliminated by epipolar line restriction. A map building algorithm based on 3D spatial SIFT landmarks is designed and implemented. Experiment results on Pioneer mobile robot in a real indoor environment show the superior performance of our proposed method.
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MIFTAHUDDIN, YUSUP, NUR FITRIANTI FAHRUDIN, and MOCHAMAD FACHRY PRAYOGA. "Algoritma Scale Invariant Feature Transform (SIFT) pada Deteksi Kendaraan Bermotor di Jalan Raya." MIND Journal 5, no. 1 (2021): 54–65. http://dx.doi.org/10.26760/mindjournal.v5i1.54-65.
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AbstrakProses perhitungan jumlah kendaraan yang masih dilakukan secara manual dan membutuhkan banyak operator dalam pendataan. Berdasarkan hal itu, diperlukan sistem yang mampu mendeteksi dan mengklasifikasi kendaraan yang melintas di jalan raya secara otomatis. Dalam mengidentifikasi citra kendaraan, sistem menggunakan algoritma SIFT. Hasil fitur akan dibandingkan dengan metode K-Nearest Neighbor (KNN). Sistem dibangun untuk mendeteksi jenis kendaraan berat dengan mengukur tingkat akurasi keberhasilan berdasarkan nilai pencahayaan, jumlah objek, perubahan rotasi, serta pada kondisi siang dan malam hari. Dataset yang digunakan berjumlah 100 citra kendaraan berat. Kinerja sistem pada kondisi siang hari mendapat nilai presisi rata-rata 100%, nilai recall 54%, dan nilai akurasi 78%. Hasil pengukuran presisi dan recall, diperoleh nilai f-measure sebesar 67 %.Kata kunci: SIFT, kendaraan berat, K-Nearest NeighbourAbstractThe process of collecting vehicles still done manually and requires a lot of human resources. Therefore, we need a system that can detect and classify vehicles passing on the highway automatically. SIFT is an algorithm for identification of an image. The features will be compared using the K-Nearest Neighbor (KNN) method. In this study, system will be designed to detect the type of heavy vehicle using the SIFT method to measure the accuracy of success based on the value of lighting, number of objects, changes in rotation, and day night conditions. Dataset used was 100 heavy vehicle images. The system performance during daytime conditions gets an average precision value of 100%, a recall value of 54%, and an accuracy value of 78%. From the results of precision and recall, the f-measure value is 67 %.Keywords: SIFT, heavy vehicles, K-Nearest Neighbour
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Yin, Mu Yi, Fei Guan, Peng Ding, and Zhong Feng Liu. "Implementation of Image Matching Algorithm Based on SIFT Features." Applied Mechanics and Materials 602-605 (August 2014): 3181–84. http://dx.doi.org/10.4028/www.scientific.net/amm.602-605.3181.
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With the aim to solve the implement problem in scale invariant feature transform (SIFT) algorithm, the theory and the implementation process was analyzed in detail. The characteristics of the SIFT method were analyzed by theory, combined with the explanation of the Rob Hess SIFT source codes. The effect of the SIFT method was validated by matching two different real images. The matching result shows that the features extracted by SIFT method have excellent adaptive and accurate characteristics to image scale, viewpoint change, which are useful for the fields of image recognition, image reconstruction, etc.
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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 matching speed and keep matching precision at the same time.
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Huang, Fenghua, Zhengyuan Mao, and Wenzao Shi. "ICA-ASIFT-Based Multi-Temporal Matching of High-Resolution Remote Sensing Urban Images." Cybernetics and Information Technologies 16, no. 5 (2016): 34–49. http://dx.doi.org/10.1515/cait-2016-0050.
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Abstract While SIFT (Scale Invariant Feature Transform) features are used to match High-Resolution (HR) remote sensing urban images captured at different phases with large scale and view variations, feature points are few and the matching accuracy is low. Although replacing SIFT with fully affine invariant features ASIFT (Affine-SIFT) can increase the number of feature points, it results in matching inefficiency and a non-uniform distribution of matched feature point pairs. To address these problems, this paper proposes the novel matching method ICA-ASIFT, which matches HR remote sensing urban images captured at different phases by using an Independent Component Analysis algorithm (ICA) and ASIFT features jointly. First, all possible affine deformations are modeled for the image transform, extracting ASIFT features of remote sensing images captured at different times. The ICA algorithm reduces the dimensionality of ASIFT features and improves matching efficiency of subsequent ASIFT feature point pairs. Next, coarse matching is performed on ASIFT feature point pairs through the algorithms of Nearest Vector Angle Ratio (NVAR), Direction Difference Analysis (DDA) and RANdom SAmple Consensus (RANSAC), eliminating apparent mismatches. Then, fine matching is performed on rough matched point pairs using a Neighborhoodbased Feature Graph Matching algorithm (NFGM) to obtain final ASIFT matching point pairs of remote sensing images. Finally, final matching point pairs are used to compute the affine transform matrix. Matching HR remote sensing images captured at different phases is achieved through affine transform. Experiments are used to compare the performance of ICA-ASFIT and three other algorithms (i.e., Harris- SIFT, PCA-SIFT, TD-ASIFT) on HR remote sensing images captured at different times in different regions. Experimental results show that the proposed ICA-ASFIT algorithm effectively matches HR remote sensing urban images and outperforms other algorithms in terms of matching accuracy and efficiency.
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Hou, Yong, and Qingjun Wang. "Research and Improvement of Content-Based Image Retrieval Framework." International Journal of Pattern Recognition and Artificial Intelligence 32, no. 12 (2018): 1850043. http://dx.doi.org/10.1142/s021800141850043x.
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This paper proposed a high-performance image retrieval framework, which combines the improved feature extraction algorithm SIFT (Scale Invariant Feature Transform), improved feature matching, improved feature coding Fisher and improved Gaussian Mixture Model (GMM) for image retrieval. Aiming at the problem of slow convergence of traditional GMM algorithm, an improved GMM is proposed. This algorithm initializes the GMM by using on-line [Formula: see text]-means clustering method, which improves the convergence speed of the algorithm. At the same time, when the model is updated, the storage space is saved through the improvement of the criteria for matching rules and generating new Gaussian distributions. Aiming at the problem that the dimension of SIFT (Scale Invariant Feature Transform) algorithm is too high, the matching speed is too slow and the matching rate is low, an improved SIFT algorithm is proposed, which preserves the advantages of SIFT algorithm in fuzzy, compression, rotation and scaling invariance advantages, and improves the matching speed, the correct match rate is increased by an average of 40% to 55%. Experiments on a recently released VOC 2012 database and a database of 20 category objects containing 230,800 images showed that the framework had high precision and recall rates and less query time. Compared with the standard image retrieval framework, the improved image retrieval framework can detect the moving target quickly and effectively and has better robustness.
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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 MDGHM-based magnitude and orientation for feature description. We compared the proposed MIFT method performance with current best practice methods for six image deformation types, and confirmed that MIFT matching accuracy was superior of other SIFT-based methods.
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Li, Xiao Qin, and Kang Ling Fang. "Super-Resolution by POCS-SIFT Approach." Applied Mechanics and Materials 519-520 (February 2014): 562–67. http://dx.doi.org/10.4028/www.scientific.net/amm.519-520.562.
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Projection Onto Convex Sets theory (POCS) and Scale Invariant Feature Transform (SIFT) algorithm were introduced for super-resolution restoration of moving blurred image. In order to achieve a better restored image, a POCS-SIFT based super-resolution image restoration algorithm was proposed, which incorporates POCS theory and SIFT algorithm. From experimental results, the improved restored images are obtained by POCS-SIFT hybrid algorithm.
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El Khattabi, Zaynab, Youness Tabii, and Abdelhamid Benkaddour. "Video Shot Boundary Detection in Sport Video Using the Scale Invariant Feature Transform." Applied Mechanics and Materials 850 (August 2016): 152–58. http://dx.doi.org/10.4028/www.scientific.net/amm.850.152.
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The main purpose of shot boundary detection is to detect visual content changes between consecutives frames of a video. In this paper, a new shot boundary detection algorithm is proposed based on the scale invariant feature transform (SIFT). The first stage consists on a top down search scheme to detect the locations of transitions by comparing the ratio of matched features extracted via SIFT for every RGB channel of video frames. A temporal sampling period is used to avoid the frame by frame processing. The overview step provides the changes of matched features ratio all along the video. Secondly, a function is performed to detect the shot boundaries. The proposed method can be used for detecting gradual transitions as well as hard cuts and without requiring any training of the video content in advance. Experiments have been conducted on sports video and show that this algorithm achieves good results in detecting both abrupt and gradual transitions.
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Akbar, Ronny Makhfuddin, and Nani Sunarmi. "Pengenalan Barang Pada Kereta Belanja Menggunakan Metode Scale Invariant Feature Transform (SIFT)." Jurnal Teknologi Informasi dan Ilmu Komputer 5, no. 6 (2018): 667. http://dx.doi.org/10.25126/jtiik.2018561046.
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Menunggu dalam suatu antrian di supermarket sering terjadi dalam kehidupan sehari-hari. Antrian tersebut terjadi karena pada kasir mengharuskan setiap barang diperiksa untuk dipindai menggunakan <em>barcode</em>. Hal ini dapat diatasi dengan menggunakan aplikasi pengenalan atau deteksi barang berbasis pengolahan citra yang akan membantu mengurangi permasalahan pada kasir seperti scanner tidak mengenali <em>barcode </em>barang, label harga barang tidak dapat dikenali sehingga membuat proses pelayanan menjadi lama. Tujuan dari penelitian ini adalah membuat algoritma yang membantu kasir untuk mengenali barang pada kereta belanja dan menampilkan harga barang dengan hanya mengambil citra kereta belanja. Algoritma yang diusulkan untuk mendeteksi dan mengidentifikasi beberapa barang dengan pencocokan citra menggunakan <em>Scale Invariant Feature Transform</em> (SIFT) serta metode RANSAC digunakan untuk menghasilkan <em>homography</em> terbaik untuk memetakan kotak pembatas dari <em>database</em> citra ke citra kereta belanja. Citra akan tersegmentasi berdasarkan barang yang ada, dan masing-masing segmen akan dianalisis secara independen dengan asumsi gambar label depan yang diambil. Begitu barang dikenali, harga setiap barang ditambahkan untuk mendapatkan harga total. Citra hasil menunjukkan posisi barang pada citra dengan informasi harga barang dan total belanja. Sistem ini dapat mengenali barang dalam citra kereta belanja dengan tingkat akurasi 100% terhadap jumlah barang pada kereta belanja sebanyak 2 sampai 5 barang, tingkat akurasi 20% dengan jumlah 6 dan 7 barang, tingkat akurasi 0% dengan jumlah 8 sampai 10 barang. Sistem ini juga dapat mengenali barang tumpang tindih dengan presentase fitur area barang bawah lebih besar dibandingkan barang atas, serta mayoritas sistem hanya bisa mengenali barang dengan bentuk objek datar.
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Yu, Yang, Min Zhang, Guo Hua Zhang, and Jie Niu. "The Airport Runway Foreign Objects Detection Method Research Based on the Algorithm of SIFT." Advanced Materials Research 424-425 (January 2012): 784–88. http://dx.doi.org/10.4028/www.scientific.net/amr.424-425.784.
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Based on the algorithm of Scale invariant feature transform SIFT, informed a method to detection the airport runway foreign objects based on the algorithm of improved SIFT, first roughly extracts object through the image segmentation algorithm, then match the object on it’s SIFT features, ensure it’s features stability, enhance the matching accuracy. Experimental results show that this method can not only handle the problems of tar-get losing evidently, which are induced by objects rotation and translation, but also has nice robustness to the conjunction of multi-targets in the process of object tracking
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El khattabi, Zaynab, Youness Tabii, and Abdelhamid Benkaddour. "Video Shot Boundary Detection using the Scale Invariant Feature Transform and RGB Color Channels." International Journal of Electrical and Computer Engineering (IJECE) 7, no. 5 (2017): 2565. http://dx.doi.org/10.11591/ijece.v7i5.pp2565-2673.
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<p>Segmentation of the video sequence by detecting shot changes is essential for video analysis, indexing and retrieval. In this context, a shot boundary detection algorithm is proposed in this paper based on the scale invariant feature transform (SIFT). The first step of our method consists on a top down search scheme to detect the locations of transitions by comparing the ratio of matched features extracted via SIFT for every RGB channel of video frames. The overview step provides the locations of boundaries. Secondly, a moving average calculation is performed to determine the type of transition. The proposed method can be used for detecting gradual transitions and abrupt changes without requiring any training of the video content in advance. Experiments have been conducted on a multi type video database and show that this algorithm achieves well performances.</p>
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Du, Yu Hong, Chen Wu, Di Zhao, Yun Chang, Xing Li, and Shuo Yang. "SIFT-Based Target Recognition in Robot Soccer." Key Engineering Materials 693 (May 2016): 1419–27. http://dx.doi.org/10.4028/www.scientific.net/kem.693.1419.
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A novel scale-invariant feature transform (SIFT) algorithm is proposed for soccer target recognition application in a robot soccer game. First, the method of generating scale space is given, extreme points are detected. This gives the precise positioning of the extraction step and the SIFT feature points. Based on the gradient and direction of the feature point neighboring pixels, a description of the key points of the vector is generated. Finally, the matching method based on feature vectors is extracted from SIFT feature points and implemented on the image of the football in a soccer game. By employing the proposed SIFT algorithm for football and stadium key feature points extraction and matching, significant increase can be achieved in the robot soccer ability to identify and locate the football.
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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 speckle noise is presented to detect extrema in scale space, and then, interest points are filtered and located. It is also used to assign orientation and generate descriptors of interest points. Simulations and experiments demonstrate that the proposed method can capture features of underwater objects more accurately than existing approaches.
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Li, X. G., C. Ren, T. X. Zhang, Z. L. Zhu, and Z. G. Zhang. "UNMANNED AERIAL VEHICLE IMAGE MATCHING BASED ON IMPROVED RANSAC ALGORITHM AND SURF ALGORITHM." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-3/W10 (February 7, 2020): 67–70. http://dx.doi.org/10.5194/isprs-archives-xlii-3-w10-67-2020.
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Abstract. A UAV image matching method based on RANSAC (Random Sample Consensus) algorithm and SURF (speeded up robust features) algorithm is proposed. The SURF algorithm is integrated with fast operation and good rotation invariance, scale invariance and illumination. The brightness is invariant and the robustness is good. The RANSAC algorithm can effectively eliminate the characteristics of mismatched point pairs. The pre-verification experiment and basic verification experiment are added to the RANSAC algorithm, which improves the rejection and running speed of the algorithm. The experimental results show that compared with the SURF algorithm, SIFT (Scale Invariant Feature Transform) algorithm and ORB (Oriented FAST and Rotated BRIEF) algorithm, the proposed algorithm is superior to other algorithms in terms of matching accuracy and matching speed, and the robustness is higher.
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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 matching demonstrate that the new algorithm improves the significance of the shape of the extracted feature points and keep a better match rate of 96%. At the same time the time complexity is reduced by 27.8%. This algorithm has a certain practicality.
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Liu, Xiao Yu, Yan Piao, and Lei Liu. "The Study of Reduce the Mismatch Based on the SIFT Feature Matching." Advanced Materials Research 580 (October 2012): 378–82. http://dx.doi.org/10.4028/www.scientific.net/amr.580.378.
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The algorithm of SIFT (scale-invariant feature transform) feature matching is an international hotspot in the areas of the keypoints matching and target recognition at the present time. The algorithm is used in the image matching widely because of the good invariance of scale, illumination and space rotation .This paper proposes a new theory to reduce the mismatch—the theory to reduce the mismatch based on the main orientation of keypoints. This theory should firstly compute the grads of the main orientation of a couple of matched keypoints in the two images and the difference between them. Because the difference of the main orientation of matched keypoints should be much larger than the couples which are matched correctly, we can distinguish and reduce the mismatch through setting the proper threshold, and it can improve the accuracy of the SIFT algorithm greatly.
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Rajasekhar, D., T. Jayachandra Prasad, and K. Soundararajan. "An affine view and illumination invariant iterative image matching approach for face recognition." International Journal of Engineering & Technology 7, no. 2.8 (2018): 42. http://dx.doi.org/10.14419/ijet.v7i2.8.10321.
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Feature detection and image matching constitutes two primary tasks in photogrammetric and have multiple applications in a number of fields. One such application is face recognition. The critical nature of this application demands that image matching algorithm used in recognition of features in facial recognition to be robust and fast. The proposed method uses affine transforms to recognize the descriptors and classified by means of Bayes theorem. This paper demonstrates the suitability of the proposed image matching algorithm for use in face recognition appli-cations. Yale facial data set is used in the validation and the results are compared with SIFT (Scale Invariant Feature Transform) based face recognition approach.
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Ajayi, O. G. "PERFORMANCE ANALYSIS OF SELECTED FEATURE DESCRIPTORS USED FOR AUTOMATIC IMAGE REGISTRATION." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLIII-B3-2020 (August 21, 2020): 559–66. http://dx.doi.org/10.5194/isprs-archives-xliii-b3-2020-559-2020.
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Abstract. Automatic detection and extraction of corresponding features is very crucial in the development of an automatic image registration algorithm. Different feature descriptors have been developed and implemented in image registration and other disciplines. These descriptors affect the speed of feature extraction and the measure of extracted conjugate features, which affects the processing speed and overall accuracy of the registration scheme. This article is aimed at reviewing the performance of most-widely implemented feature descriptors in an automatic image registration scheme. Ten (10) descriptors were selected and analysed under seven (7) conditions viz: Invariance to rotation, scale and zoom, their robustness, repeatability, localization and efficiency using UAV acquired images. The analysis shows that though four (4) descriptors performed better than the other Six (6), no single feature descriptor can be affirmed to be the best, as different descriptors perform differently under different conditions. The Modified Harris and Stephen Corner Detector (MHCD) proved to be invariant to scale and zoom while it is excellent in robustness, repeatability, localization and efficiency, but it is variant to rotation. Also, the Scale Invariant feature Transform (SIFT), Speeded Up Robust Features (SURF) and the Maximally Stable Extremal Region (MSER) algorithms proved to be invariant to scale, zoom and rotation, and very good in terms of repeatability, localization and efficiency, though MSER proved to be not as robust as SIFT and SURF. The implication of the findings of this research is that the choice of feature descriptors must be informed by the imaging conditions of the image registration analysts.
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Faradilla Zenda, Siti, Bambang Hidayat, and Suhardjo Suhardjo. "DETEKSI CITRA GRANULOMA MELALUI RADIOGRAF PERIAPIKAL DENGAN METODE SCALE INVARIANT FEATURE TRANSFORM DAN KLASIFIKASI K-NEAREST NEIGHBOR." Jurnal Mnemonic 1, no. 1 (2018): 10–16. http://dx.doi.org/10.36040/mnemonic.v1i1.13.
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Radiograf periapikal merupakan komponen yang menghasilkan gambar radiografi dari gigi secara rinci dan jaringan apeks sekitarnya. Gambaran radiografi sangat membantu dokter gigi menegakkan diagnosis dan rencana perawatan kasus gigi impaksi. Dokter gigi mendiagnosa citra perapikal radiograf menggunakan mata namun karena keterbatasan indra penglihatan manusia bisa menyebabkan interpretasi masing-masing dokter gigi berbeda. Pada penelitian ini dibuatlah metode pengolahan citra yang dapat mendeteksi granuloma dari citra periapikal radiograf. Keluaran sistem dapat memberikan hasil yang dapat membantu dokter gigi dalam membuat keputusan dan meningkatkan diagnosis terhadap radiografi periapikal. Pada penelitian ini pembuatan sistem dilakukan dalam beberapa tahap yaitu pre-processing,ektraksi ciri dan klasifikasi. Metode yang akan digunakan dalam penelitian ini adalah Scale Invariant Feature Transformation (SIFT) sebagai metode ekstrasi ciri. SIFT adalah algoritma untuk mendeteksi dan menjelaskan fitur lokal pada citra. Proses klasifikasi menggunakan metode K-Nearest Neighbor (K-NN). K-NN adalah metode untuk mengklasifikasi obyek berdasarkan contoh latih terdekat. Hasil dari sistem ini adalah mampu untuk mengidentifikasi penyakit granuloma dengan akurasi 85,84% dengan waktu komputasi rata-rata 4,04 detik.Katakunci: granuloma, radiografi periapikal, Scale Invariant Feature Transformation (SIFT), K-Nearest Neighbor (K-NN).
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Yang, Huai Ming, and Jin Guang Sun. "An Improved Face Recognition Algorithm Based on SIFT and LBP." Applied Mechanics and Materials 427-429 (September 2013): 1999–2004. http://dx.doi.org/10.4028/www.scientific.net/amm.427-429.1999.
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A new face image feature extraction and recognition algorithm based on Scale Invariant Feature Transform (SIFT) and Local Linary Patterns (LBP) is proposed in this paper. Firstly, a set of keypoints are extracted from images by using the SIFT algorithm; Secondly, each keypoint is described by LBP patterns; Finally, a combination of the global and local similarity is adopted to calculate the matching results for face images. Calculation results show that the algorithm can reduce the matching dimension of feature points, improve the recognition rate and perspective; it has nice robustness against the interferences such as rotation, lighting and expression.
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Zhou, Shang Bo, and Kai Kang. "An Object Tracking Method Based on the Coordinate-Locating of SIFT Algorithm." Advanced Materials Research 268-270 (July 2011): 2178–84. http://dx.doi.org/10.4028/www.scientific.net/amr.268-270.2178.
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The SIFT (scale invariant feature transform) algorithm has been successfully used in the image matching field. In this paper, a simplified SIFT algorithm is designed. The number of layers in the Gaussian pyramid is reduced. When it is comparing the keypoints, it uses an outspreading method. The new method can reduce the comparison time and matching time. Although the new algorithm (C-SIFT algorithm) has less matching accuracy than the SIFT algorithm, it adopts a distortion detection method to abandon the wrong matching. Then it uses the coordinate displacement to determine the tracking position. Experimental results show that C-SIFT algorithm can perform steadily and timely.
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Chen, Yong, Lei Shang, and Eric Hu. "Robust Image Matching Algorithm Using SIFT on Multiple Layered Strategies." Mathematical Problems in Engineering 2013 (2013): 1–12. http://dx.doi.org/10.1155/2013/452604.
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As for the unsatisfactory accuracy caused by SIFT (scale-invariant feature transform) in complicated image matching, a novel matching method on multiple layered strategies is proposed in this paper. Firstly, the coarse data sets are filtered by Euclidean distance. Next, geometric feature consistency constraint is adopted to refine the corresponding feature points, discarding the points with uncoordinated slope values. Thirdly, scale and orientation clustering constraint method is proposed to precisely choose the matching points. The scale and orientation differences are employed as the elements ofk-means clustering in the method. Thus, two sets of feature points and the refined data set are obtained. Finally, 3 * delta rule of the refined data set is used to search all the remaining points. Our multiple layered strategies make full use of feature constraint rules to improve the matching accuracy of SIFT algorithm. The proposed matching method is compared to the traditional SIFT descriptor in various tests. The experimental results show that the proposed method outperforms the traditional SIFT algorithm with respect to correction ratio and repeatability.
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Al Caruban, Rosidin, Bambang Sugiantoro, and Yudi Prayudi. "ANALISIS PENDETEKSI KECOCOKAN OBJEK PADA CITRA DIGITAL DENGAN METODE ALGORITMA SIFT DAN HISTOGRAM COLOR RGB." Cyber Security dan Forensik Digital 1, no. 1 (2018): 20–27. http://dx.doi.org/10.14421/csecurity.2018.1.1.1235.
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Through using tools of image processing on digital images just like gimp and adobe photoshop applications, an image on digital images can be a source of information for anyone who observes it. On one hand, those applications can easily change or manipulate the authenticity of the image. On the other hand, they can be misused to undermine the credibility of the authenticity of the image in various aspects. Thus, they can be considered as a crime. The implementation of the SIFT Algorithm (Scale Invariant feature transform) and RGB color histogram in Matlab can detect object fitness in digital images and perform accurate test. This study discusses the implementation of getting object fitness on digital image that has been manipulated by SIFT Algorithm method on the Matlab source. It is done by comparing the original image with the manipulated one. The object fitness in digital images can be obtained from a number of key points and other additional parameters through comparing number of pixels on the analyzed image and on the changed histogram in RGB color on each analyzed image. The digital image forensics which is known as one of the scientific methods commonly used in researches is aimed to obtain evidences or facts in determining the authenticity of the image on digital images. The use of the SIFT algorithm is chosen as an extraction method because it is invariant to scale, rotation, translation, and illumination changes. SIFT is used to obtain characteristics of the pattern of the gained key point. The tested result of the SIFT Algorithm method (Scale Invariant feature transform) is expected to produce a better image analysis.
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Liu, Gang, Sen Liu, Liuke Liang, Zhonghua Liu, and Jianwei Ma. "Scene Matching for Infrared and Visible Images with Compressive Sensing SIFT Feature Representation in Bandelet Domain." International Journal of Pattern Recognition and Artificial Intelligence 34, no. 11 (2020): 2054029. http://dx.doi.org/10.1142/s0218001420540294.
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Aimed at scene matching problem for taking infrared image as the actual data and the visible image as the referenced data, a multi-resolution matching algorithm which fuses compressive sensing Scale Invariant Feature Transform (SIFT) feature is presented based on Bandelet transform. Two kinds of images are separately transformed into Bandelet domain to compress the feature search space of scene matching based on the best sparse representation of natural images by Bandelet transform. On the basis of adaptive Bayes threshold denoising for infrared image, the concept of sparse feature representation of compressive sensing theory is introduced into SIFT algorithm. For low-frequency image in Bandelet domain, high-dimensional SIFT key point feature description vector is projected on compressive sensing random measurement matrix to achieve dimensionality reduction. Then, the improved Genetic Algorithm (GA) to overcome premature phenomena is used as the search strategy, and the L1 distance measure of SIFT feature vectors of compressive sensing for two kinds of images is applied to the search similarity criterion to match low-frequency image of high scale in Bandelet domain. The matching result is used as the guidance of the matching process for low-frequency image of low scale, and the matching result of full-resolution image is obtained iteratively. Experimental results show that the proposed method has not only high matching accuracy and fast matching speed, but also better robustness in comparison with some classic matching algorithms, which can resist the geometric distortion of rotation for actual image.
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Cheng, Yujie, Hang Yuan, Hongmei Liu, and Chen Lu. "Fault diagnosis for rolling bearing based on SIFT-KPCA and SVM." Engineering Computations 34, no. 1 (2017): 53–65. http://dx.doi.org/10.1108/ec-01-2016-0005.
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Purpose The purpose of this paper is to propose a fault diagnosis method for rolling bearings, in which the fault feature extraction is realized in a two-dimensional domain using scale invariant feature transform (SIFT) algorithm. This method is different from those methods extracting fault feature directly from the traditional one-dimensional domain. Design/methodology/approach The vibration signal of rolling bearings is first transformed into a two-dimensional image. Then, the SIFT algorithm is applied to the image to extract the scale invariant feature vector which is highly distinctive and insensitive to noises and working condition variation. As the extracted feature vector is high-dimensional, kernel principal component analysis (KPCA) algorithm is utilized to reduce the dimension of the feature vector, and singular value decomposition technique is used to extract the singular values of the reduced feature vector. Finally, these singular values are introduced into a support vector machine (SVM) classifier to realize fault classification. Findings The experiment results show a high fault classification accuracy based on the proposed method. Originality/value The proposed approach for rolling bearing fault diagnosis based on SIFT-KPCA and SVM is highly effective in the experiment. The practical value in engineering application of this method can be researched in the future.
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Shi, Shuo, Ming Yu, Cui Hong Xue, and Ying Zhou. "The Application of SIFT Algorithm in Blind Road Environmental Image Matching." Applied Mechanics and Materials 155-156 (February 2012): 1137–41. http://dx.doi.org/10.4028/www.scientific.net/amm.155-156.1137.
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Image Matching is a key technology in the intelligent navigation system for the blind, which is based on the computer video. The images of moving blind people, collected at real time, have variety of changes in light, rotation, scaling, etc. Against this feature, we propose a practical matching algorithm, which is based on the SIFT (Scale Invariant, Feature Transform). That is the image matching algorithm. We focus on the algorithm of the SIFT feature extraction and matching, and obtain the feature points of the image through feature extraction algorithm. We verify the effect of the algorithm by selecting practical images with rotation, scaling and different light. The result is that this method can get better matches for the blind road environmental image.
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Lee, Dong-Hoon, Do-Wan Lee, and Bong-Soo Han. "Possibility Study of Scale Invariant Feature Transform (SIFT) Algorithm Application to Spine Magnetic Resonance Imaging." PLOS ONE 11, no. 4 (2016): e0153043. http://dx.doi.org/10.1371/journal.pone.0153043.
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Jiang, Xiao Cun, Xiao Liu, Tao Tang, Xiao Hu Fan, and Xiao Cui. "A Comparison of Two Typical Local Feature Matching Algorithm: SIFT and MSER." Applied Mechanics and Materials 687-691 (November 2014): 4119–22. http://dx.doi.org/10.4028/www.scientific.net/amm.687-691.4119.
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Scale invariant feature transform matching algorithm and Maximally Stable Extremal Regions matching algorithm have been widely used because of their good performance. The two local feature matching algorithms were compared through numbers of experiments in this paper. The experiment results showed that SIFT is good at dealing with the image distortion from shooting distance difference and small shooting viewpoint deviation; MSER is good at handling the complicated affine distortion from big shooting viewpoint deviation. From the aspect of scene types, the performance of SIFT is good both to structure images and texture images. MSER is suitable for the matching of structure images, but not so successful to that of texture images.
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Zhou, Bin, and Min Chen. "MRI Images under the Optimized Registration Algorithm for Primary Open Angle Glaucoma Visual Path Damage." Scientific Programming 2021 (July 5, 2021): 1–9. http://dx.doi.org/10.1155/2021/4921276.
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To explore the impact of different image registration algorithms on the diagnosis of visual path damage in patients with primary open angle glaucoma (POAG), 60 cases of suspected POAG patients were selected as the research objects. Shape-preserving scale invariant feature transform (SP-SIFT) algorithm, scale invariant feature transform (SIFT) algorithm, and Kanade-Lucas-Tomasi (KLT) algorithm were compared and applied to MRI images of 60 POAG patients. It was found that the SP-SIFT algorithm converged after 33 iterations, which had a higher registration speed than the SIFT algorithm and the KLT algorithm. The mean errors of the SP-SIFT algorithm in the rotation angle, X-direction translation, and Y-direction translation were 2.11%, 4.56%, and 4.31%, respectively. Those of the SIFT algorithm were 5.55%, 9.98%, and 7.01%, respectively. Those of the KLT algorithm were 7.45%, 11.31%, and 8.56%, respectively, and the difference among algorithms was significant ( P < 0.05 ). The diagnostic sensitivity and accuracy of the SP-SIFT algorithm for POAG were 96.15% and 94.34%, respectively. Those of the SIFT algorithm were 94.68% and 90.74%, respectively. Those of the KLT algorithm were 94.21% and 90.57%, respectively, and the three algorithms had significant differences ( P < 0.05 ). The results of MRI images based on the SP-SIFT algorithm showed that the average thickness of the cortex of the patient’s left talar sulcus, right talar sulcus, left middle temporal gyrus, and left fusiform gyrus were 2.49 ± 0.15 mm, 2.62 ± 0.13 mm, 3.00 ± 0.10 mm, and 2.99 ± 0.17 mm, respectively. Those of the SIFT algorithm were 2.51 ± 0.17 mm, 2.69 ± 0.12 mm, 3.11 ± 0.13 mm, and 3.09 ± 0.14 mm, respectively. Those of the KLT algorithm were 2.35 ± 0.12 mm, 2.52 ± 0.16 mm, 2.77 ± 0.11 mm, and 2.87 ± 0.17 mm, respectively, and the three algorithms had significant differences ( P < 0.05 ). In summary, the SP-SIFT algorithm was ideal for POAG visual pathway diagnosis and was of great adoption potential in clinical diagnosis.
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Zhong, Yang Jun, and Qian Cai. "A Novel Registration Approach for Mammograms Based on SIFT and Graph Transformation." Applied Mechanics and Materials 157-158 (February 2012): 1313–19. http://dx.doi.org/10.4028/www.scientific.net/amm.157-158.1313.
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Mammogram registration is an important step in the processing of automatic detection of breast cancer. It provides aid to better visualization correspondence on temporal pairs of mammograms. This paper presents a novel algorithm based on SIFT feature and Graph Transformation methods for mammogram registration. First, features are extracted from the mammogram images by scale invariant feature transform (SIFT) method. Second, we use graph transformation matching (GTM) approach to obtain more accurate image information. At last, we registered a pair of mammograms using Thin-Plate spline (TPS) interpolation based on corresponding points on the two breasts, and acquire the mammogram registration image. Performance of the proposed algorithm is evaluated by three criterions. The experimental results show that our method is accurate and closely to the source images.
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Wang, Ende, Jinlei Jiao, Jingchao Yang, Dongyi Liang, and Jiandong Tian. "Tri-SIFT: A Triangulation-Based Detection and Matching Algorithm for Fish-Eye Images." Information 9, no. 12 (2018): 299. http://dx.doi.org/10.3390/info9120299.
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Keypoint matching is of fundamental importance in computer vision applications. Fish-eye lenses are convenient in such applications that involve a very wide angle of view. However, their use has been limited by the lack of an effective matching algorithm. The Scale Invariant Feature Transform (SIFT) algorithm is an important technique in computer vision to detect and describe local features in images. Thus, we present a Tri-SIFT algorithm, which has a set of modifications to the SIFT algorithm that improve the descriptor accuracy and matching performance for fish-eye images, while preserving its original robustness to scale and rotation. After the keypoint detection of the SIFT algorithm is completed, the points in and around the keypoints are back-projected to a unit sphere following a fish-eye camera model. To simplify the calculation in which the image is on the sphere, the form of descriptor is based on the modification of the Gradient Location and Orientation Histogram (GLOH). In addition, to improve the invariance to the scale and the rotation in fish-eye images, the gradient magnitudes are replaced by the area of the surface, and the orientation is calculated on the sphere. Extensive experiments demonstrate that the performance of our modified algorithms outweigh that of SIFT and other related algorithms for fish-eye images.
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El Mobacher, Ayman, Nicholas Mitri, and Mariette Awad. "Entropy-Based and Weighted Selective SIFT Clustering as an Energy Aware Framework for Supervised Visual Recognition of Man-Made Structures." Mathematical Problems in Engineering 2013 (2013): 1–9. http://dx.doi.org/10.1155/2013/730143.
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Using local invariant features has been proven by published literature to be powerful for image processing and pattern recognition tasks. However, in energy aware environments, these invariant features would not scale easily because of their computational requirements. Motivated to find an efficient building recognition algorithm based on scale invariant feature transform (SIFT) keypoints, we present in this paper uSee, a supervised learning framework which exploits the symmetrical and repetitive structural patterns in buildings to identify subsets of relevant clusters formed by these keypoints. Once an image is captured by a smart phone, uSee preprocesses it using variations in gradient angle- and entropy-based measures before extracting the building signature and comparing its representative SIFT keypoints against a repository of building images. Experimental results on 2 different databases confirm the effectiveness of uSee in delivering, at a greatly reduced computational cost, the high matching scores for building recognition that local descriptors can achieve. With only 14.3% of image SIFT keypoints, uSee exceeded prior literature results by achieving an accuracy of 99.1% on the Zurich Building Database with no manual rotation; thus saving significantly on the computational requirements of the task at hand.
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Jang, Dong-Hwa, Kyeong-Seok Kwon, Jung-Kon Kim, Ka-Young Yang, and Jong-Bok Kim. "Dog Identification Method Based on Muzzle Pattern Image." Applied Sciences 10, no. 24 (2020): 8994. http://dx.doi.org/10.3390/app10248994.
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Currently, invasive and external radio frequency identification (RFID) devices and pet tags are widely used for dog identification. However, social problems such as abandoning and losing dogs are constantly increasing. A more effective alternative to the existing identification method is required and the biometrics can be the alternative. This paper proposes an effective dog muzzle recognition method to identify individual dogs. The proposed method consists of preprocessing, feature extraction, matching, and postprocessing. For preprocessing, proposed resize and histogram equalization are used. For feature extraction algorithm, Scale Invariant Feature Transform (SIFT), Speeded Up Robust Features (SURF), Binary Robust Invariant Scaling Keypoints (BRISK) and Oriented FAST, and Rotated BRIEF (ORB) are applied and compared. For matching, Fast Library for Approximate Nearest Neighbors (FLANN) is used for SIFT and SURF, and hamming distance are used for BRISK and ORB. For postprocessing, two techniques to reduce incorrect matches are proposed. The proposed method was evaluated with 55 dog muzzle pattern images acquired from 11 dogs and 990 images augmented by the image deformation (i.e., angle, illumination, noise, affine transform). The best Equal Error Rate (EER) of the proposed method was 0.35%, and ORB was the most appropriate for the dog muzzle pattern recognition.
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Li, Shu Rong, Yuan Yuan Huang, and Zuo Jin Hu. "A Fast Feature Matching Algorithm Based on Multi Scale Spatial Segmentation Technology." Applied Mechanics and Materials 490-491 (January 2014): 1217–20. http://dx.doi.org/10.4028/www.scientific.net/amm.490-491.1217.
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SIFT (Scale invariant feature transform) and correlative algorithms are now widely used in content based image retrieval technology. They compute distance and use neighbor algorithm to look for the optimal matching couples. The disadvantage of such way is high complexity, especially when huge amount of images need to be retrieved or recognized. To solve this problem, a new matching way based on feature space division under multi-scale is proposed. The algorithm will divide the feature space under multiple scales, so that those feature points which are located in somewhere can use a code to represent, and finally realize the matching through the code. Without calculating distance, the algorithm complexity is greatly reduced. Experiments show that, the algorithm keeps the matching accuracy and greatly enhance the efficiency of the matching at the same time.
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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 some computers. Our purpose here is to present an interest point detector and a descriptor suitable for FPGA implementation. Results show that a detector using gradient variance inspection performs faster than SIFT or speeded-up robust features (SURF), and is more robust against illumination changes than any other method compared in this study. A descriptor with a hierarchical gradient structure has a simpler algorithm than SIFT and SURF descriptors, and the result of stereo matching achieves better performance than SIFT or SURF.
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Wang, Xiongxiong, Li Xu, Xuena Cui, Hefei Zhu, and Min Zhang. "Scale-Invariant Feature Transform (SIFT) Algorithm-Based Detection of Cardiac Insufficiency in Sepsis Patients with Echocardiography." Scientific Programming 2022 (February 22, 2022): 1–10. http://dx.doi.org/10.1155/2022/2260500.
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This research aimed to explore the application of cardiac ultrasound imaging (CUI) in the examination and diagnosis of sepsis patients with cardiac insufficiency under a speckle tracking algorithm (STA). Scale-invariant feature transform (SIFT) algorithm was introduced to process images of CUI through STA under feature points of cardiac ultrasonic images. 90 patients with sepsis who were admitted to the hospital were selected and randomly divided into a sound cardiac function group (n = 62) (group A) and a cardiac insufficient group (n = 28) (group B) under whether they had cardiac insufficiency, and 20 healthy people were selected as a control group. Sepsis patients were examined on the seventh day after diagnosis including laboratory-related indicators, echocardiography, and echocardiographic data. The results showed that there were differences in cardiac ultrasound, cardiac marker, and laboratory examinations of subjects in the three groups ( P < 0.05). Creatine kinase isoenzyme-MB (CK-MB), glycogen phosphorylase isoenzyme BB (GPBB), and heart-type fatty acid-binding protein (H-FABP) of subjects in the three groups showed statistically marked differences ( P < 0.05). The other results indicated tricuspid late diastolic blood flow velocity (A′) value was greater than stroke volume (SV) and isovolumetric relaxation time (IVRT) value, followed by left ventricular ejection fraction (EF) and cardiac output per minute output (CO), and EF and CO values were over peak velocity of right ventricular free wall tricuspid valve systolic movement (SM) and left ventricular fractional shortening (FS) value, and left ventricular end-diastolic volume (EDV) value came next. The analysis of CUI based on STA could help doctors to judge sepsis patients with cardiac function. In addition, sepsis patients with sound cardiac function and sepsis patients with cardiac insufficiency could be distinguished by CUI, which had certain guiding significance for clinical diagnosis.
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Chau, Khanh Ngan, and Nghi Thanh Doan. "DENSE SIFT FEATURE AND LOCAL NAIVE BAYES NEAREST NEIGHBOR FOR FACE RECOGNITION." Scientific Journal of Tra Vinh University 1, no. 28 (2017): 56–63. http://dx.doi.org/10.35382/18594816.1.28.2017.46.
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Human face recognition is a technology which is widely used in life. There have been much effort on developing face recognition algorithms. In this paper, we present a new methodology that combines Haar Like Features - Cascade of Boosted Classifiers, Dense Scale-Invariant Feature Transform (DSIFT), Local Naive Bayes Nearest Neighbor (LNBNN) algorithm for the recognition of human face. We use Haar Like Features and the combination of AdaBoost algorithm and Cascade stratified model to detect and extract the face image, the DSIFT descriptors of the image are computed only for the aligned and cropped face image.Then, we apply the LNBNN algorithms for object recognition. Numerical testing on several benchmark datasets using our proposed method for facerecognition gives the better results than other methods. The accuracies obtained by LNBNN method is 99.74 %.
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Tripathi, Akash, T. V. Ajay Kumar, Tarun Kanth Dhansetty, and J. Selva Kumar. "Real Time Object Detection using CNN." International Journal of Engineering & Technology 7, no. 2.24 (2018): 33. http://dx.doi.org/10.14419/ijet.v7i2.24.11994.
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Achieving new heights in object detection and image classification was made possible because of Convolution Neural Network(CNN). However, compared to image classification the object detection tasks are more difficult to analyze, more energy consuming and computation intensive. To overcome these challenges, a novel approach is developed for real time object detection applications to improve the accuracy and energy efficiency of the detection process. This is achieved by integrating the Convolutional Neural Networks (CNN) with the Scale Invariant Feature Transform (SIFT) algorithm. Here, we obtain high accuracy output with small sample data to train the model by integrating the CNN and SIFT features. The proposed detection model is a cluster of multiple deep convolutional neural networks and hybrid CNN-SIFT algorithm. The reason to use the SIFT featureis to amplify the model‟s capacity to detect small data or features as the SIFT requires small datasets to detect objects. Our simulation results show better performance in accuracy when compared with the conventional CNN method. As the resources like RAM, graphic card, ROM, etc. are limited we propose a pipelined implementation on an aggregate Central Processing Unit(CPU) and Graphical Processing Unit(GPU) platform.
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Li, Xin Ke, Chao Gao, Yong Cai Guo, Yan Hua Shao, and Fu Liang He. "Image Mosaic Using SIFT for Bridge Cable Surface Flaw Detection." Applied Mechanics and Materials 333-335 (July 2013): 1654–58. http://dx.doi.org/10.4028/www.scientific.net/amm.333-335.1654.
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A machine vision system is developed to detect the cable surface damage of cable-stayed bridge. In the system, four cameras are employed to acquire images around the cable surface. So the same one defect may be split into several images. Image mosaic had to be done to obtain a complete defect image for further process. The feature of cable surface image is simple and its illumination is heterogeneous. So the Scale Invariant Feature Transform (SIFT) feature matching algorithm is suitable for the image mosaic. Firstly, cable surface images should be preprocessed. Secondly, the SIFT algorithm achieves the detection, extraction, description and matching of feature points for defect images. Finally, image fusion is implemented to acquire the integrated image and a complete defect will be showed in the image. Experimental results show that using SIFT for the cable defect image mosaic has good effect on improving the detection accuracy and integrity for cable surface defects.
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Tong, Guofeng, Xue Chen, and Ning Ye. "A Spherical Model Based Keypoint Descriptor and Matching Algorithm for Omnidirectional Images." Advances in Mechanical Engineering 6 (January 1, 2014): 154376. http://dx.doi.org/10.1155/2014/154376.
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Omnidirectional images generally have nonlinear distortion in radial direction. Unfortunately, traditional algorithms such as scale-invariant feature transform (SIFT) and Descriptor-Nets (D-Nets) do not work well in matching omnidirectional images just because they are incapable of dealing with the distortion. In order to solve this problem, a new voting algorithm is proposed based on the spherical model and the D-Nets algorithm. Because the spherical-based keypoint descriptor contains the distortion information of omnidirectional images, the proposed matching algorithm is invariant to distortion. Keypoint matching experiments are performed on three pairs of omnidirectional images, and comparison is made among the proposed algorithm, the SIFT and the D-Nets. The result shows that the proposed algorithm is more robust and more precise than the SIFT, and the D-Nets in matching omnidirectional images. Comparing with the SIFT and the D-Nets, the proposed algorithm has two main advantages: (a) there are more real matching keypoints; (b) the coverage range of the matching keypoints is wider, including the seriously distorted areas.
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Lv, Feng, Chunmei ZHANG та Changwei Lv. "Image recognition of individual cow based on SIFT in Lαβ color space". MATEC Web of Conferences 176 (2018): 01023. http://dx.doi.org/10.1051/matecconf/201817601023.
Full textAbstract:
Using image recognition technology to identify individual dairy cattle with her biological features shows strong stability. This kind of non-contact, high precision and low cost individual recognition methods based on image processing are more and more popular recently to replace the electronic tag and ear mark which can hurt the cattle’s psychology and physical health and can affect cattle’s behavior. By comparing the various color space transformations, he proposed a scale-invariant feature transform algorithm based on the Luminace of Lαβ color space. With this algorithm, a biological features recognition and management system of Holstein cow has been developed. The identification accuracy is higher than 98%, which is the best result than all the similar reports for cows’ identification.