Relevant bibliographies by topics / Scale-Invariant-Feature-Transform (SIFT)

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  2. Dissertations / Theses
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Academic literature on the topic 'Scale-Invariant-Feature-Transform (SIFT)'

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Published: 4 June 2021
Last updated: 25 May 2024

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

1

B.Daneshvar, M. "SCALE INVARIANT FEATURE TRANSFORM PLUS HUE FEATURE." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-2/W6 (August 23, 2017): 27–32. http://dx.doi.org/10.5194/isprs-archives-xlii-2-w6-27-2017.

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This paper presents an enhanced method for extracting invariant features from images based on Scale Invariant Feature Transform (SIFT). Although SIFT features are invariant to image scale and rotation, additive noise, and changes in illumination but we think this algorithm suffers from excess keypoints. Besides, by adding the hue feature, which is extracted from combination of hue and illumination values in HSI colour space version of the target image, the proposed algorithm can speed up the matching phase. Therefore, we proposed the Scale Invariant Feature Transform plus Hue (SIFTH) that can remove the excess keypoints based on their Euclidean distances and adding hue to feature vector to speed up the matching process which is the aim of feature extraction. In this paper we use the difference of hue features and the Mean Square Error (MSE) of orientation histograms to find the most similar keypoint to the under processing keypoint. The keypoint matching method can identify correct keypoint among clutter and occlusion robustly while achieving real-time performance and it will result a similarity factor of two keypoints. Moreover removing excess keypoint by SIFTH algorithm helps the matching algorithm to achieve this goal.
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Cheung, W., and G. Hamarneh. "$n$-SIFT: $n$-Dimensional Scale Invariant Feature Transform." IEEE Transactions on Image Processing 18, no. 9 (September 2009): 2012–21. http://dx.doi.org/10.1109/tip.2009.2024578.

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Taha, Mohammed A., Hanaa M. Ahmed, and Saif O. Husain. "Iris Features Extraction and Recognition based on the Scale Invariant Feature Transform (SIFT)." Webology 19, no. 1 (January 20, 2022): 171–84. http://dx.doi.org/10.14704/web/v19i1/web19013.

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Iris Biometric authentication is considered to be one of the most dependable biometric characteristics for identifying persons. In actuality, iris patterns have invariant, stable, and distinguishing properties for personal identification. Due to its excellent dependability in personal identification, iris recognition has received more attention. Current iris recognition methods give good results especially when NIR and specific capture conditions are used in collaboration with the user. On the other hand, values related to images captured using VW are affected by noise such as blurry images, eye skin, occlusion, and reflection, which negatively affects the overall performance of the recognition systems. In both NIR and visible spectrum iris images, this article presents an effective iris feature extraction strategy based on the scale-invariant feature transform algorithm (SIFT). The proposed method was tested on different databases such as CASIA v1 and ITTD v1, as NIR images, as well as UBIRIS v1 as visible-light color images. The proposed system gave good accuracy rates compared to existing systems, as it gave an accuracy rate of (96.2%) when using CASIA v1 and (96.4%) in ITTD v1, while the system accuracy dropped to (84.0 %) when using UBIRIS v1.
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Wu, Shu Guang, Shu He, and Xia Yang. "The Application of SIFT Method towards Image Registration." Advanced Materials Research 1044-1045 (October 2014): 1392–96. http://dx.doi.org/10.4028/www.scientific.net/amr.1044-1045.1392.

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The scale invariant features transform (SIFT) is commonly used in object recognition,According to the problems of large memory consumption and low computation speed in SIFT (Scale Invariant Feature Transform) algorithm.During the image registration methods based on point features,SIFT point feature is invariant to image scale and rotation, and provides robust matching across a substantial range of affine distortion. Experiments show that on the premise that registration accuracy is stable, the proposed algorithm solves the problem of high requirement of memory and the efficiency is improved greatly, which is applicable for registering remote sensing images of large areas.
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A, Kalaiselvi, Sangeetha V, and Kasiselvanathan M. "Palm Pattern Recognition using Scale Invariant Feature Transform (SIFT)." International Journal of Intelligence and Sustainable Computing 1, no. 1 (2018): 1. http://dx.doi.org/10.1504/ijisc.2018.10023048.

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

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Qu, Zhong, and Zheng Yong Wang. "The Improved Algorithm of Scale Invariant Feature Transform on Palmprint Recognition." Advanced Materials Research 186 (January 2011): 565–69. http://dx.doi.org/10.4028/www.scientific.net/amr.186.565.

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This paper presents a new method of palmprint recognition based on improved scale invariant feature transform (SIFT) algorithm which combines the Euclidean distance and weighted sub-region. It has the scale, rotation, affine, perspective, illumination invariance, and also has good robustness to the target's motion, occlusion, noise and other factors. Simulation results show that the recognition rate of the improved SIFT algorithm is higher than the recognition rate of SIFT algorithm.
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Yuehua Tao, Youming Xia, Tianwei Xu, and Xiaoxiao Chi. "Research Progress of the Scale Invariant Feature Transform (SIFT) Descriptors." Journal of Convergence Information Technology 5, no. 1 (February 28, 2010): 116–21. http://dx.doi.org/10.4156/jcit.vol5.issue1.13.

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

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Fusi citra adalah proses menggabungkan dua atau lebih citra ke dalam satu citra, dengan mempertahankan fitur penting dari masing-masing gambar. Fusi citra adalah salah satu cara untuk menyelesaikan masalah gambar yang tidak fokus hasil dari penggunaan kamera non-profesional. Fusi citra juga dapat digunakan dalam penginderaan jauh, pengamatan, dan aplikasi medis. Dalam penelitian ini, diusulkan teknik fusi citra baru dengan menggunakan SIFT (Scale Invariant Feature Transform) sebagai registrasi citra. Prosedur fusi dilakukan dengan mencocokkan fitur gambar SIFT menggunakan RANSAC dan kemudian menggabungkan dua citra dengan aturan rata-rata piksel. Langkah terakhir membandingkan hasil fusi citra menggunakan QABF, intensitas rata-rata piksel dan standard deviasi. Hasil eksperimental menunjukkan bahwa metode yang diusulkan mengungguli teknik fusi konvensional, terutama untuk citra yang mengalami translasi atau rotasi.
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Ariel, Muhammad Baresi, Ratri Dwi Atmaja, and Azizah Azizah. "Implementasi Metode Speed Up Robust Feature dan Scale Invariant Feature Transform untuk Identifikasi Telapak Kaki Individu." JURNAL Al-AZHAR INDONESIA SERI SAINS DAN TEKNOLOGI 3, no. 4 (December 28, 2017): 178. http://dx.doi.org/10.36722/sst.v3i4.232.

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<p><em>Abstrak</em><strong> - Biometrik merupakan metode pengidentifikasian individu berdasarkan ciri fisiknya. Salah satu ciri fisik yang dapat digunakan untuk biometrik adalah telapak kaki. Ciri fisik ini dipilih karena memiliki tingkat keunikan yang tinggi, sehingga hampir tidak terdapat individu yang memiliki ciri yang sama. Metode-metode ekstraksi ciri seperti Scale Invariant Feature Transform (SIFT) dan Speed Up Robust Feature (SURF) akan sesuai jika digunakan untuk mendukung sistem identifikasi telapak kaki. Tahapan yang dilakukan untuk mendapatkan deskriptor dimulai dari scanning telapak kaki, pre-processing, ekstraksi ciri dengan menggunakan SURF dan SIFT sampai pada proses matching pada saat pengujian. Perbandingan keduanya dilihat dari aspek akurasi. Proses penentuan klasifikasi dan kelas menggunakan algoritma K-Nearest Neighbor (K- NN). Hasilnya akan menjadi data-data penelitian dalam paper ini. Diharapkan menggunakan metode SIFT dan SURF akan memberikan hasil dengan tingkat keakurasian yang tinggi.</strong></p><p><em><strong>Kata Kunci</strong> – Biometric, Footprint, SURF, SIFT, K- NN</em></p><p><em>Abstract</em><strong> - Biometric is a method used to identify indivduals using their physical features. One of the physical features that can be used for biometric is the footprint. The footprint was chosen because of having a high level of uniqueness where it is almost impossible to find two individuals that have the same footprint. Feature extraction methods such as Scale Invariant Feature Transform (SIFT) and Speed Up Robust Feature (SURF) are appropriate if used for footprint identification system. The steps used in obtaining descriptor start from scanning the footprint, pre-processing, feature extraction using SURF and SIFT and last the matching process. The comparison between the two methods will be observed by their accuracy. The K-Nearest Neighbor (K-NN) algorithm will be used for the classification process. The outputs will be used for research data in this research proposal. It will be expected that using SIFT and SURF for the feature extraction will result in high accuracy.</strong></p><p><em><strong>Keywords</strong> – Biometric, Footprint, SURF, SIFT, K- NN</em></p>
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Dissertations / Theses on the topic "Scale-Invariant-Feature-Transform (SIFT)"

1

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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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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4

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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5

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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6

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

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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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7

Dellinger, Flora. "Descripteurs locaux pour l'imagerie radar et applications." Electronic Thesis or Diss., Paris, ENST, 2014. http://www.theses.fr/2014ENST0037.

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Abstract:
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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8

Leoputra, Wilson Suryajaya. "Video foreground extraction for mobile camera platforms." Thesis, Curtin University, 2009. http://hdl.handle.net/20.500.11937/1384.

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Foreground object detection is a fundamental task in computer vision with many applications in areas such as object tracking, event identification, and behavior analysis. Most conventional foreground object detection methods work only in a stable illumination environments using fixed cameras. In real-world applications, however, it is often the case that the algorithm needs to operate under the following challenging conditions: drastic lighting changes, object shape complexity, moving cameras, low frame capture rates, and low resolution images. This thesis presents four novel approaches for foreground object detection on real-world datasets using cameras deployed on moving vehicles.The first problem addresses passenger detection and tracking tasks for public transport buses investigating the problem of changing illumination conditions and low frame capture rates. Our approach integrates a stable SIFT (Scale Invariant Feature Transform) background seat modelling method with a human shape model into a weighted Bayesian framework to detect passengers. To deal with the problem of tracking multiple targets, we employ the Reversible Jump Monte Carlo Markov Chain tracking algorithm. Using the SVM classifier, the appearance transformation models capture changes in the appearance of the foreground objects across two consecutives frames under low frame rate conditions. In the second problem, we present a system for pedestrian detection involving scenes captured by a mobile bus surveillance system. It integrates scene localization, foreground-background separation, and pedestrian detection modules into a unified detection framework. The scene localization module performs a two stage clustering of the video data.In the first stage, SIFT Homography is applied to cluster frames in terms of their structural similarity, and the second stage further clusters these aligned frames according to consistency in illumination. This produces clusters of images that are differential in viewpoint and lighting. A kernel density estimation (KDE) technique for colour and gradient is then used to construct background models for each image cluster, which is further used to detect candidate foreground pixels. Finally, using a hierarchical template matching approach, pedestrians can be detected.In addition to the second problem, we present three direct pedestrian detection methods that extend the HOG (Histogram of Oriented Gradient) techniques (Dalal and Triggs, 2005) and provide a comparative evaluation of these approaches. The three approaches include: a) a new histogram feature, that is formed by the weighted sum of both the gradient magnitude and the filter responses from a set of elongated Gaussian filters (Leung and Malik, 2001) corresponding to the quantised orientation, which we refer to as the Histogram of Oriented Gradient Banks (HOGB) approach; b) the codebook based HOG feature with branch-and-bound (efficient subwindow search) algorithm (Lampert et al., 2008) and; c) the codebook based HOGB approach.In the third problem, a unified framework that combines 3D and 2D background modelling is proposed to detect scene changes using a camera mounted on a moving vehicle. The 3D scene is first reconstructed from a set of videos taken at different times. The 3D background modelling identifies inconsistent scene structures as foreground objects. For the 2D approach, foreground objects are detected using the spatio-temporal MRF algorithm. Finally, the 3D and 2D results are combined using morphological operations.The significance of these research is that it provides basic frameworks for automatic large-scale mobile surveillance applications and facilitates many higher-level applications such as object tracking and behaviour analysis.
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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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Book chapters on the topic "Scale-Invariant-Feature-Transform (SIFT)"

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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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Burger, Wilhelm, and Mark J. Burge. "Scale-Invariant Feature Transform (SIFT)." In Texts in Computer Science, 709–63. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-05744-1_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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Chowdhary, Chiranji Lal. "Application of Object Recognition With Shape-Index Identification and 2D Scale Invariant Feature Transform for Key-Point Detection." In Feature Dimension Reduction for Content-Based Image Identification, 218–31. IGI Global, 2018. http://dx.doi.org/10.4018/978-1-5225-5775-3.ch012.

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Humans make object recognition look inconsequential. In this chapter, scale-invariant feature extraction and shape-index depiction are used on a range of images for identifying objects. The shape-index is attained and used as a local descriptor or key-point descriptor. First surface properties for shape index identification and second as 2D scale invariant feature transformed for key-point detection and feature extraction. The object recognition classification is compared results with shape-index identification and 2D scale-invariant feature transform for key-point detection with SIFT and SURF. The authors are using images from the ImageNet dataset, and with use of shift-index + SIFT descriptors, they are finding better accuracy at the classification stage.
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Park, Jae-Han, Kyung-Wook Park, Seung-Ho Baeg, and Moon-Hong Baeg. "pi-SIFT: A Photometric and Scale Invariant Feature Transform." In Pattern Recognition Recent Advances. InTech, 2010. http://dx.doi.org/10.5772/9346.

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Govindarajan, Satyavratan, and Ramakrishnan Swaminathan. "Performance of SURF and SIFT Keypoints for the Automated Differentiation of Abnormality in Chest Radiographs." In Studies in Health Technology and Informatics. IOS Press, 2021. http://dx.doi.org/10.3233/shti210219.

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In this work, automated abnormality detection using keypoint information from Speeded-Up Robust feature (SURF) and Scale Invariant Feature Transform (SIFT) descriptors in chest Radiographic (CR) images is investigated and compared. Computerized image analysis using artificial intelligence is crucial to detect subtle and non-specific alterations of Tuberculosis (TB). For this, the healthy and TB CRs are subjected to lung field segmentation. SURF and SIFT keypoints are extracted from the segmented lung images. Statistical features from keypoints, its scale and orientation are computed. Discrimination of TB from healthy is performed using SVM. Results show that the SURF and SIFT methods are able to extract local keypoint information in CRs. Linear SVM is found to perform better with precision of 88.9% and AUC of 91% in TB detection for combined features. Hence, the application of keypoint techniques is found to have clinical relevance in the automated screening of non-specific TB abnormalities using CRs.
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Das, Tapan Kumar. "Logo Matching and Recognition Based on Context." In Feature Dimension Reduction for Content-Based Image Identification, 164–76. IGI Global, 2018. http://dx.doi.org/10.4018/978-1-5225-5775-3.ch009.

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Logos are graphic productions that recall some real-world objects or emphasize a name, simply display some abstract signs that have strong perceptual appeal. Color may have some relevance to assess the logo identity. Different logos may have a similar layout with slightly different spatial disposition of the graphic elements, localized differences in the orientation, size and shape, or differ by the presence/absence of one or few traits. In this chapter, the author uses ensemble-based framework to choose the best combination of preprocessing methods and candidate extractors. The proposed system has reference logos and test logos which are verified depending on some features like regions, pre-processing, key points. These features are extracted by using gray scale image by scale-invariant feature transform (SIFT) and Affine-SIFT (ASIFT) descriptor method. Pre-processing phase employs four different filters. Key points extraction is carried by SIFT and ASIFT algorithm. Key points are matched to recognize fake logo.
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Salahat, Ehab Najeh, and Murad Qasaimeh. "Recent Advances in Feature Extraction and Description Algorithms." In Computer Vision, 27–57. IGI Global, 2018. http://dx.doi.org/10.4018/978-1-5225-5204-8.ch002.

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Computer vision is one of the most active research fields in technology today. Giving machines the ability to see and comprehend the world at the speed of sight creates endless applications and opportunities. Feature detection and description algorithms are considered as the retina for machine vision. However, most of these algorithms are typically computationally intensive, which prevents them from achieving real-time performance. As such, embedded vision accelerators (FPGA, ASIC, etc.) can be targeted due to their inherent parallelizability. This chapter provides a comprehensive study on some of the recent feature detection and description algorithms and their hardware solutions. Specifically, it begins with a synopsis on basic concepts followed by a comparative study, from which the maximally stable extremal regions (MSER) and the scale invariant feature transform (SIFT) algorithms are selected for further analysis due to their robust performance. The chapter then reports some of their recent algorithmic derivatives and highlights their recent hardware designs and architectures.
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Conference papers on the topic "Scale-Invariant-Feature-Transform (SIFT)"

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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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Kaduhm, Haider S., and Hameed M. Abduljabbar. "Texture image classification using scale invariant feature transform (SIFT) method." In TECHNOLOGIES AND MATERIALS FOR RENEWABLE ENERGY, ENVIRONMENT AND SUSTAINABILITY: TMREES22Fr. AIP Publishing, 2023. http://dx.doi.org/10.1063/5.0129552.

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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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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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