Relevant bibliographies by topics / SIFT Feature Descriptor

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  2. Dissertations / Theses
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Academic literature on the topic 'SIFT Feature Descriptor'

Author: Grafiati
Published: 3 June 2025
Last updated: 19 July 2025

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Journal articles on the topic "SIFT Feature Descriptor"

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Ming-liang Gao, Ming-liang Gao, Xiaomin Yang Xiaomin Yang, Yanmei Yu Yanmei Yu, and Daisheng Luo Daisheng Luo. "Photometric invariant feature descriptor based on SIFT." Chinese Optics Letters 10, s1 (2012): S11003–311008. http://dx.doi.org/10.3788/col201210.s11003.

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D, Hema, and Kannan S. "Patch-SIFT: Enhanced feature descriptor to learn human facial emotions using an Ensemble approach." Indian Journal of Science and Technology 14, no. 21 (2021): 1740–47. https://doi.org/10.17485/IJST/v14i21.2261.

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Abstract <strong>Background:</strong>&nbsp;Having experienced more than a year of pandemic, a variety of applications such as online classrooms, virtual office meetings, conferences, online games, Social media &amp; Networks, Mobile applications, and many other infotainment areas have made humans live with gadgets and respond to them. However, all these applications have an impact on human behavioral transformation. It is very significant for employers to understand the emotions of their employees in the era of online office &amp; work from home concept to increase productivity. Learning and identifying emotions from the human face has its application in all online portals when physical contact could not be achieved.&nbsp;<strong>Ojbective:</strong>&nbsp;Human Facial emotions can be learned using enormous feature descriptors that extract image features. While local feature descriptors retrieve pixel-level information, global feature descriptors extract the overall image information. Both of the feature descriptors quantify the image information, however, they don&rsquo;t provide complete and relevant information. Hence, this research work aims to improve the existing local feature descriptor to perform globally for emotion recognition.&nbsp;<strong>Method:</strong>&nbsp;Our proposed feature descriptor, Patch-SIFT collects features from multiple patches within an image. This strategy is evolved to globally apply the local feature descriptor as a hybridization paradigm. The extracted features are trained and tested on an ensemble model.&nbsp;<strong>Findings:</strong>&nbsp;The Proposed Feature descriptor (Patch-SIFT) performance with ensemble model is found to produce an improved accuracy of 98% compared with existing feature descriptors and Machine learning classifiers.&nbsp;<strong>Novelty:</strong>&nbsp;This research work tries to evolve a new Feature descriptor algorithm based on SIFT algorithm for an efficient emotion recognition system that works without the need for any additional GPU or huge dataset. <strong>Keywords</strong> Classification, Ensemble, Feature descriptor, Patch&shy;SIFT &nbsp;
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Zhang, Wanyuan, Tian Zhou, Chao Xu, and Meiqin Liu. "A SIFT-Like Feature Detector and Descriptor for Multibeam Sonar Imaging." Journal of Sensors 2021 (July 15, 2021): 1–14. http://dx.doi.org/10.1155/2021/8845814.

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Multibeam imaging sonar has become an increasingly important tool in the field of underwater object detection and description. In recent years, the scale-invariant feature transform (SIFT) algorithm has been widely adopted to obtain stable features of objects in sonar images but does not perform well on multibeam sonar images due to its sensitivity to speckle noise. In this paper, we introduce MBS-SIFT, a SIFT-like feature detector and descriptor for multibeam sonar images. This algorithm contains a feature detector followed by a local feature descriptor. A new gradient definition robust to speckle noise is presented to detect extrema in scale space, and then, interest points are filtered and located. It is also used to assign orientation and generate descriptors of interest points. Simulations and experiments demonstrate that the proposed method can capture features of underwater objects more accurately than existing approaches.
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Anbarasu, B., and G. Anitha. "Indoor Scene recognition for Micro Aerial Vehicles Navigation using Enhanced SIFT-ScSPM Descriptors." Journal of Navigation 73, no. 1 (2019): 37–55. http://dx.doi.org/10.1017/s0373463319000420.

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In this paper, a new scene recognition visual descriptor called Enhanced Scale Invariant Feature Transform-based Sparse coding Spatial Pyramid Matching (Enhanced SIFT-ScSPM) descriptor is proposed by combining a Bag of Words (BOW)-based visual descriptor (SIFT-ScSPM) and Gist-based descriptors (Enhanced Gist-Enhanced multichannel Gist (Enhanced mGist)). Indoor scene classification is carried out by multi-class linear and non-linear Support Vector Machine (SVM) classifiers. Feature extraction methodology and critical review of several visual descriptors used for indoor scene recognition in terms of experimental perspectives have been discussed in this paper. An empirical study is conducted on the Massachusetts Institute of Technology (MIT) 67 indoor scene classification data set and assessed the classification accuracy of state-of-the-art visual descriptors and the proposed Enhanced mGist, Speeded Up Robust Features-Spatial Pyramid Matching (SURF-SPM) and Enhanced SIFT-ScSPM visual descriptors. Experimental results show that the proposed Enhanced SIFT-ScSPM visual descriptor performs better with higher classification rate, precision, recall and area under the Receiver Operating Characteristic (ROC) curve values with respect to the state-of-the-art and the proposed Enhanced mGist and SURF-SPM visual descriptors.
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Cao, Lei, Di Liao, and Bin Dang Xue. "Reference Point-Based SIFT Feature Matching." Applied Mechanics and Materials 543-547 (March 2014): 2670–73. http://dx.doi.org/10.4028/www.scientific.net/amm.543-547.2670.

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Aiming to solve the high computational and time consuming problem in SIFT feature matching, this paper presents an improved SIFT feature matching algorithm based on reference point. The algorithm starts from selecting a suitable reference point in the feature descriptor space when SIFT features are extracted. In the feature matching stage, this paper uses the Euclidean distance between descriptor vectors of the feature point to be matched and the reference point to make a fast filtration which removes most of the features that could not be matched. For the remaining SIFT features, Best-bin-first (BBF) algrithm is utilized to obtain precise matches. Experimental results demonstrate that the proposed matching algorithm achieves good effectiveness in image matching, and takes only about 60 percent of the time that the traditional matching algorithm takes.
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Yang, Yao, Jinkang Wei, Ximing Zhan, and Xikui Miao. "A novel method for SIFT features matching based on feature dimension matching degree." MATEC Web of Conferences 277 (2019): 02027. http://dx.doi.org/10.1051/matecconf/201927702027.

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We proposes a method for fast matching SIFT feature points based on SIFT feature descriptor vector element matching. First, we discretize each dimensional feature element into an array address based on a fixed threshold value and store the corresponding feature point labels in an address. If the same dimensional feature element of the descriptor vector has the same discrete value, their feature point labels may fall into the same address. Secondly, we search the mapping address of the feature descriptor vector element to obtain the matching state of the corresponding dimensions of the feature descriptor vector, thus obtaining the number of dimensions matching between feature points and feature dimension matching degree. Then we use the feature dimension matching degree to obtain the suspect matching feature points. Finally we use the Euclidean distance to eliminate the mismatching feature points to obtain accurate matching feature point pairs. The method is essentially a high-dimensional feature vector matching method based on local feature vector element matching. Experimental results show that the new algorithm can guarantee the number of matching SIFT feature points and their matching accuracy and that its running time is similar to that of HKMT, RKDT and LSH algorithms
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Yuan, Wenan, Sai Raghavendra Prasad Poosa, and Rutger Francisco Dirks. "Comparative Analysis of Color Space and Channel, Detector, and Descriptor for Feature-Based Image Registration." Journal of Imaging 10, no. 5 (2024): 105. http://dx.doi.org/10.3390/jimaging10050105.

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The current study aimed to quantify the value of color spaces and channels as a potential superior replacement for standard grayscale images, as well as the relative performance of open-source detectors and descriptors for general feature-based image registration purposes, based on a large benchmark dataset. The public dataset UDIS-D, with 1106 diverse image pairs, was selected. In total, 21 color spaces or channels including RGB, XYZ, Y′CrCb, HLS, L*a*b* and their corresponding channels in addition to grayscale, nine feature detectors including AKAZE, BRISK, CSE, FAST, HL, KAZE, ORB, SIFT, and TBMR, and 11 feature descriptors including AKAZE, BB, BRIEF, BRISK, DAISY, FREAK, KAZE, LATCH, ORB, SIFT, and VGG were evaluated according to reprojection error (RE), root mean square error (RMSE), structural similarity index measure (SSIM), registration failure rate, and feature number, based on 1,950,984 image registrations. No meaningful benefits from color space or channel were observed, although XYZ, RGB color space and L* color channel were able to outperform grayscale by a very minor margin. Per the dataset, the best-performing color space or channel, detector, and descriptor were XYZ/RGB, SIFT/FAST, and AKAZE. The most robust color space or channel, detector, and descriptor were L*a*b*, TBMR, and VGG. The color channel, detector, and descriptor with the most initial detector features and final homography features were Z/L*, FAST, and KAZE. In terms of the best overall unfailing combinations, XYZ/RGB+SIFT/FAST+VGG/SIFT seemed to provide the highest image registration quality, while Z+FAST+VGG provided the most image features.
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Hagiwara, Hayato, Yasufumi Touma, Kenichi Asami, and Mochimitsu Komori. "FPGA-Based Stereo Vision System Using Gradient Feature Correspondence." Journal of Robotics and Mechatronics 27, no. 6 (2015): 681–90. http://dx.doi.org/10.20965/jrm.2015.p0681.

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&lt;div class=""abs_img""&gt;&lt;img src=""[disp_template_path]/JRM/abst-image/00270006/10.jpg"" width=""300"" /&gt; Mobile robot with a stereo vision&lt;/div&gt;This paper describes an autonomous mobile robot stereo vision system that uses gradient feature correspondence and local image feature computation on a field programmable gate array (FPGA). Among several studies on interest point detectors and descriptors for having a mobile robot navigate are the Harris operator and scale-invariant feature transform (SIFT). Most of these require heavy computation, however, and using them may burden some computers. Our purpose here is to present an interest point detector and a descriptor suitable for FPGA implementation. Results show that a detector using gradient variance inspection performs faster than SIFT or speeded-up robust features (SURF), and is more robust against illumination changes than any other method compared in this study. A descriptor with a hierarchical gradient structure has a simpler algorithm than SIFT and SURF descriptors, and the result of stereo matching achieves better performance than SIFT or SURF.
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Yu, Yang, Yong Ma, Xiaoguang Mei, Fan Fan, Jun Huang, and Jiayi Ma. "A Spatial-Spectral Feature Descriptor for Hyperspectral Image Matching." Remote Sensing 13, no. 23 (2021): 4912. http://dx.doi.org/10.3390/rs13234912.

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Hyperspectral Images (HSIs) have been utilized in many fields which contain spatial and spectral features of objects simultaneously. Hyperspectral image matching is a fundamental and critical problem in a wide range of HSI applications. Feature descriptors for grayscale image matching are well studied, but few descriptors are elaborately designed for HSI matching. HSI descriptors, which should have made good use of the spectral feature, are essential in HSI matching tasks. Therefore, this paper presents a descriptor for HSI matching, called HOSG-SIFT, which ensembles spectral features with spatial features of objects. First, we obtain the grayscale image by dimensional reduction from HSI and apply it to extract keypoints and descriptors of spatial features. Second, the descriptors of spectral features are designed based on the histogram of the spectral gradient (HOSG), which effectively preserves the physical significance of the spectral profile. Third, we concatenate the spatial descriptors and spectral descriptors with the same weights into a new descriptor and apply it for HSI matching. Experimental results demonstrate that the proposed HOSG-SIFT performs superior against traditional feature descriptors.
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Saleem, Sajid, and Abdul Bais. "Visible Spectrum and Infra-Red Image Matching: A New Method." Applied Sciences 10, no. 3 (2020): 1162. http://dx.doi.org/10.3390/app10031162.

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Textural and intensity changes between Visible Spectrum (VS) and Infra-Red (IR) images degrade the performance of feature points. We propose a new method based on a regression technique to overcome this problem. The proposed method consists of three main steps. In the first step, feature points are detected from VS-IR images and Modified Normalized (MN)-Scale Invariant Feature Transform (SIFT) descriptors are computed. In the second step, correct MN-SIFT descriptor matches are identified between VS-IR images with projection error. A regression model is trained on correct MN-SIFT descriptors. In the third step, the regression model is used to process the MN-SIFT descriptors of test VS images in order to remove misalignment with the MN-SIFT descriptors of test IR images and to overcome textural and intensity changes. Experiments are performed on two different VS-IR image datasets. The experimental results show that the proposed method works really well and demonstrates on average 14% and 15% better precision and matching scores compared to recently proposed Histograms of Directional Maps (HoDM) descriptor.
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Dissertations / Theses on the topic "SIFT Feature Descriptor"

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Piemontese, Cristiano. "Progettazione e implementazione di una applicazione didattica interattiva per il riconoscimento di oggetti basata sull'algoritmo SIFT." Bachelor's thesis, Alma Mater Studiorum - Università di Bologna, 2016. http://amslaurea.unibo.it/10883/.

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Nell'elaborato viene introdotto l'ambito della Computer Vision e come l'algoritmo SIFT si inserisce nel suo panorama. Viene inoltre descritto SIFT stesso, le varie fasi di cui si compone e un'applicazione al problema dell'object recognition. Infine viene presentata un'implementazione di SIFT in linguaggio Python creata per ottenere un'applicazione didattica interattiva e vengono mostrati esempi di questa applicazione.
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González, Valenzuela Ricardo Eugenio 1984. "Linear dimensionality reduction applied to SIFT and SURF feature descriptors." [s.n.], 2014. http://repositorio.unicamp.br/jspui/handle/REPOSIP/275499.

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Orientadores: Hélio Pedrini, William Robson Schwartz<br>Dissertação (mestrado) - Universidade Estadual de Campinas, Instituto de Computação<br>Made available in DSpace on 2018-08-24T12:45:45Z (GMT). No. of bitstreams: 1 GonzalezValenzuela_RicardoEugenio_M.pdf: 22940228 bytes, checksum: 972bc5a0fac686d7eda4da043bbd61ab (MD5) Previous issue date: 2014<br>Resumo: Descritores locais robustos normalmente compõem-se de vetores de características de alta dimensionalidade para descrever atributos discriminativos em imagens. A alta dimensionalidade de um vetor de características implica custos consideráveis em termos de tempo computacional e requisitos de armazenamento afetando o desempenho de várias tarefas que utilizam descritores de características, tais como correspondência, recuperação e classificação de imagens. Para resolver esses problemas, pode-se aplicar algumas técnicas de redução de dimensionalidade, escencialmente, construindo uma matrix de projeção que explique adequadamente a importancia dos dados em outras bases. Esta dissertação visa aplicar técnicas de redução linear de dimensionalidade aos descritores SIFT e SURF. Seu principal objetivo é demonstrar que, mesmo com o risco de diminuir a precisão dos vetores de caraterísticas, a redução de dimensionalidade pode resultar em um equilíbrio adequado entre tempo computacional e recursos de armazenamento. A redução linear de dimensionalidade é realizada por meio de técnicas como projeções aleatórias (RP), análise de componentes principais (PCA), análise linear discriminante (LDA) e mínimos quadrados parciais (PLS), a fim de criar vetores de características de menor dimensão. Este trabalho avalia os vetores de características reduzidos em aplicações de correspondência e de recuperação de imagens. O tempo computacional e o uso de memória são medidos por comparações entre os vetores de características originais e reduzidos<br>Abstract: Robust local descriptors usually consist of high dimensional feature vectors to describe distinctive characteristics of images. The high dimensionality of a feature vector incurs into considerable costs in terms of computational time and storage requirements, which affects the performance of several tasks that employ feature vectors, such as matching, image retrieval and classification. To address these problems, it is possible to apply some dimensionality reduction techniques, by building a projection matrix which explains adequately the importance of the data in other basis. This dissertation aims at applying linear dimensionality reduction to SIFT and SURF descriptors. Its main objective is to demonstrate that, even risking to decrease the accuracy of the feature vectors, the dimensionality reduction can result in a satisfactory trade-off between computational time and storage. We perform the linear dimensionality reduction through Random Projections (RP), Independent Component Analysis (ICA), Principal Component Analysis (PCA), Linear Discriminant Analysis (LDA) and Partial Least Squares (PLS) in order to create lower dimensional feature vectors. This work evaluates such reduced feature vectors in a matching application, as well as their distinctiveness in an image retrieval application. The computational time and memory usage are then measured by comparing the original and the reduced feature vectors. OBSERVAÇÃONa segunda folha, do arquivo em anexo, o meu nome tem dois pequenos erros<br>Mestrado<br>Ciência da Computação<br>Mestre em Ciência da Computação
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Žilka, Filip. "Detektory a deskriptory oblastí v obrazu." Master's thesis, Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií, 2016. http://www.nusl.cz/ntk/nusl-240912.

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This master’s thesis deals with an important part of computer vision field. Main focus of this thesis is on feature detectors and descriptors in an image. Throughout the thesis the simplest feature detectors like Moravec detector will be presented, building up to more complex detectors like MSER or FAST. The purpose of feature descriptors is in a mathematical description of these points. We begin with the oldest ones like SIFT and move on to newest and best performing descriptors like FREAK or ORB. The major objective of the thesis is comparison of presented methods on licence plate localization task.
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Zhu, Chao. "Effective and efficient visual description based on local binary patterns and gradient distribution for object recognition." Phd thesis, Ecole Centrale de Lyon, 2012. http://tel.archives-ouvertes.fr/tel-00755644.

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Cette thèse est consacrée au problème de la reconnaissance visuelle des objets basé sur l'ordinateur, qui est devenue un sujet de recherche très populaire et important ces dernières années grâce à ses nombreuses applications comme l'indexation et la recherche d'image et de vidéo , le contrôle d'accès de sécurité, la surveillance vidéo, etc. Malgré beaucoup d'efforts et de progrès qui ont été fait pendant les dernières années, il reste un problème ouvert et est encore considéré comme l'un des problèmes les plus difficiles dans la communauté de vision par ordinateur, principalement en raison des similarités entre les classes et des variations intra-classe comme occlusion, clutter de fond, les changements de point de vue, pose, l'échelle et l'éclairage. Les approches populaires d'aujourd'hui pour la reconnaissance des objets sont basé sur les descripteurs et les classiffieurs, ce qui généralement extrait des descripteurs visuelles dans les images et les vidéos d'abord, et puis effectue la classification en utilisant des algorithmes d'apprentissage automatique sur la base des caractéristiques extraites. Ainsi, il est important de concevoir une bonne description visuelle, qui devrait être à la fois discriminatoire et efficace à calcul, tout en possédant certaines propriétés de robustesse contre les variations mentionnées précédemment. Dans ce contexte, l'objectif de cette thèse est de proposer des contributions novatrices pour la tâche de la reconnaissance visuelle des objets, en particulier de présenter plusieurs nouveaux descripteurs visuelles qui représentent effectivement et efficacement le contenu visuel d'image et de vidéo pour la reconnaissance des objets. Les descripteurs proposés ont l'intention de capturer l'information visuelle sous aspects différents. Tout d'abord, nous proposons six caractéristiques LBP couleurs de multi-échelle pour traiter les défauts principaux du LBP original, c'est-à-dire, le déffcit d'information de couleur et la sensibilité aux variations des conditions d'éclairage non-monotoniques. En étendant le LBP original à la forme de multi-échelle dans les différents espaces de couleur, les caractéristiques proposées non seulement ont plus de puissance discriminante par l'obtention de plus d'information locale, mais possèdent également certaines propriétés d'invariance aux différentes variations des conditions d'éclairage. En plus, leurs performances sont encore améliorées en appliquant une stratégie de l'image division grossière à fine pour calculer les caractéristiques proposées dans les blocs d'image afin de coder l'information spatiale des structures de texture. Les caractéristiques proposées capturent la distribution mondiale de l'information de texture dans les images. Deuxièmement, nous proposons une nouvelle méthode pour réduire la dimensionnalité du LBP appelée la combinaison orthogonale de LBP (OC-LBP). Elle est adoptée pour construire un nouveau descripteur local basé sur la distribution en suivant une manière similaire à SIFT. Notre objectif est de construire un descripteur local plus efficace en remplaçant l'information de gradient coûteux par des patterns de texture locales dans le régime du SIFT. Comme l'extension de notre première contribution, nous étendons également le descripteur OC-LBP aux différents espaces de couleur et proposons six descripteurs OC-LBP couleurs pour améliorer la puissance discriminante et la propriété d'invariance photométrique du descripteur basé sur l'intensité. Les descripteurs proposés capturent la distribution locale de l'information de texture dans les images. Troisièmement, nous introduisons DAISY, un nouveau descripteur local rapide basé sur la distribution de gradient, dans le domaine de la reconnaissance visuelle des objets. [...]
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Němec, Lukáš. "Lokalizace mobilního robota v prostředí." Master's thesis, Vysoké učení technické v Brně. Fakulta informačních technologií, 2016. http://www.nusl.cz/ntk/nusl-255455.

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This paper addresses the problem of mobile robot localization based on current 2D and 3D data and previous records. Focusing on practical loop detection in the trajectory of a robot. The objective of this work was to evaluate current methods of image processing and depth data for issues of localization in environment. This work uses Bag of Words for 2D data and environment of point cloud with Viewpoint Feature Histogram for 3D data. Designed system was implemented and evaluated.
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Hubený, Marek. "Koncepty strojového učení pro kategorizaci objektů v obrazu." Master's thesis, Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií, 2017. http://www.nusl.cz/ntk/nusl-316388.

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This work is focused on objects and scenes recognition using machine learning and computer vision tools. Before the solution of this problem has been studied basic phases of the machine learning concept and statistical models with accent on their division into discriminative and generative method. Further, the Bag-of-words method and its modification have been investigated and described. In the practical part of this work, the implementation of the Bag-of-words method with the SVM classifier was created in the Matlab environment and the model was tested on various sets of publicly available images.
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Li, Jui-Wen, and 李瑞文. "Effective Macro and Micro Feature Descriptor Based on SIFT for Image Retrieving and Classification." Thesis, 2013. http://ndltd.ncl.edu.tw/handle/35265126497236011695.

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碩士<br>義守大學<br>資訊工程學系<br>101<br>With the development of the information technology, multimedia database has been growing rapidly in recent year. Therefore, it is more important to develop an effectively image retrieval system to manage enormous multimedia database. SIFT has been proven to be the most robust local invariant feature descriptor, which is extensively used in image retrieval and classification. We adopted SIFT to extract image feature points as micro feature information. In this thesis, we divide image content information into micro information and macro information . Firstly, we use SIFT to extract feature points as our micro information. In order to extract background information effectively, such as sky, ocean , etc…, so we exploit keyblock to extract the flat part of image as macro feature information.
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GUPTA, ANKITA. "PERSONAL MULTIMODAL BIOMETRIC AUTHENTICATION USING UNSUPERVISED LEARNING, HIDDEN MARKOV MODEL (HMM)." Thesis, 2016. http://dspace.dtu.ac.in:8080/jspui/handle/repository/14543.

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ABSTRACT Biometric authentication systems have been used since decades. Palmprint and finger knuckle prints are two such modalities that are universal and possess uniqueness. A variety of algorithms are available to extract features from these modalities and do the authentication process. In this report, use of a machine learning, unsupervised Hidden Markov Model algorithm is proposed to classify the users into genuine and imposter classes. In the following report, a multimodal system using palmprint and finger knuckle print has been proposed using a combination of Harris Corner Detector; SIFT descriptors and Continuous Density Hidden Markov Model (CDHMM). Here the states defining the origination of the observation feature vectors are hidden. The features are extracted using Harris Corner Detector and are described using Scale Invariant Feature Descriptor (SIFT). An approach is proposed to do the authentication at feature level as well as at score level. The log-likelihood computed by HMM and the parameters are maximised by Expectation-Maximization Algorithm. An iterative approach is used to increase the authentication rates and to get the correct number of states in each Hidden Markov Model of each user at feature level and for genuine and imposter classes at score level. The various fusion methods at score level are experimented for the PolyU, IITD palmprint and PolyU finger knuckle print database. The authentication rates obtained are as high as 99% GAR at 0.01 FAR for PolyU palmprint database that are comparable to other methods of authentication at score level. The highest GAR was recorded using SUM fusion rule. The authentication rates are high for feature level authentication as well for both knuckle prints and palmprints. GAR was recorded as high as 97% for right middle knuckle finger print at 0.01 FAR.
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Gat, Christopher. "Feature-based matching in historic repeat photography: an evaluation and assessment of feasibility." Thesis, 2011. http://hdl.handle.net/1828/3461.

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This study reports on the quantitative evaluation of a set of state-of-the-art feature detectors and descriptors in the context of repeat photography. Unlike most related work, the proposed study assesses the performance of feature detectors when intra-pair variations are uncontrolled and due to a variety of factors (landscape change, weather conditions, different acquisition sensors). There is no systematic way to model the factors inducing image change. The proposed evaluation is performed in the context of image matching, i.e. in conjunction with a descriptor and matching strategy. Thus, beyond just comparing the performance of these detectors and descriptors, we also examine the feasibility of feature-based matching on repeat photography. Our dataset consists of a set of repeat and historic images pairs that are representative for the database created by the Mountain Legacy Project www.mountainlegacy.ca.<br>Graduate
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Book chapters on the topic "SIFT Feature Descriptor"

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Li, Jing, and Zhaoyang Lu. "B-SIFT: A Highly Efficient Binary SIFT Descriptor for Invariant Feature Correspondence." In Intelligent Science and Intelligent Data Engineering. Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-31919-8_55.

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Ju, Lei, Ke Xie, Hao Zheng, Baochang Zhang, and Wankou Yang. "GPCA-SIFT: A New Local Feature Descriptor for Scene Image Classification." In Communications in Computer and Information Science. Springer Singapore, 2016. http://dx.doi.org/10.1007/978-981-10-3005-5_24.

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Mani Kumar, Nerella Arun, and P. S. Sathidevi. "Wavelet SIFT Feature Descriptors for Robust Face Recognition." In Advances in Computing and Information Technology. Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-31552-7_87.

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Pui, SukTing, and Jacey-Lynn Minoi. "Keypoint Descriptors in SIFT and SURF for Face Feature Extractions." In Lecture Notes in Electrical Engineering. Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-10-8276-4_7.

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Sanromà, Gerard, René Alquézar, and Francesc Serratosa. "Attributed Graph Matching for Image-Features Association Using SIFT Descriptors." In Lecture Notes in Computer Science. Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-14980-1_24.

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Quy, Nguyen Hong, Nguyen Hoang Quoc, Nguyen Tran Lan Anh, Hyung-Jeong Yang, and Pham The Bao. "3D Human Face Recognition Using Sift Descriptors of Face’s Feature Regions." In New Trends in Computational Collective Intelligence. Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-10774-5_11.

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S., Rakesh, Kailash Atal, Ashish Arora, Pulak Purkait, and Bhabatosh Chanda. "Face Image Retrieval Based on Probe Sketch Using SIFT Feature Descriptors." In Perception and Machine Intelligence. Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-27387-2_7.

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Pai, Sneha, and Ramesha Shettigar. "Gender Recognition from Face Images Using SIFT Descriptors and Trainable Features." In Advances in Intelligent Systems and Computing. Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-3514-7_87.

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Doan, Dung A., Ngoc-Trung Tran, Dinh-Phong Vo, Bac Le, and Atsuo Yoshitaka. "Combining Descriptors Extracted from Feature Maps of Deconvolutional Networks and SIFT Descriptors in Scene Image Classification." In Lecture Notes in Computer Science. Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-39640-3_24.

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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. 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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Conference papers on the topic "SIFT Feature Descriptor"

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Ni, Zhen-Sheng. "B-SIFT: A Binary SIFT Based Local Image Feature Descriptor." In 2012 4th International Conference on Digital Home (ICDH). IEEE, 2012. http://dx.doi.org/10.1109/icdh.2012.69.

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Guo, He, Kai Zhang, and Qi Jia. "2.5D SIFT Descriptor for Facial Feature Extraction." In 2010 Sixth International Conference on Intelligent Information Hiding and Multimedia Signal Processing (IIH-MSP). IEEE, 2010. http://dx.doi.org/10.1109/iihmsp.2010.183.

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Meixi, Chen, Yuan Yule, and Zhao Yong. "KAZE Feature Point with Modified-SIFT Descriptor." In 3rd International Conference on Multimedia Technology(ICMT-13). Atlantis Press, 2013. http://dx.doi.org/10.2991/icmt-13.2013.153.

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Wei, Mao, and Peng Xiwei. "WLIB-SIFT: A Distinctive Local Image Feature Descriptor." In 2019 IEEE 2nd International Conference on Information Communication and Signal Processing (ICICSP). IEEE, 2019. http://dx.doi.org/10.1109/icicsp48821.2019.8958587.

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Liao, Kaiyang, and Guizhong Liu. "An improved local feature descriptor based on SIFT." In the Second International Conference. ACM Press, 2010. http://dx.doi.org/10.1145/1937728.1937759.

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Jansen, Sybren, Amirhosein Shantia, and Marco A. Wiering. "The neural-SIFT feature descriptor for visual vocabulary object recognition." In 2015 International Joint Conference on Neural Networks (IJCNN). IEEE, 2015. http://dx.doi.org/10.1109/ijcnn.2015.7280660.

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Bermudez, Francely Franco, Christian Santana Diaz, Sheneeka Ward, Rafael Radkowski, Timothy Garrett, and James Oliver. "Comparison of Natural Feature Descriptors for Rigid-Object Tracking for Real-Time Augmented Reality." In ASME 2014 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2014. http://dx.doi.org/10.1115/detc2014-35319.

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This paper presents a comparison of natural feature descriptors for rigid object tracking for augmented reality (AR) applications. AR relies on object tracking in order to identify a physical object and to superimpose virtual object on an object. Natural feature tracking (NFT) is one approach for computer vision-based object tracking. NFT utilizes interest points of a physcial object, represents them as descriptors, and matches the descriptors against reference descriptors in order to identify a phsical object to track. In this research, we investigate four different natural feature descriptors (SIFT, SURF, FREAK, ORB) and their capability to track rigid objects. Rigid objects need robust descriptors since they need to describe the objects in a 3D space. AR applications are also real-time application, thus, fast feature matching is mandatory. FREAK and ORB are binary descriptors, which promise a higher performance in comparison to SIFT and SURF. We deployed a test in which we match feature descriptors to artificial rigid objects. The results indicate that the SIFT descriptor is the most promising solution in our addressed domain, AR-based assembly training.
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Xiong, Yu-Jie, Ying Wen, Patrick S. P. Wang, and Yue Lu. "Text-independent writer identification using SIFT descriptor and contour-directional feature." In 2015 13th International Conference on Document Analysis and Recognition (ICDAR). IEEE, 2015. http://dx.doi.org/10.1109/icdar.2015.7333732.

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Leyva, P., G. Domenech-Asensi, J. Garrigos, et al. "Simplification and hardware implementation of the feature descriptor vector calculation in the SIFT algorithm." In 2014 24th International Conference on Field Programmable Logic and Applications (FPL). IEEE, 2014. http://dx.doi.org/10.1109/fpl.2014.6927409.

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Peng, Yong Kang, Yi Lai Zhang, Xi En Cheng, Yi Cheng Li, and Shi Dong Zhao. "An Object Detection Method Based on the Joint Feature of the H-S Color Descriptor and the SIFT Feature." In 2018 International Conference on Audio, Language and Image Processing (ICALIP). IEEE, 2018. http://dx.doi.org/10.1109/icalip.2018.8455641.

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