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Academic literature on the topic 'Algorytm SIFT (Scale Invariant Feature Transform)'
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Journal articles on the topic "Algorytm SIFT (Scale Invariant Feature Transform)"
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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