標(biāo)題: Titlebook: Riemannian Computing in Computer Vision; Pavan K. Turaga,Anuj Srivastava Book 2016 The Editor(s) (if applicable) and The Author(s), under [打印本頁(yè)] 作者: Clinton 時(shí)間: 2025-3-21 16:11
書目名稱Riemannian Computing in Computer Vision影響因子(影響力)
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書目名稱Riemannian Computing in Computer Vision網(wǎng)絡(luò)公開度
書目名稱Riemannian Computing in Computer Vision網(wǎng)絡(luò)公開度學(xué)科排名
書目名稱Riemannian Computing in Computer Vision被引頻次
書目名稱Riemannian Computing in Computer Vision被引頻次學(xué)科排名
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書目名稱Riemannian Computing in Computer Vision年度引用學(xué)科排名
書目名稱Riemannian Computing in Computer Vision讀者反饋
書目名稱Riemannian Computing in Computer Vision讀者反饋學(xué)科排名
作者: 不規(guī)則的跳動(dòng) 時(shí)間: 2025-3-21 23:09 作者: 得意人 時(shí)間: 2025-3-22 01:41 作者: Diuretic 時(shí)間: 2025-3-22 05:52 作者: 多產(chǎn)魚 時(shí)間: 2025-3-22 11:33
P. Thomas Fletcher,Miaomiao Zhangiese Frage zu beantworten, wurde ein Analyseraster entwickelt, dessen heuristischer Nutzen darin besteht, die wichtigsten Bestimmungsfaktoren der Wahlergebnisse von Grünen Parteien zu identifizieren (vgl. Kapitel 5.1). In das Analyseraster wurden insgesamt 29 Erkl?rungsvariablen einbezogen. Diese Fa作者: optional 時(shí)間: 2025-3-22 14:31 作者: Hangar 時(shí)間: 2025-3-22 20:05
Martin Bauer,Martins Bruveris,Peter W. Michorlschaftlichen Praxis‘ gegenüber als in einem ?Elfenbeinturm‘ befindlich, als von jeglicher Realit?t abgekoppelte Selbstreferentialit?t ?lebensfremder‘ Individuen vorgestellt. In dem mit dieser Vorstellung von Theorie verbundenen Praxisbegriff ist eine tiefe Abneigung gegenüber gedanklicher Arbeit en作者: 死貓他燒焦 時(shí)間: 2025-3-23 00:47 作者: 打折 時(shí)間: 2025-3-23 03:54
pfelchoreographiekünste“ das Scheitern der internationalen Klimapolitik verbergen konnten, wurde als wesentliche Errungenschaft der Klimakonferenz in Cancun im Dezember 2010 gefeiert, dass das Zusammenbrechen des internationalen Klimapolitikprozesses verhindert werden konnte (Passadakis/Sander 2010:作者: Recess 時(shí)間: 2025-3-23 08:29 作者: habitat 時(shí)間: 2025-3-23 11:55 作者: 債務(wù) 時(shí)間: 2025-3-23 15:47 作者: extinct 時(shí)間: 2025-3-23 21:17 作者: affect 時(shí)間: 2025-3-23 22:23 作者: 爆炸 時(shí)間: 2025-3-24 05:39 作者: 觀察 時(shí)間: 2025-3-24 08:22 作者: Restenosis 時(shí)間: 2025-3-24 10:43 作者: intercede 時(shí)間: 2025-3-24 18:39 作者: 谷物 時(shí)間: 2025-3-24 19:58
that will allow re-application in other contexts.Written by This book presents a comprehensive treatise on Riemannian geometric computations and related statistical inferences in several computer vision problems. This edited volume?includes chapter contributions from leading figures in the field of 作者: Orthodontics 時(shí)間: 2025-3-25 00:32 作者: 加劇 時(shí)間: 2025-3-25 07:20 作者: Saline 時(shí)間: 2025-3-25 09:48 作者: Canopy 時(shí)間: 2025-3-25 14:37
Canonical Correlation Analysis on SPD(,) Manifoldsand has found a multitude of applications in computer vision, medical imaging, and machine learning. The classical formulation assumes that the data live in a pair of . which makes its use in certain important scientific domains problematic. For instance, the set of symmetric positive definite matri作者: 膠水 時(shí)間: 2025-3-25 17:05 作者: Hyperopia 時(shí)間: 2025-3-25 22:33
Robust Estimation for Computer Vision Using Grassmann Manifolds studied for Euclidean spaces and their use has also been extended to Riemannian spaces. In this chapter, we present the necessary mathematical constructs for Grassmann manifolds, followed by two different algorithms that can perform robust estimation on them. In the first one, we describe a nonline作者: 暫停,間歇 時(shí)間: 2025-3-26 02:00 作者: jealousy 時(shí)間: 2025-3-26 06:05 作者: 假裝是你 時(shí)間: 2025-3-26 11:50
Covariance Weighted Procrustes Analysisetely general covariance matrix, extending previous approaches based on factored covariance structures. Procrustes matching is used to compute the Riemannian metric in shape space and is used more widely for carrying out inference such as estimation of mean shape and covariance structure. Rather tha作者: 芳香一點(diǎn) 時(shí)間: 2025-3-26 16:41
Elastic Shape Analysis of Functions, Curves and Trajectoriesnd trajectories can also have important geometric features, we use shape as an all-encompassing term for the descriptors of curves, scalar functions and trajectories. Our framework relies on functional representation and analysis of curves and scalar functions, by square-root velocity fields (SRVF) 作者: Chagrin 時(shí)間: 2025-3-26 18:57 作者: 懸崖 時(shí)間: 2025-3-26 22:11
Elastic Shape Analysis of Surfaces and Imageson, deformation, averaging, statistical modeling, and random sampling of surface shapes. A crucial property of both of these frameworks is that they are invariant to reparameterizations of surfaces. Thus, they result in natural shape comparisons and statistics. The first method we describe is based 作者: 領(lǐng)先 時(shí)間: 2025-3-27 02:15
Designing a Boosted Classifier on Riemannian Manifoldsescriptors lying on a Riemannian manifold. This chapter describes a boosted classification approach that incorporates the a priori knowledge of the geometry of the Riemannian space. The presented classifier incorporated into a rejection cascade and applied to single image human detection task. Resul作者: 無(wú)孔 時(shí)間: 2025-3-27 05:39 作者: Pelago 時(shí)間: 2025-3-27 12:00
Domain Adaptation Using the Grassmann Manifoldact that given data may have variations that can be difficult to incorporate into well-known, classical methods. One of these sources of variation is that of differing data sources, often called domain adaptation. Many domain adaptation techniques use the notion of a shared representation to attempt作者: MAL 時(shí)間: 2025-3-27 17:37 作者: 知識(shí) 時(shí)間: 2025-3-27 20:31 作者: 價(jià)值在貶值 時(shí)間: 2025-3-28 00:41
Book 2016s. This edited volume?includes chapter contributions from leading figures in the field of computer vision who are applying Riemannian geometric approaches in problems such as face recognition, activity recognition, object detection, biomedical image analysis, and structure-from-motion. Some of the m作者: Leaven 時(shí)間: 2025-3-28 03:46 作者: 沉默 時(shí)間: 2025-3-28 07:05 作者: Salivary-Gland 時(shí)間: 2025-3-28 11:48 作者: 輕率看法 時(shí)間: 2025-3-28 18:35 作者: 可觸知 時(shí)間: 2025-3-28 19:55 作者: 清晰 時(shí)間: 2025-3-29 00:31
Shantanu H. Joshi,Jingyong Su,Zhengwu Zhang,Boulbaba Ben Amorird die Legitimation für diese Theorie einem Prinzip unterstellt, das nicht n?her erl?utert zu werden braucht und damit auch einer allgemeinen überprüfbarkeit enthoben wird. Allein ihr Bezug zur ?Gesellschaft‘, zur ?Praxis‘ oder zum ?Handeln‘ zeichnet eine Theorie als ?brauchbare‘ aus.作者: 起波瀾 時(shí)間: 2025-3-29 06:16 作者: Radiation 時(shí)間: 2025-3-29 10:09 作者: 歡呼 時(shí)間: 2025-3-29 13:58
nur eine M?glichkeit zu erhalten, die Klimaerw?rmung auf 2 °C zu begrenzen (vgl. CAT 2011; IEA 2010: 380; PIK 2010; Rogelj/Meinshausen 2010; UNEP 2010; UNFCCC 2009). Rogelj/Meinshau-sen stellen fest: ?It is amazing how unambitious these pledges are“ (Rogelj/ Meinshausen 2010: 1126).作者: 背信 時(shí)間: 2025-3-29 18:53 作者: 定點(diǎn) 時(shí)間: 2025-3-29 22:58
Recursive Computation of the Fréchet Mean on Non-positively Curved Riemannian Manifolds with Applicaometric generalization of the well-known incremental algorithm for computing arithmetic mean, since it reinterprets this algebraic formula in terms of geometric operations on geodesics in the more general manifold setting. In particular, given known formulas for geodesics, iFEE does not require any 作者: Allergic 時(shí)間: 2025-3-30 01:49 作者: 亞麻制品 時(shí)間: 2025-3-30 06:34 作者: frozen-shoulder 時(shí)間: 2025-3-30 08:14
Lie-Theoretic Multi-Robot Localization relative pose and orientation information can be provided, and it is scalable in that the computational complexity does not increase with the size of the robot team and increases linearly with the number of measurements taken from nearby robots. The proposed approach is validated with simulation in作者: Affable 時(shí)間: 2025-3-30 14:34
Covariance Weighted Procrustes Analysisape and covariance structure is difficult due to the inherent non-identifiability. The method requires the specification of constraints to carry out inference, and we discuss some possible practical choices. We illustrate the methodology using data from fish silhouettes and mouse vertebra images.作者: MAOIS 時(shí)間: 2025-3-30 17:29
Elastic Shape Analysis of Functions, Curves and Trajectoriests. A fundamental tool in shape analysis is the construction and implementation of geodesic paths between shapes. This is used to accomplish a variety of tasks, including the definition of a metric to compare shapes, the computation of intrinsic statistics for a set of shapes, and the definition of 作者: 枕墊 時(shí)間: 2025-3-30 21:20
Elastic Shape Analysis of Surfaces and Imageshods by computing geodesic paths between highly articulated surfaces and shape statistics of manually generated surfaces. We also describe applications?of this framework to image registration and medical diagnosis.作者: generic 時(shí)間: 2025-3-31 04:40
A General Least Squares Regression Framework on Matrix Manifolds for Computer Vision visual tracking, object categorization, and activity recognition to human interaction recognition. Our experiments reveal that the proposed method yields competitive performance, including state-of-the-art results on challenging activity recognition benchmarks.作者: 深淵 時(shí)間: 2025-3-31 05:19
in modeling camera motion), stick figures (e.g. for activity recognition), subspace comparisons (e.g. in face recognition), symmetric positive-definite matrices (e.g. in diffusion tensor imaging), and function-spaces (e.g. in studying shapes of closed contours).978-3-319-36095-9978-3-319-22957-7
