Relevant bibliographies by topics / SIFT - scale-Invariant feature transform

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Academic literature on the topic 'SIFT - scale-Invariant feature transform'

Author: Grafiati
Published: 4 June 2021
Last updated: 1 February 2022

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Journal articles on the topic "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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Dissertations / Theses on the topic "SIFT - scale-Invariant feature transform"

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Decombas, Marc. "Compression vidéo très bas débit par analyse du contenu." Thesis, Paris, ENST, 2013. http://www.theses.fr/2013ENST0067/document.

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L’objectif de cette thèse est de trouver de nouvelles méthodes de compression sémantique compatible avec un encodeur classique tel que H.264/AVC. . L’objectif principal est de maintenir la sémantique et non pas la qualité globale. Un débit cible de 300 kb/s a été fixé pour des applications de sécurité et de défense Pour cela une chaine complète de compression a dû être réalisée. Une étude et des contributions sur les modèles de saillance spatio-temporel ont été réalisées avec pour objectif d’extraire l’information pertinente. Pour réduire le débit, une méthode de redimensionnement dénommée «seam carving » a été combinée à un encodeur H.264/AVC. En outre, une métrique combinant les points SIFT et le SSIM a été réalisée afin de mesurer la qualité des objets sans être perturbée par les zones de moindre contenant la majorité des artefacts. Une base de données pouvant être utilisée pour des modèles de saillance mais aussi pour de la compression est proposée avec des masques binaires. Les différentes approches ont été validées par divers tests. Une extension de ces travaux pour des applications de résumé vidéo est proposée
The objective of this thesis is to find new methods for semantic video compatible with a traditional encoder like H.264/AVC. The main objective is to maintain the semantic and not the global quality. A target bitrate of 300 Kb/s has been fixed for defense and security applications. To do that, a complete chain of compression has been proposed. A study and new contributions on a spatio-temporal saliency model have been done to extract the important information in the scene. To reduce the bitrate, a resizing method named seam carving has been combined with the H.264/AVC encoder. Also, a metric combining SIFT points and SSIM has been created to measure the quality of objects without being disturbed by less important areas containing mostly artifacts. A database that can be used for testing the saliency model but also for video compression has been proposed, containing sequences with their manually extracted binary masks. All the different approaches have been thoroughly validated by different tests. An extension of this work on video summary application has also been proposed
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May, Michael. "Data analytics and methods for improved feature selection and matching." Thesis, University of Manchester, 2012. https://www.research.manchester.ac.uk/portal/en/theses/data-analytics-and-methods-for-improved-feature-selection-and-matching(965ded10-e3a0-4ed5-8145-2af7a8b5e35d).html.

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This work focuses on analysing and improving feature detection and matching. After creating an initial framework of study, four main areas of work are researched. These areas make up the main chapters within this thesis and focus on using the Scale Invariant Feature Transform (SIFT).The preliminary analysis of the SIFT investigates how this algorithm functions. Included is an analysis of the SIFT feature descriptor space and an investigation into the noise properties of the SIFT. It introduces a novel use of the a contrario methodology and shows the success of this method as a way of discriminating between images which are likely to contain corresponding regions from images which do not. Parameter analysis of the SIFT uses both parameter sweeps and genetic algorithms as an intelligent means of setting the SIFT parameters for different image types utilising a GPGPU implementation of SIFT. The results have demonstrated which parameters are more important when optimising the algorithm and the areas within the parameter space to focus on when tuning the values. A multi-exposure, High Dynamic Range (HDR), fusion features process has been developed where the SIFT image features are matched within high contrast scenes. Bracketed exposure images are analysed and features are extracted and combined from different images to create a set of features which describe a larger dynamic range. They are shown to reduce the effects of noise and artefacts that are introduced when extracting features from HDR images directly and have a superior image matching performance. The final area is the development of a novel, 3D-based, SIFT weighting technique which utilises the 3D data from a pair of stereo images to cluster and class matched SIFT features. Weightings are applied to the matches based on the 3D properties of the features and how they cluster in order to attempt to discriminate between correct and incorrect matches using the a contrario methodology. The results show that the technique provides a method for discriminating between correct and incorrect matches and that the a contrario methodology has potential for future investigation as a method for correct feature match prediction.
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Dardas, Nasser Hasan Abdel-Qader. "Real-time Hand Gesture Detection and Recognition for Human Computer Interaction." Thèse, Université d'Ottawa / University of Ottawa, 2012. http://hdl.handle.net/10393/23499.

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This thesis focuses on bare hand gesture recognition by proposing a new architecture to solve the problem of real-time vision-based hand detection, tracking, and gesture recognition for interaction with an application via hand gestures. The first stage of our system allows detecting and tracking a bare hand in a cluttered background using face subtraction, skin detection and contour comparison. The second stage allows recognizing hand gestures using bag-of-features and multi-class Support Vector Machine (SVM) algorithms. Finally, a grammar has been developed to generate gesture commands for application control. Our hand gesture recognition system consists of two steps: offline training and online testing. In the training stage, after extracting the keypoints for every training image using the Scale Invariance Feature Transform (SIFT), a vector quantization technique will map keypoints from every training image into a unified dimensional histogram vector (bag-of-words) after K-means clustering. This histogram is treated as an input vector for a multi-class SVM to build the classifier. In the testing stage, for every frame captured from a webcam, the hand is detected using my algorithm. Then, the keypoints are extracted for every small image that contains the detected hand posture and fed into the cluster model to map them into a bag-of-words vector, which is fed into the multi-class SVM classifier to recognize the hand gesture. Another hand gesture recognition system was proposed using Principle Components Analysis (PCA). The most eigenvectors and weights of training images are determined. In the testing stage, the hand posture is detected for every frame using my algorithm. Then, the small image that contains the detected hand is projected onto the most eigenvectors of training images to form its test weights. Finally, the minimum Euclidean distance is determined among the test weights and the training weights of each training image to recognize the hand gesture. Two application of gesture-based interaction with a 3D gaming virtual environment were implemented. The exertion videogame makes use of a stationary bicycle as one of the main inputs for game playing. The user can control and direct left-right movement and shooting actions in the game by a set of hand gesture commands, while in the second game, the user can control and direct a helicopter over the city by a set of hand gesture commands.
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Murtin, Chloé Isabelle. "Traitement d’images de microscopie confocale 3D haute résolution du cerveau de la mouche Drosophile." Thesis, Lyon, 2016. http://www.theses.fr/2016LYSEI081/document.

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La profondeur possible d’imagerie en laser-scanning microscopie est limitée non seulement par la distance de travail des lentilles de objectifs mais également par la dégradation de l’image causée par une atténuation et une diffraction de la lumière passant à travers l’échantillon. Afin d’étendre cette limite, il est possible, soit de retourner le spécimen pour enregistrer les images depuis chaque côté, or couper progressivement la partie supérieure de l’échantillon au fur et à mesure de l‘acquisition. Les différentes images prises de l’une de ces manières doivent ensuite être combinées pour générer un volume unique. Cependant, des mouvements de l’échantillon durant les procédures d’acquisition engendrent un décalage non seulement sur en translation selon les axes x, y et z mais également en rotation autour de ces même axes, rendant la fusion entres ces multiples images difficile. Nous avons développé une nouvelle approche appelée 2D-SIFT-in-3D-Space utilisant les SIFT (scale Invariant Feature Transform) pour atteindre un recalage robuste en trois dimensions de deux images. Notre méthode recale les images en corrigeant séparément les translations et rotations sur les trois axes grâce à l’extraction et l’association de caractéristiques stables de leurs coupes transversales bidimensionnelles. Pour évaluer la qualité du recalage, nous avons également développé un simulateur d’images de laser-scanning microscopie qui génère une paire d’images 3D virtuelle dans laquelle le niveau de bruit et les angles de rotations entre les angles de rotation sont contrôlés avec des paramètres connus. Pour une concaténation précise et naturelle de deux images, nous avons également développé un module permettant une compensation progressive de la luminosité et du contraste en fonction de la distance à la surface de l’échantillon. Ces outils ont été utilisés avec succès pour l’obtention d’images tridimensionnelles de haute résolution du cerveau de la mouche Drosophila melanogaster, particulièrement des neurones dopaminergiques, octopaminergiques et de leurs synapses. Ces neurones monoamines sont particulièrement important pour le fonctionnement du cerveau et une étude de leur réseau et connectivité est nécessaire pour comprendre leurs interactions. Si une évolution de leur connectivité au cours du temps n’a pas pu être démontrée via l’analyse de la répartition des sites synaptiques, l’étude suggère cependant que l’inactivation de l’un de ces types de neurones entraine des changements drastiques dans le réseau neuronal
Although laser scanning microscopy is a powerful tool for obtaining thin optical sections, the possible depth of imaging is limited by the working distance of the microscope objective but also by the image degradation caused by the attenuation of both excitation laser beam and the light emitted from the fluorescence-labeled objects. Several workaround techniques have been employed to overcome this problem, such as recording the images from both sides of the sample, or by progressively cutting off the sample surface. The different views must then be combined in a unique volume. However, a straightforward concatenation is often not possible, because the small rotations that occur during the acquisition procedure, not only in translation along x, y and z axes but also in rotation around those axis, making the fusion uneasy. To address this problem we implemented a new algorithm called 2D-SIFT-in-3D-Space using SIFT (scale Invariant Feature Transform) to achieve a robust registration of big image stacks. Our method register the images fixing separately rotations and translations around the three axes using the extraction and matching of stable features in 2D cross-sections. In order to evaluate the registration quality, we created a simulator that generates artificial images that mimic laser scanning image stacks to make a mock pair of image stacks one of which is made from the same stack with the other but is rotated arbitrarily with known angles and filtered with a known noise. For a precise and natural-looking concatenation of the two images, we also developed a module progressively correcting the sample brightness and contrast depending on the sample surface. Those tools we successfully used to generate tridimensional high resolution images of the fly Drosophila melanogaster brain, in particular, its octopaminergic and dopaminergic neurons and their synapses. Those monoamine neurons appear to be determinant in the correct operating of the central nervous system and a precise and systematic analysis of their evolution and interaction is necessary to understand its mechanisms. If an evolution over time could not be highlighted through the pre-synaptic sites analysis, our study suggests however that the inactivation of one of these neuron types triggers drastic changes in the neural network
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Dellinger, Flora. "Descripteurs locaux pour l'imagerie radar et applications." Thesis, Paris, ENST, 2014. http://www.theses.fr/2014ENST0037/document.

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Nous étudions ici l’intérêt des descripteurs locaux pour les images satellites optiques et radar. Ces descripteurs, par leurs invariances et leur représentation compacte, offrent un intérêt pour la comparaison d’images acquises dans des conditions différentes. Facilement applicables aux images optiques, ils offrent des performances limitées sur les images radar, en raison de leur fort bruit multiplicatif. Nous proposons ici un descripteur original pour la comparaison d’images radar. Cet algorithme, appelé SAR-SIFT, repose sur la même structure que l’algorithme SIFT (détection de points-clés et extraction de descripteurs) et offre des performances supérieures pour les images radar. Pour adapter ces étapes au bruit multiplicatif, nous avons développé un opérateur différentiel, le Gradient par Ratio, permettant de calculer une norme et une orientation du gradient robustes à ce type de bruit. Cet opérateur nous a permis de modifier les étapes de l’algorithme SIFT. Nous présentons aussi deux applications pour la télédétection basées sur les descripteurs. En premier, nous estimons une transformation globale entre deux images radar à l’aide de SAR-SIFT. L’estimation est réalisée à l’aide d’un algorithme RANSAC et en utilisant comme points homologues les points-clés mis en correspondance. Enfin nous avons mené une étude prospective sur l’utilisation des descripteurs pour la détection de changements en télédétection. La méthode proposée compare les densités de points-clés mis en correspondance aux densités de points-clés détectés pour mettre en évidence les zones de changement
We study here the interest of local features for optical and SAR images. These features, because of their invariances and their dense representation, offer a real interest for the comparison of satellite images acquired under different conditions. While it is easy to apply them to optical images, they offer limited performances on SAR images, because of their multiplicative noise. We propose here an original feature for the comparison of SAR images. This algorithm, called SAR-SIFT, relies on the same structure as the SIFT algorithm (detection of keypoints and extraction of features) and offers better performances for SAR images. To adapt these steps to multiplicative noise, we have developed a differential operator, the Gradient by Ratio, allowing to compute a magnitude and an orientation of the gradient robust to this type of noise. This operator allows us to modify the steps of the SIFT algorithm. We present also two applications for remote sensing based on local features. First, we estimate a global transformation between two SAR images with help of SAR-SIFT. The estimation is realized with help of a RANSAC algorithm and by using the matched keypoints as tie points. Finally, we have led a prospective study on the use of local features for change detection in remote sensing. The proposed method consists in comparing the densities of matched keypoints to the densities of detected keypoints, in order to point out changed areas
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Saad, Elhusain Salem. "Defocus Blur-Invariant Scale-Space Feature Extractions." University of Dayton / OhioLINK, 2014. http://rave.ohiolink.edu/etdc/view?acc_num=dayton1418907974.

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Hejl, Zdeněk. "Rekonstrukce 3D scény z obrazových dat." Master's thesis, Vysoké učení technické v Brně. Fakulta informačních technologií, 2012. http://www.nusl.cz/ntk/nusl-236495.

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This thesis describes methods of reconstruction of 3D scenes from photographs and videos using the Structure from motion approach. A new software capable of automatic reconstruction of point clouds and polygonal models from common images and videos was implemented based on these methods. The software uses variety of existing and custom solutions and clearly links them into one easily executable application. The reconstruction consists of feature point detection, pairwise matching, Bundle adjustment, stereoscopic algorithms and polygon model creation from point cloud using PCL library. Program is based on Bundler and PMVS. Poisson surface reconstruction algorithm, as well as simple triangulation and own reconstruction method based on plane segmentation were used for polygonal model creation.
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Saravi, Sara. "Use of Coherent Point Drift in computer vision applications." Thesis, Loughborough University, 2013. https://dspace.lboro.ac.uk/2134/12548.

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This thesis presents the novel use of Coherent Point Drift in improving the robustness of a number of computer vision applications. CPD approach includes two methods for registering two images - rigid and non-rigid point set approaches which are based on the transformation model used. The key characteristic of a rigid transformation is that the distance between points is preserved, which means it can be used in the presence of translation, rotation, and scaling. Non-rigid transformations - or affine transforms - provide the opportunity of registering under non-uniform scaling and skew. The idea is to move one point set coherently to align with the second point set. The CPD method finds both the non-rigid transformation and the correspondence distance between two point sets at the same time without having to use a-priori declaration of the transformation model used. The first part of this thesis is focused on speaker identification in video conferencing. A real-time, audio-coupled video based approach is presented, which focuses more on the video analysis side, rather than the audio analysis that is known to be prone to errors. CPD is effectively utilised for lip movement detection and a temporal face detection approach is used to minimise false positives if face detection algorithm fails to perform. The second part of the thesis is focused on multi-exposure and multi-focus image fusion with compensation for camera shake. Scale Invariant Feature Transforms (SIFT) are first used to detect keypoints in images being fused. Subsequently this point set is reduced to remove outliers, using RANSAC (RANdom Sample Consensus) and finally the point sets are registered using CPD with non-rigid transformations. The registered images are then fused with a Contourlet based image fusion algorithm that makes use of a novel alpha blending and filtering technique to minimise artefacts. The thesis evaluates the performance of the algorithm in comparison to a number of state-of-the-art approaches, including the key commercial products available in the market at present, showing significantly improved subjective quality in the fused images. The final part of the thesis presents a novel approach to Vehicle Make & Model Recognition in CCTV video footage. CPD is used to effectively remove skew of vehicles detected as CCTV cameras are not specifically configured for the VMMR task and may capture vehicles at different approaching angles. A LESH (Local Energy Shape Histogram) feature based approach is used for vehicle make and model recognition with the novelty that temporal processing is used to improve reliability. A number of further algorithms are used to maximise the reliability of the final outcome. Experimental results are provided to prove that the proposed system demonstrates an accuracy in excess of 95% when tested on real CCTV footage with no prior camera calibration.
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Sahin, Yavuz. "A Programming Framework To Implement Rule-based Target Detection In Images." Master's thesis, METU, 2008. http://etd.lib.metu.edu.tr/upload/12610213/index.pdf.

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An expert system is useful when conventional programming techniques fall short of capturing human expert knowledge and making decisions using this information. In this study, we describe a framework for capturing expert knowledge under a decision tree form and this framework can be used for making decisions based on captured knowledge. The framework proposed in this study is generic and can be used to create domain specific expert systems for different problems. Features are created or processed by the nodes of decision tree and a final conclusion is reached for each feature. Framework supplies 3 types of nodes to construct a decision tree. First type is the decision node, which guides the search path with its answers. Second type is the operator node, which creates new features using the inputs. Last type of node is the end node, which corresponds to a conclusion about a feature. Once the nodes of the tree are developed, then user can interactively create the decision tree and run the supplied inference engine to collect the result on a specific problem. The framework proposed is experimented with two case studies
"
Airport Runway Detection in High Resolution Satellite Images"
and "
Urban Area Detection in High Resolution Satellite Images"
. In these studies linear features are used for structural decisions and Scale Invariant Feature Transform (SIFT) features are used for testing existence of man made structures.
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Yang, Tzung-Da, and 楊宗達. "Scale-Invariant Feature Transform (SIFT) Based Iris Match Technology for Identity Identification." Thesis, 2017. http://ndltd.ncl.edu.tw/handle/52714099795239015467.

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碩士
國立中興大學
電機工程學系所
105
Biometrics has been applied to the personal recognition popularly and it becomes more important. The iris recognition is one of the biometric identification methods, and the technology can provide the accurate personal recognition. As early as 2004, the German airport in Frankfurt began to use the iris identification system. By the iris scan identification, the iris information is linked to the passport data database, and the personal identity is functional. In recent years, the iris identification is used widely and increasingly in personal identifications. Even the mobile phone also begin to use the iris identification system, and the importance of biometrics gains more and more attention. The traditional iris recognition technology mainly transforms the iris feature region into a square matrix by using the polar coordinate method, and the square matrix is transformed to the feature codes, and then the signature is used to the feature match finally. The difference between the proposed and the traditional iris recognition systems is : to avoid the eyelid and eyelash interferences, the retrieved iris region in the proposed design only locates near the pupil around the ring area and the lower half of the iris area for recognitions. On the other side, the traditional iris identification uses the feature code matching technology; however, the proposed method uses the image feature matching technology, i.e. the scale-invariant feature transform (SIFT) method. The SIFT uses the local features of the image, and it keeps the feature invariance for the changes of rotation, scaling, and brightness. The SIFT also maintains a certain degree of stability for the change of the perspective affine transformation and noises. Therefore, it is very suitable that the SIFT technology is applied to iris feature matching. In the proposed design, the accuracy of the iris recognition is 95%. Compared with other methods by using the same database and the similar SIFT technology as the matching method, the recognition performance of the proposed design is suitable.
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Book chapters on the topic "SIFT - scale-Invariant feature transform"

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Burger, Wilhelm, and Mark J. Burge. "Scale-Invariant Feature Transform (SIFT)." In Texts in Computer Science, 609–64. London: Springer London, 2016. http://dx.doi.org/10.1007/978-1-4471-6684-9_25.

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Yang, Donglei, Lili Liu, Feiwen Zhu, and Weihua Zhang. "A Parallel Analysis on Scale Invariant Feature Transform (SIFT) Algorithm." In Lecture Notes in Computer Science, 98–111. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-24151-2_8.

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Maestas, Dominic R., Ron Lumia, Gregory Starr, and John Wood. "Scale Invariant Feature Transform (SIFT) Parametric Optimization Using Taguchi Design of Experiments." In Intelligent Robotics and Applications, 630–41. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-16584-9_61.

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Shekar, B. H., M. Sharmila Kumari, Leonid M. Mestetskiy, and Natalia Dyshkant. "FLD-SIFT: Class Based Scale Invariant Feature Transform for Accurate Classification of Faces." In Computer Networks and Information Technologies, 15–21. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-19542-6_3.

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Lim, Naeun, Daejune Ko, Kun Ha Suh, and Eui Chul Lee. "Thumb Biometric Using Scale Invariant Feature Transform." In Lecture Notes in Electrical Engineering, 85–90. Singapore: Springer Singapore, 2017. http://dx.doi.org/10.1007/978-981-10-5041-1_15.

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Nguyen, Thao, Eun-Ae Park, Jiho Han, Dong-Chul Park, and Soo-Young Min. "Object Detection Using Scale Invariant Feature Transform." In Advances in Intelligent Systems and Computing, 65–72. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-01796-9_7.

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Qiao, Xueting, and Yingmin Jia. "Scale Adaptive Kernelized Correlation Filter with Scale-Invariant Feature Transform." In Lecture Notes in Electrical Engineering, 311–23. Singapore: Springer Singapore, 2017. http://dx.doi.org/10.1007/978-981-10-6496-8_29.

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Kumar, Raman, and Uffe Kock Wiil. "Enhancing Gadgets for Blinds Through Scale Invariant Feature Transform." In Recent Advances in Computational Intelligence, 149–59. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-12500-4_9.

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Cui, Yan, Nils Hasler, Thorsten Thormählen, and Hans-Peter Seidel. "Scale Invariant Feature Transform with Irregular Orientation Histogram Binning." In Lecture Notes in Computer Science, 258–67. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-02611-9_26.

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Das, Bandita, Debabala Swain, Bunil Kumar Balabantaray, Raimoni Hansda, and Vishal Shukla. "Copy-Move Forgery Detection Using Scale Invariant Feature Transform." In Machine Learning and Information Processing, 521–32. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-33-4859-2_51.

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Conference papers on the topic "SIFT - scale-Invariant feature transform"

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Park, Jae-Han, Kyung-Wook Park, Seung-Ho Baeg, and Moon-Hong Baeg. "π-SIFT: A photometric and Scale Invariant Feature Transform." In 2008 19th International Conference on Pattern Recognition (ICPR). IEEE, 2008. http://dx.doi.org/10.1109/icpr.2008.4761181.

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Qasaimeh, Murad, Assim Sagahyroon, and Tamer Shanableh. "A parallel hardware architecture for Scale Invariant Feature Transform (SIFT)." In 2014 International Conference on Multimedia Computing and Systems (ICMCS). IEEE, 2014. http://dx.doi.org/10.1109/icmcs.2014.6911251.

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Zhang, Guimei, Binbin Chen, and YangQuan Chen. "Research on Image Matching Combining on Fractional Differential With Scale Invariant Feature Transform." In ASME 2015 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2015. http://dx.doi.org/10.1115/detc2015-47015.

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Abstract:
Image matching is one of the most important problems in computer vision. Scale Invariant Feature Transform (SIFT) algorithm has been proved to be effective for detecting features for image matching. However SIFT algorithm has limitation to extract features in textile image or self-similar construction image. Fortunately fractional differentiation has advantage to strengthen and extract textural features of digital images. Aiming at the problem, this paper proposes a new method for image matching based on fractional differentiation and SIFT. The method calculates the image pyramid combining the Riemann-Liouville (R-L) fractional differentiation and the derivative of the Gaussian function. Thus image feature has been enhanced, and more feature points can be extracted. As a result the matching accuracy is improved. Additionally, a new feature detection mask based on fractional differential is constructed. The proposed method is a significant extension of SIFT algorithm. The experiments carried out with images in database and real images indicate that the proposed method can obtain good matching results. It can be used for matching textile image or some self-similar construct image.
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Cheung, Warren, and Ghassan Hamarneh. "N-SIFT: N-DIMENSIONAL SCALE INVARIANT FEATURE TRANSFORM FOR MATCHING MEDICAL IMAGES." In 2007 4th IEEE International Symposium on Biomedical Imaging: From Nano to Macro. IEEE, 2007. http://dx.doi.org/10.1109/isbi.2007.356953.

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Hermansyah, Adi, Arif Nugroho, Arief Kurniawan, Supeno Mardi Susiki Nugroho, and Eko Mulyanto Yuniarno. "Panoramic of Image Reconstruction Based on Geospatial Data using SIFT (Scale Invariant Feature Transform)." In 2019 International Seminar on Intelligent Technology and Its Applications (ISITIA). IEEE, 2019. http://dx.doi.org/10.1109/isitia.2019.8937152.

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Rahman, Aviv Yuniar, Surya Sumpeno, and Mauridhi Hery Purnomo. "Arca Detection and Matching Using Scale Invariant Feature Transform (SIFT) Method of Stereo Camera." In 2017 International Conference on Soft Computing, Intelligent System and Information Technology (ICSIIT). IEEE, 2017. http://dx.doi.org/10.1109/icsiit.2017.45.

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Widyastuti, Rifka, and Chuan-Kai Yang. "Cat’s Nose Recognition Using You Only Look Once (Yolo) and Scale-Invariant Feature Transform (SIFT)." In 2018 IEEE 7th Global Conference on Consumer Electronics (GCCE). IEEE, 2018. http://dx.doi.org/10.1109/gcce.2018.8574870.

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Che Hussin, Nuril Aslina, Nursuriati Jamil, Sharifalillah Nordin, and Khalil Awang. "Plant species identification by using Scale Invariant Feature Transform (SIFT) and Grid Based Colour Moment (GBCM)." In 2013 IEEE Conference on Open Systems (ICOS). IEEE, 2013. http://dx.doi.org/10.1109/icos.2013.6735079.

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Sumiharto, Raden, Ristya Ginanjar Putra, and Samuel Demetouw. "Methods for Determining Nitrogen, Phosphorus, and Potassium (NPK) Nutrient Content Using Scale-Invariant Feature Transform (SIFT)." In 2020 8th International Conference on Information and Communication Technology (ICoICT). IEEE, 2020. http://dx.doi.org/10.1109/icoict49345.2020.9166292.

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Marlinda, Linda, Supriadi Rustad, Ruri Suko Basuki, Fikri Budiman, and Muhamad Fatchan. "Matching Images On The Face Of A Buddha Statue Using The Scale Invariant Feature Transform (SIFT) Method." In 2020 7th International Conference on Information Technology, Computer, and Electrical Engineering (ICITACEE). IEEE, 2020. http://dx.doi.org/10.1109/icitacee50144.2020.9239221.

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Reports on the topic "SIFT - scale-Invariant feature transform"

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Lei, Lydia. Three dimensional shape retrieval using scale invariant feature transform and spatial restrictions. Gaithersburg, MD: National Institute of Standards and Technology, 2009. http://dx.doi.org/10.6028/nist.ir.7625.

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