Journal articles: '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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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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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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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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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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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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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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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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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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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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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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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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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 (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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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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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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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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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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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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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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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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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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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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Chang, Wen-Yang, and Chih-Ping Tsai. "Illumination characteristics and image stitching for automatic inspection of bicycle part." Assembly Automation 34, no. 4 (September 9, 2014): 342–48. http://dx.doi.org/10.1108/aa-09-2013-076.

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Purpose – This study aims to investigate the spectral illumination characteristics and geometric features of bicycle parts and proposes an image stitching method for their automatic visual inspection. Design/methodology/approach – The unrealistic color casts of feature inspection is removed using white balance for global adjustment. The scale-invariant feature transforms (SIFT) is used to extract and detect the image features of image stitching. The Hough transform is used to detect the parameters of a circle for roundness of bicycle parts. Findings – Results showed that maximum errors of 0°, 10°, 20°, 30°, 40° and 50° for the spectral illumination of white light light-emitting diode arrays with differential shift displacements are 4.4, 4.2, 7.8, 6.8, 8.1 and 3.5 per cent, respectively. The deviation error of image stitching for the stem accessory in x and y coordinates are 2 pixels. The SIFT and RANSAC enable to transform the stem image into local feature coordinates that are invariant to the illumination change. Originality/value – This study can be applied to many fields of modern industrial manufacturing and provide useful information for automatic inspection and image stitching.

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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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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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Kang, Nan Nan, Xiao Fang Wang, and Rong Rong Zhang. "Image Classification Based on Color Topic Model." Applied Mechanics and Materials 556-562 (May 2014): 4770–73. http://dx.doi.org/10.4028/www.scientific.net/amm.556-562.4770.

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This paper addresses semantic image classification with topic model, which focusing on discovering a hidden semantic to solve the semantic gap between low-level visual feature and high-level feature. In our approach, Latent Dirichlet Allocation (LDA) model successfully reflect the high level features and the RGB SIFT features which integrating the Scale-invariant feature transform (SIFT) features with color features on the assumption that pictures generated by mixture of latent semantic which we called topics. The proposed approach has a sufficient theoretical basis and the experimental evaluations the COREL database demonstrate its promise of the effectiveness.

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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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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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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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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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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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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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Stanciu, Stefan G., Radu Hristu, Radu Boriga, and George A. Stanciu. "On the Suitability of SIFT Technique to Deal with Image Modifications Specific to Confocal Scanning Laser Microscopy." Microscopy and Microanalysis 16, no. 5 (August 5, 2010): 515–30. http://dx.doi.org/10.1017/s1431927610000371.

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AbstractComputer vision tasks such as recognition and classification of objects and structures or image registration and retrieval can provide significant information when applied to microscopy images. Recently developed techniques for the detection and description of local features make the extraction and description of local image features that are invariant to various changes possible. The invariance and robustness of feature detection and description techniques play a key role in the design and implementation of object recognition, image registration, or image mosaicing applications. The scale-invariant feature transform (SIFT) technique is a widely used method for the detection, description, and matching of image features. In this article we present the results of our experiments regarding the repeatability of SIFT features, and to the precision of the SIFT feature matching, under image modifications specific to confocal scanning laser microscopy (CSLM). We have analyzed the behavior of SIFT while changing the pinhole aperture, photomultiplier gain, laser beam power, and electronic zoom. Our experiments, conducted on CSLM images, show that the SIFT technique is able to match detected key points between images acquired at different values of the acquisition parameters with good precision and represents a consistent tool for computer vision applications in CSLM.

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Yousuf, Muhammad, Zahid Mehmood, Hafiz Adnan Habib, Toqeer Mahmood, Tanzila Saba, Amjad Rehman, and Muhammad Rashid. "A Novel Technique Based on Visual Words Fusion Analysis of Sparse Features for Effective Content-Based Image Retrieval." Mathematical Problems in Engineering 2018 (2018): 1–13. http://dx.doi.org/10.1155/2018/2134395.

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Content-based image retrieval (CBIR) is a mechanism that is used to retrieve similar images from an image collection. In this paper, an effective novel technique is introduced to improve the performance of CBIR on the basis of visual words fusion of scale-invariant feature transform (SIFT) and local intensity order pattern (LIOP) descriptors. SIFT performs better on scale changes and on invariant rotations. However, SIFT does not perform better in the case of low contrast and illumination changes within an image, while LIOP performs better in such circumstances. SIFT performs better even at large rotation and scale changes, while LIOP does not perform well in such circumstances. Moreover, SIFT features are invariant to slight distortion as compared to LIOP. The proposed technique is based on the visual words fusion of SIFT and LIOP descriptors which overcomes the aforementioned issues and significantly improves the performance of CBIR. The experimental results of the proposed technique are compared with another proposed novel features fusion technique based on SIFT-LIOP descriptors as well as with the state-of-the-art CBIR techniques. The qualitative and quantitative analysis carried out on three image collections, namely, Corel-A, Corel-B, and Caltech-256, demonstrate the robustness of the proposed technique based on visual words fusion as compared to features fusion and the state-of-the-art CBIR techniques.

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Javeed.S, Imran, Aanandha Saravanan, and Rajendra Kumar. "Efficient Biometric Recognition Methodology using Guided Filtering and SIFT Feature Matching." International Journal of Engineering & Technology 7, no. 3.1 (August 4, 2018): 23. http://dx.doi.org/10.14419/ijet.v7i3.1.16789.

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A novel infrared finger vein biometric identification is proposed using Linear Gabor filter with Guidance image and SIFT feature matching. Linear Gabor filter with guidance image is used for extracting finger vein pattern without segmentation processing and also performs well with some poor quality images due to low contrast, illuminance imbalance or noise etc. Firstly, we utilized Guided Linear Gabor filter for ridge detection as simple Linear Gabor filter and also enhance the image by performing edge preserving smoothing operation. Secondly we utilized SIFT feature matching for verification. A SIFT (Scale Invariant Feature Transform) can extract features to posses rotation invariance and shift invariance for providing better matching rate. The simulation analysis shows our proposed system is an effective feature for finger vein biometric identification.

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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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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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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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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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Li, Yanshan, Weiming Liu, Xiaotang Li, Qinghua Huang, and Xuelong Li. "GA-SIFT: A new scale invariant feature transform for multispectral image using geometric algebra." Information Sciences 281 (October 2014): 559–72. http://dx.doi.org/10.1016/j.ins.2013.12.022.

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吕, 鹏霄. "Emotional Image Classification Based on Color Scale Invariant Feature Transform Feature and Spatial Pyramid Model." Journal of Image and Signal Processing 03, no. 01 (2014): 1–8. http://dx.doi.org/10.12677/jisp.2014.31001.

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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 (March 19, 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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N. Sultani, Zainab, and Ban N. Dhannoon. "Modified Bag of Visual Words Model for Image Classification." Al-Nahrain Journal of Science 24, no. 2 (June 1, 2021): 78–86. http://dx.doi.org/10.22401/anjs.24.2.11.

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Image classification is acknowledged as one of the most critical and challenging tasks in computer vision. The bag of visual words (BoVW) model has proven to be very efficient for image classification tasks since it can effectively represent distinctive image features in vector space. In this paper, BoVW using Scale-Invariant Feature Transform (SIFT) and Oriented Fast and Rotated BRIEF(ORB) descriptors are adapted for image classification. We propose a novel image classification system using image local feature information obtained from both SIFT and ORB local feature descriptors. As a result, the constructed SO-BoVW model presents highly discriminative features, enhancing the classification performance. Experiments on Caltech-101 and flowers dataset prove the effectiveness of the proposed method.

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Purwandari, Endina Putri, Arie Vatresia, and Sudarti Siburian. "Deteksi Image Splicing Pada Citra dengan Metode Discrete Cosine Transform (DCT) dan Scale Invariant Feature Transform (SIFT)." Pseudocode 6, no. 2 (October 28, 2019): 138–48. http://dx.doi.org/10.33369/pseudocode.6.2.138-148.

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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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Journal articles on the topic 'SIFT - scale-Invariant feature transform'

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