Journal articles: 'Scale-Invariant-Feature-Transform (SIFT)'

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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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Cheung, W., and G. Hamarneh. "$n$-SIFT: $n$-Dimensional Scale Invariant Feature Transform." IEEE Transactions on Image Processing 18, no. 9 (September 2009): 2012–21. http://dx.doi.org/10.1109/tip.2009.2024578.

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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 (January 20, 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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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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A, Kalaiselvi, Sangeetha V, and Kasiselvanathan M. "Palm Pattern Recognition using Scale Invariant Feature Transform (SIFT)." International Journal of Intelligence and Sustainable Computing 1, no. 1 (2018): 1. http://dx.doi.org/10.1504/ijisc.2018.10023048.

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Azeem, A., M. Sharif, J. H. Shah, and M. Raza. "Hexagonal scale invariant feature transform (H-SIFT) for facial feature extraction." Journal of Applied Research and Technology 13, no. 3 (June 2015): 402–8. http://dx.doi.org/10.1016/j.jart.2015.07.006.

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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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Yuehua Tao, Youming Xia, Tianwei Xu, and Xiaoxiao Chi. "Research Progress of the Scale Invariant Feature Transform (SIFT) Descriptors." Journal of Convergence Information Technology 5, no. 1 (February 28, 2010): 116–21. http://dx.doi.org/10.4156/jcit.vol5.issue1.13.

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Wulandari, Irma. "FUSI CITRA DENGAN SCALE INVARIANT FEATURE TRANSFORM (SIFT) SEBAGAI REGISTRASI CITRA." Jurnal Ilmiah Informatika Komputer 25, no. 2 (2020): 137–46. http://dx.doi.org/10.35760/ik.2020.v25i2.2870.

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Fusi citra adalah proses menggabungkan dua atau lebih citra ke dalam satu citra, dengan mempertahankan fitur penting dari masing-masing gambar. Fusi citra adalah salah satu cara untuk menyelesaikan masalah gambar yang tidak fokus hasil dari penggunaan kamera non-profesional. Fusi citra juga dapat digunakan dalam penginderaan jauh, pengamatan, dan aplikasi medis. Dalam penelitian ini, diusulkan teknik fusi citra baru dengan menggunakan SIFT (Scale Invariant Feature Transform) sebagai registrasi citra. Prosedur fusi dilakukan dengan mencocokkan fitur gambar SIFT menggunakan RANSAC dan kemudian menggabungkan dua citra dengan aturan rata-rata piksel. Langkah terakhir membandingkan hasil fusi citra menggunakan QABF, intensitas rata-rata piksel dan standard deviasi. Hasil eksperimental menunjukkan bahwa metode yang diusulkan mengungguli teknik fusi konvensional, terutama untuk citra yang mengalami translasi atau rotasi.

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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 (December 28, 2017): 178. http://dx.doi.org/10.36722/sst.v3i4.232.

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Abstrak - 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.Kata Kunci – Biometric, Footprint, SURF, SIFT, K- NNAbstract - 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.Keywords – Biometric, Footprint, SURF, SIFT, K- NN

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LI, QIAOLIANG, HUISHENG ZHANG, and TIANFU WANG. "SCALE INVARIANT FEATURE MATCHING USING ROTATION-INVARIANT DISTANCE FOR REMOTE SENSING IMAGE REGISTRATION." International Journal of Pattern Recognition and Artificial Intelligence 27, no. 02 (March 2013): 1354004. http://dx.doi.org/10.1142/s0218001413540049.

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Scale invariant feature transform (SIFT) has been widely used in image matching. But when SIFT is introduced in the registration of remote sensing images, the keypoint pairs which are expected to be matched are often assigned two different value of main orientation owing to the significant difference in the image intensity between remote sensing image pairs, and therefore a lot of incorrect matches of keypoints will appear. This paper presents a method using rotation-invariant distance instead of Euclid distance to match the scale invariant feature vectors associated with the keypoints. In the proposed method, the feature vectors are reorganized into feature matrices, and fast Fourier transform (FFT) is introduced to compute the rotation-invariant distance between the matrices. Much more correct matches are obtained by the proposed method since the rotation-invariant distance is independent of the main orientation of the keypoints. Experimental results indicate that the proposed method improves the match performance compared to other state-of-art methods in terms of correct match rate and aligning accuracy.

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Partheeba, S., and N. Radha. "Fingerprint bio-Crypto key generation using Scale Invariant Feature Transform (SIFT)." International Journal of Computer Applications 153, no. 8 (November 17, 2016): 35–41. http://dx.doi.org/10.5120/ijca2016912129.

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Journal, Baghdad Science. "Scale-Invariant Feature Transform Algorithm with Fast Approximate Nearest Neighbor." Baghdad Science Journal 14, no. 3 (September 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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Umale, Prajakta, Aboli Patil, Chanchal Sahani, Anisha Gedam, and Kajal Kawale. "PLANER OBJECT DETECTION USING SURF AND SIFT METHOD." International Journal of Engineering Applied Sciences and Technology 6, no. 11 (March 1, 2022): 36–39. http://dx.doi.org/10.33564/ijeast.2022.v06i11.008.

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Object Detection refers to the capability of computer and software to locate objects in an image/scene and identify each object. Object detection is a computer vision technique works to identify and locate objects within an image or video. In this study, we compare and analyze Scale-invariant feature transform (SIFT) and speeded up robust features (SURF) and propose a various geometric transformation. To increase the accuracy, the proposed system firstly performs the separation of the image by reducing the pixel size, using the Scale-invariant feature transform (SIFT). Then the key points are picked around feature description regions. After that we perform one more geometric transformation which is rotation, and is used to improve visual appearance of image. By using this, we perform Speeded Up Robust Features (SURF) feature which highlights the high pixel value of the image. After that we compare two different images and by comparing all features of that object from image, the desired object detected in a scene.

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Gao, Junchai, and Zhen Sun. "An Improved ASIFT Image Feature Matching Algorithm Based on POS Information." Sensors 22, no. 20 (October 12, 2022): 7749. http://dx.doi.org/10.3390/s22207749.

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The affine scale-invariant feature transform (ASIFT) algorithm is a feature extraction algorithm with affinity and scale invariance, which is suitable for image feature matching using unmanned aerial vehicles (UAVs). However, there are many problems in the matching process, such as the low efficiency and mismatching. In order to improve the matching efficiency, this algorithm firstly simulates image distortion based on the position and orientation system (POS) information from real-time UAV measurements to reduce the number of simulated images. Then, the scale-invariant feature transform (SIFT) algorithm is used for feature point detection, and the extracted feature points are combined with the binary robust invariant scalable keypoints (BRISK) descriptor to generate the binary feature descriptor, which is matched using the Hamming distance. Finally, in order to improve the matching accuracy of the UAV images, based on the random sample consensus (RANSAC) a false matching eliminated algorithm is proposed. Through four groups of experiments, the proposed algorithm is compared with the SIFT and ASIFT. The results show that the algorithm can optimize the matching effect and improve the matching speed.

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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 (May 19, 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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Alkaff, Muhammad, Husnul Khatimi, Nur Lathifah, and Yuslena Sari. "Sasirangan Motifs Classification using Scale- Invariant Feature Transform (SIFT) and Support Vector Machine (SVM)." MATEC Web of Conferences 280 (2019): 05023. http://dx.doi.org/10.1051/matecconf/201928005023.

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Sasirangan is one of the traditional cloth from Indonesia. Specifically, it comes from South Borneo. It has many variations of motifs with a different meaning for each pattern. This paper proposes a prototype of Sasirangan motifs classification using four (4) type of Sasirangan motifs namely Hiris Gagatas, Gigi Haruan, Kulat Kurikit, and Hiris Pudak. We used primary data of Sasirangan images collected from Kampung Sasirangan, Banjarmasin, South Kalimantan. After that, the images are processed using Scale-Invariant Feature Transform (SIFT) to extract its features. Furthermore, the extracted features vectors obtained is classified using the Support Vector Machine (SVM). The result shows that the Scale- Invariant Feature Transform (SIFT) feature extraction with Support Vector Machine (SVM) classification able to classify Sasirangan motifs with an overall accuracy of 95%.

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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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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, 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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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 (April 11, 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, 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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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 (July 8, 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 high-contrast detection is simplified by moderately changing a threshold value, which also benefits the reduction of the resulting hardware in realization. The proposed enhanced-SIFT not only accelerates the operational speed but also reduces the hardware cost. The experiment results show that the proposed enhanced-SIFT reaches a frame rate of 205 fps for 640 × 480 images. Integrated with two enhanced-SIFT, a finite-area parallel checking is also proposed without the aid of external memory to improve the efficiency of feature matching. The resulting frame rate by the proposed stereo vision matching can be as high as 181 fps with good matching accuracy as demonstrated in the experimental results.

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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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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 (November 22, 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 barcode. 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 barcode 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 Scale Invariant Feature Transform (SIFT) serta metode RANSAC digunakan untuk menghasilkan homography terbaik untuk memetakan kotak pembatas dari database 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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Hicham, Benradi, Chater Ahmed, and Lasfar Abdelali. "Face recognition method combining SVM machine learning and scale invariant feature transform." E3S Web of Conferences 351 (2022): 01033. http://dx.doi.org/10.1051/e3sconf/202235101033.

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Facial recognition is a method to identify an individual from his image. It has attracted the intention of a large number of researchers in the field of computer vision in recent years due to its wide scope of application in several areas (health, security, robotics, biometrics...). The operation of this technology, so much in demand in today's market, is based on the extraction of features from an input image using techniques such as SIFT, SURF, LBP... and comparing them with others from another image to confirm or assert the identity of an individual. In this paper, we have performed a comparative study of a machine learning-based approach using several classification methods, applied on two face databases, which will be divided into two groups. The first one is the Train database used for the training stage of our model and the second one is the Test database, which will be used in the test phase of the model. The results of this comparison showed that the SIFT technique merged with the SVM classifier outperforms the other classifiers in terms of identification accuracy rate.

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Halawa, Elisabet Noferia. "Penerapan Metode Eigenface Untuk Pencocokan Wajah Dengan Menggunakan Scale Invariant Feature Transform." Jurnal Kajian Ilmiah Teknologi Informasi dan Komputer 2, no. 1 (January 17, 2024): 43–49. http://dx.doi.org/10.62866/jutik.v2i1.115.

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The problem in this study is matching face objects to recognize a person's identity that can be used in the field of data security, so to secure a data is encoded by using a facial image so that data access is maintained from parties who do not need to access it. One solution to the problems described above is to apply a method that can recognize the sutu object. One method that is often used is Invariant Feature Transform (SIFT). Scale Invariant Feature Transform (SIFT) is a method for detecting and describing local features in an image. The author did not find the same Title of the previous study, but the author found the previous research using the same method among others. From the results obtained in table 4.1, this method is able to identify faces by comparing keypoint values, where as in step 7 the keypoint obtained against the previous sample image only has 2 keypoints produced, where the sample face image has 64 keypoints while the results obtained on the test image have 66 keypoints.

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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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Alviando, Muhammad Restu, Muhammad Ezar Al Rivan, and Yoannita Yoannita. "KLASIFIKASI AMERICAN SIGN LANGUAGE MENGGUNAKAN FITUR SCALE INVARIANT FEATURE TRANSFORM DAN JARINGAN SARAF TIRUAN." Jurnal Algoritme 1, no. 1 (October 10, 2020): 1–11. http://dx.doi.org/10.35957/algoritme.v1i1.403.

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American Sign Language (ASL) is a sign language in the world. This study uses the neural network method as a classification and the scale invariant feature transform (SIFT) as feature extraction. Training data and test data for ASL images were extracted using the SIFT feature, then ANN training was conducted using 17 training functions with 2 hidden layers. There are architecture used [250-5-10-24], [250-5-15-24] and [250-15-15-24] so there are 3 different ANN architectures. Each architecture is performed 3 times so that there are 9 experiments (3 x 3 trials run the program). Determination of the number of neurons concluded by the training function is selected by the best test results on the test data. Based on the training function and the extraction of SIFT features as input values in the neural network it can be concluded that from 17 training functions, trainb with neuron architecture [250-5-10-24] becomes the best training function producing an accuracy value of 95%, precision of 15 % and recall 5%.

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Retissin, Aditya, and Mohammed Jasim J S. "Performance Analysis of SIFT/SURF Algorithms in Neural Networks for Optimized Feature Detection." International Journal of Engineering and Advanced Technology 8, no. 4s2 (August 1, 2020): 51–56. http://dx.doi.org/10.35940/ijeat.d1004.0484s219.

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This paper is an experiment on the implementation of scale-invariant feature transform (SIFT) and speeded up robust features (SURF) algorithms into multi-dimensional neural networks. We are attempting to perform a comparative performance evaluation by using different scale factors of the SIFT algorithm in multi-layered neural networks. This method will help us to understand the best way of implementing the above algorithms in neural networks and from a given sample, extracting distinctive invariant features and finding points of interests. Hence performing a large data set computation would be made much easier because of the neural network implementation. The conventional method of performing SIFT has computational limitations and we aim to achieve best possible way of performing the feature detection when using SIFT and neural network combined, hence transcending computational limitations that SIFT previously had. This approach to recognition of features can robustly find results much faster on bigger dataset and at the same time have the benefits of SIFT algorithm.

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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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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 (March 31, 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 SIFT method looks for keypoint descriptors in images that can be used as a reference in comparing different images. After the keypoint descriptor is found with SIFT, the Haar Cascade Classifier method is used to detect faces in the image. Haar Cascade Classifier is a practical algorithm for object detection in images. After facial features are extracted with these two methods, the results are compared with the K-Nearest Neighbor (KNN) approach. This research involves the introduction of 28 color images with complex backgrounds. The results of combining these two methods produce an accuracy of 81.75%. This shows that combining these two methods effectively overcomes complex background challenges in face detection.

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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 (October 1, 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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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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Xin, Ming, Sheng Wei Li, and Miao Hui Zhang. "Robust Object Tracking by Particle Filter with Scale Invariant Features." Applied Mechanics and Materials 151 (January 2012): 458–62. http://dx.doi.org/10.4028/www.scientific.net/amm.151.458.

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Few literatures employ SIFT (scale-invariant feature transform) for tracking because it is time-consuming. However, we found that SIFT can be adapted to real-time tracking by employing it on a subarea of the whole image. In this paper the particle filter based method exploits SIFT features to handle challenging scenarios such as partial occlusions, scale variations and moderate deformations. As proposed in our method, not a brute-force feature extraction in the whole image, we firstly extract SIFT keypoints in the object search region only for once, through matching SIFT features between object search region and object template, the number of matched keypoints is obtained, which is utilized to compute the particle weights. Finally, we can obtain an optimal estimate to object location by the particle filter framework. Comparative experiments with quantitative evaluations are provided, which indicate that the proposed method is both robust and faster.

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Li, Xin, Bin Feng, Sai Qiao, Haiyan Wei, and Changli Feng. "SIFT-GVF-based lung edge correction method for correcting the lung region in CT images." PLOS ONE 18, no. 2 (February 28, 2023): e0282107. http://dx.doi.org/10.1371/journal.pone.0282107.

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Juxtapleural nodules were excluded from the segmented lung region in the Hounsfield unit threshold-based segmentation method. To re-include those regions in the lung region, a new approach was presented using scale-invariant feature transform and gradient vector flow models in this study. First, the scale-invariant feature transform method was utilized to detect all scale-invariant points in the binary lung region. The boundary points in the neighborhood of a scale-invariant point were collected to form the supportive boundary lines. Then, we utilized a Fourier descriptor to obtain a character representation of each supportive boundary line. Spectrum energy recognizes supportive boundaries that must be corrected. Third, the gradient vector flow-snake method was presented to correct the recognized supportive borders with a smooth profile curve, giving an ideal correction edge in those regions. Finally, the performance of the proposed method was evaluated through experiments on multiple authentic computed tomography images. The perfect results and robustness proved that the proposed method could correct the juxtapleural region precisely.

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Zhang Qiang, 张强, 郝凯 Hao Kai, and 李海滨 Li Haibin. "Research on Scale Invariant Feature Transform Feature Matching Based on Underwater Curve Constraint." Acta Optica Sinica 34, no. 2 (2014): 0215003. http://dx.doi.org/10.3788/aos201434.0215003.

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Patil, Sandeep Baburao, and G. R. Sinha. "Distinctive Feature Extraction for Indian Sign Language (ISL) Gesture using Scale Invariant Feature Transform (SIFT)." Journal of The Institution of Engineers (India): Series B 98, no. 1 (July 21, 2016): 19–26. http://dx.doi.org/10.1007/s40031-016-0250-8.

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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 (August 27, 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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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 (October 1, 2017): 2565. http://dx.doi.org/10.11591/ijece.v7i5.pp2565-2673.

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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.

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R.Vimala Devi, M., and S. Kalaivani. "Band Selection Using SIFT in Hyperspectral Images." International Journal of Engineering & Technology 7, no. 4.10 (October 2, 2018): 28. http://dx.doi.org/10.14419/ijet.v7i4.10.20698.

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In this paper an approach for dimension reduction of the hyperspectral image using scale invariant feature transform (SIFT) is introduced. Due to high dimensionality of hyperspectral cubes, it is a very difficult task to select few informative bands from original hyperspectral remote sensing images. Band with maximum amount of non-redundant information are chosen using the dissimilarity matrix obtained from scale invariant feature transformed image. The performance of the dimension reduction technique is analyzed by implementing a post-processing technique named spectral un-mixing. Spectral unmixing is the process of extracting end members and generating their abundance maps. End members are extracted with these selected informative bands using N-FINDR and abundance maps are generated using fully constrained least square estimation. The simulation software used for implementation of algorithms is MATLAB. Qualitatively and quantitatively the proposed feature based approach has been analyzed with application to spectral unmixing by comparing with two well-known existing dimension reduction techniques namely principal Component Analysis and Linear Discriminant Analysis. Hyperspectral images finds application in astronomy, agriculture, geosciences and surveillance.

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Hamidi, Mohamed, Mohamed El Haziti, Hocine Cherifi, and Mohammed El Hassouni. "A Hybrid Robust Image Watermarking Method Based on DWT-DCT and SIFT for Copyright Protection." Journal of Imaging 7, no. 10 (October 19, 2021): 218. http://dx.doi.org/10.3390/jimaging7100218.

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In this paper, a robust hybrid watermarking method based on discrete wavelet transform (DWT), discrete cosine transform (DCT), and scale-invariant feature transformation (SIFT) is proposed. Indeed, it is of prime interest to develop robust feature-based image watermarking schemes to withstand both image processing attacks and geometric distortions while preserving good imperceptibility. To this end, a robust watermark is embedded in the DWT-DCT domain to withstand image processing manipulations, while SIFT is used to protect the watermark from geometric attacks. First, the watermark is embedded in the middle band of the discrete cosine transform (DCT) coefficients of the HL1 band of the discrete wavelet transform (DWT). Then, the SIFT feature points are registered to be used in the extraction process to correct the geometric transformations. Extensive experiments have been conducted to assess the effectiveness of the proposed scheme. The results demonstrate its high robustness against standard image processing attacks and geometric manipulations while preserving a high imperceptibility. Furthermore, it compares favorably with alternative methods.

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Pandey, Ramesh Chand, Sanjay Kumar Singh, and K. K. Shukla. "Passive Copy- Move Forgery Detection Using Speed-Up Robust Features, Histogram Oriented Gradients and Scale Invariant Feature Transform." International Journal of System Dynamics Applications 4, no. 3 (July 2015): 70–89. http://dx.doi.org/10.4018/ijsda.2015070104.

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Copy-Move is one of the most common technique for digital image tampering or forgery. Copy-Move in an image might be done to duplicate something or to hide an undesirable region. In some cases where these images are used for important purposes such as evidence in court of law, it is important to verify their authenticity. In this paper the authors propose a novel method to detect single region Copy-Move Forgery Detection (CMFD) using Speed-Up Robust Features (SURF), Histogram Oriented Gradient (HOG), Scale Invariant Features Transform (SIFT), and hybrid features such as SURF-HOG and SIFT-HOG. SIFT and SURF image features are immune to various transformations like rotation, scaling, translation, so SIFT and SURF image features help in detecting Copy-Move regions more accurately in compared to other image features. Further the authors have detected multiple regions COPY-MOVE forgery using SURF and SIFT image features. Experimental results demonstrate commendable performance of proposed methods.

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Widya Agata, Ayu, Wahyu S J Saputra, and Chrystia Aji Putra. "PENGENALAN BAHASA ISYARAT INDONESIA (BISINDO) MENGGUNAKAN ALGORITMA SCALE INVARIANT FEATURE TRANSFORM (SIFT) DAN CONVOLUTIONAL NEURAL NETWORK (CNN)." JATI (Jurnal Mahasiswa Teknik Informatika) 8, no. 1 (March 27, 2024): 1054–61. http://dx.doi.org/10.36040/jati.v8i1.8917.

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Bahasa isyarat berperan penting sebagai media komunikasi bagi tuna rungu dan tuna wicara. Namun, keterbatasan pemahaman masyarakat umum terhadap bahasa isyarat menjadi hambatan dalam interaksi dengan mereka. Untuk mengatasi masalah ini, dirancang suatu sistem pengenalan bahasa isyarat yang dapat membantu masyarakat umum berkomunikasi dengan mudah. Sistem ini mengenali alfabet Bahasa Isyarat Indonesia (BISINDO) dengan menggabungkan algoritma Scale Invariant Feature Transform (SIFT) dan Convolutional Neural Network (CNN). Pendekatan ini dipilih karena SIFT digunakan untuk ekstraksi fitur awal, sementara CNN untuk mengenali pola yang kompleks, meningkatkan kekuatan model terhadap variasi gerakan dan pencahayaan dalam bahasa isyarat. Dengan menggunakan algoritma SIFT dan CNN gerakan tangan Bahasa isyarat indonesia dapat diperoleh, dicocokkan, dikenali, diautentikasi dan kinerja pencocokannya disimulasikan menggunakan library tersorflow dan OpenCV. Dalam penelitian, beberapa gerakan memerlukan percobaan berulang untuk deteksi yang akurat, mengingat kemiripan dengan gerakan bahasa isyarat lain. Meskipun demikian, akurasi tertinggi tercapai pada model dengan nilai epoch 55, mencapai 99.78%. Dengan demikian, integrasi algoritma SIFT dan CNN dalam sistem pengenalan bahasa isyarat dapat menjadi solusi efektif untuk mengatasi hambatan komunikasi antara tuna rungu, tuna wicara, dan masyarakat umum.

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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 (February 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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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 (February 21, 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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Wu, Zhen Yu, and Hu Hong. "Distance-Scale Restricted SIFT Matching Method for Complicated Content & Different Scales Image Pairs." Applied Mechanics and Materials 526 (February 2014): 292–96. http://dx.doi.org/10.4028/www.scientific.net/amm.526.292.

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Scale invariant feature transform (SIFT) matching performance decreases greatly when images are in different scales with complicated content and wide-baseline. In this paper, we address this problem, and propose a simple method to improve SIFT matching. The proposed method restricts the matching searching area into much smaller and more likely region to improve matching performance. Experiments shows that the proposed method has saved up to 90% matching time and increased up to 4% in the accuracy, compared with SIFT and previously solutions which only improve the matching accuracy.

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Belarbi, Mohammed Amin, Saïd Mahmoudi, and Ghalem Belalem. "PCA as Dimensionality Reduction for Large-Scale Image Retrieval Systems." International Journal of Ambient Computing and Intelligence 8, no. 4 (October 2017): 45–58. http://dx.doi.org/10.4018/ijaci.2017100104.

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Dimensionality reduction in large-scale image research plays an important role for their performance in different applications. In this paper, we explore Principal Component Analysis (PCA) as a dimensionality reduction method. For this purpose, first, the Scale Invariant Feature Transform (SIFT) features and Speeded Up Robust Features (SURF) are extracted as image features. Second, the PCA is applied to reduce the dimensions of SIFT and SURF feature descriptors. By comparing multiple sets of experimental data with different image databases, we have concluded that PCA with a reduction in the range, can effectively reduce the computational cost of image features, and maintain the high retrieval performance as well

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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 (March 28, 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, combining them together leads to produce distinct features. Many experiments have been carried out using IIT-Delhi knuckle database to assess the accuracy of the proposed approach. The analysis of the results of these extensive experiments implies that the suggested technique has gained 98% accuracy rate. Furthermore, the robustness against the noise has been evaluated. The results of these experiments lead to concluding that the proposed technique is robust against the noise variation. Keywords: finger knuckle, biometric system, Chebyshev Fourier moments, scale invariant feature transform, IIT-Delhi knuckle database.

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Journal articles on the topic 'Scale-Invariant-Feature-Transform (SIFT)'

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