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101.
Vasileios Belagiannis Xinchao Wang Horesh Beny Ben Shitrit Kiyoshi Hashimoto Ralf Stauder Yoshimitsu Aoki Michael Kranzfelder Armin Schneider Pascal Fua Slobodan Ilic Hubertus Feussner Nassir Navab 《Machine Vision and Applications》2016,27(7):1035-1046
Multiple human pose estimation is an important yet challenging problem. In an operating room (OR) environment, the 3D body poses of surgeons and medical staff can provide important clues for surgical workflow analysis. For that purpose, we propose an algorithm for localizing and recovering body poses of multiple human in an OR environment under a multi-camera setup. Our model builds on 3D Pictorial Structures and 2D body part localization across all camera views, using convolutional neural networks (ConvNets). To evaluate our algorithm, we introduce a dataset captured in a real OR environment. Our dataset is unique, challenging and publicly available with annotated ground truths. Our proposed algorithm yields to promising pose estimation results on this dataset. 相似文献
102.
Chaabouni Souad Benois-pineau Jenny Tison François Ben Amar Chokri Zemmari Akka 《Multimedia Tools and Applications》2017,76(21):22527-22546
Multimedia Tools and Applications - Studies of visual attention of patients with Dementia such as Parkinson’s Disease Dementia and Alzheimer Disease is a promising way for non-invasive... 相似文献
103.
Endmember variability in Spectral Mixture Analysis: A review 总被引:9,自引:0,他引:9
The composite nature of remotely sensed spectral information often masks diagnostic spectral features and hampers the detailed identification and mapping of targeted constituents of the earth's surface. Spectral Mixture Analysis (SMA) is a well established and effective technique to address this mixture problem. SMA models a mixed spectrum as a linear or nonlinear combination of its constituent spectral components or spectral endmembers weighted by their subpixel fractional cover. By model inversion SMA provides subpixel endmember fractions. The lack of ability to account for temporal and spatial variability between and among endmembers has been acknowledged as a major shortcoming of conventional SMA approaches using a linear mixture model with fixed endmembers. Over the past decades numerous efforts have been made to circumvent this issue. This review paper summarizes the available methods and results of endmember variability reduction in SMA. Five basic principles to mitigate endmember variability are identified: (i) the use of multiple endmembers for each component in an iterative mixture analysis cycle, (ii) the selection of a subset of stable spectral features, (iii) the spectral weighting of bands, (iv) spectral signal transformations and (v) the use of radiative transfer models in a mixture analysis. We draw attention to the high complementarities between the different techniques and suggest that an integrated approach is necessary to effectively address endmember variability issues in SMA. 相似文献
104.
Many decision-makers in industry, government and academia routinely make decisions whose outcome depends on the evolution of software technology trends. Even though the stakes of these decisions are usually very high, decision makers routinely depend on expert opinions and qualitative assessments to model the evolution of software technology; both of these sources of decision-making information are subjective, are based on opinions rather than facts, and are prone to error. In this paper, we report on our ongoing work to build quantitative models of the evolution of software technology trends. In particular, we discuss how we took specific evolutionary models and merged them into a single (general-purpose) model. The original specific models are derived empirically using statistical methods on trend data we had collected over several years, and have been validated individually; in this paper we further validate the generic (general-purpose) model. 相似文献
105.
Heterogeneous datasets arise naturally in most applications due to the use of a variety of sensors and measuring platforms. Such datasets can be heterogeneous in terms of the error characteristics and sensor models. Treating such data is most naturally accomplished using a Bayesian or model-based geostatistical approach; however, such methods generally scale rather badly with the size of dataset, and require computationally expensive Monte Carlo based inference. Recently within the machine learning and spatial statistics communities many papers have explored the potential of reduced rank representations of the covariance matrix, often referred to as projected or fixed rank approaches. In such methods the covariance function of the posterior process is represented by a reduced rank approximation which is chosen such that there is minimal information loss. In this paper a sequential Bayesian framework for inference in such projected processes is presented. The observations are considered one at a time which avoids the need for high dimensional integrals typically required in a Bayesian approach. A C++ library, gptk, which is part of the INTAMAP web service, is introduced which implements projected, sequential estimation and adds several novel features. In particular the library includes the ability to use a generic observation operator, or sensor model, to permit data fusion. It is also possible to cope with a range of observation error characteristics, including non-Gaussian observation errors. Inference for the covariance parameters is explored, including the impact of the projected process approximation on likelihood profiles. We illustrate the projected sequential method in application to synthetic and real datasets. Limitations and extensions are discussed. 相似文献
106.
Barycentric coordinates are very popular for interpolating data values on polyhedral domains. It has been recently shown that expressing them as complex functions has various advantages when interpolating two‐dimensional data in the plane, and in particular for holomorphic maps. We extend and generalize these results by investigating the complex representation of real‐valued barycentric coordinates, when applied to planar domains. We show how the construction for generating real‐valued barycentric coordinates from a given weight function can be applied to generating complex‐valued coordinates, thus deriving complex expressions for the classical barycentric coordinates: Wachspress, mean value, and discrete harmonic. Furthermore, we show that a complex barycentric map admits the intuitive interpretation as a complex‐weighted combination of edge‐to‐edge similarity transformations, allowing the design of “home‐made” barycentric maps with desirable properties. Thus, using the tools of complex analysis, we provide a methodology for analyzing existing barycentric mappings, as well as designing new ones. 相似文献
107.
Justin Solomon Mirela Ben‐Chen Adrian Butscher Leonidas Guibas 《Computer Graphics Forum》2011,30(2):365-374
The discovery of meaningful parts of a shape is required for many geometry processing applications, such as parameterization, shape correspondence, and animation. It is natural to consider primitives such as spheres, cylinders and cones as the building blocks of shapes, and thus to discover parts by fitting such primitives to a given surface. This approach, however, will break down if primitive parts have undergone almost‐isometric deformations, as is the case, for example, for articulated human models. We suggest that parts can be discovered instead by finding intrinsic primitives, which we define as parts that posses an approximate intrinsic symmetry. We employ the recently‐developed method of computing discrete approximate Killing vector fields (AKVFs) to discover intrinsic primitives by investigating the relationship between the AKVFs of a composite object and the AKVFs of its parts. We show how to leverage this relationship with a standard clustering method to extract k intrinsic primitives and remaining asymmetric parts of a shape for a given k. We demonstrate the value of this approach for identifying the prominent symmetry generators of the parts of a given shape. Additionally, we show how our method can be modified slightly to segment an entire surface without marking asymmetric connecting regions and compare this approach to state‐of‐the‐art methods using the Princeton Segmentation Benchmark. 相似文献
108.
109.
Solvability conditions and solutions to perfect regulation problem under measurement output feedback
The problem of perfect regulation is to design a family of control laws for a given plant such that the resulting overall closed-loop system is internally stable and its controlled output can be reduced to zero arbitrarily fast from any initial condition. Such a problem was heavily studied by many researchers in the 1970s and early 1980s. However, to the best of our knowledge, all of the earlier results deal only with the problem under full state feedback. In this paper, we solve the long-standing problem of perfect regulation via measurement output feedback for general linear time-invariant multivariable systems. In particular, we derive necessary and sufficient conditions under which the problem of perfect regulation via measurement output feedback is solvable for general systems, and, under these conditions, construct two families of feedback laws, one of full order and the other reduced order, that solve the problem. 相似文献
110.
Globally maximizing, locally minimizing: unsupervised discriminant projection with applications to face and palm biometrics 总被引:5,自引:0,他引:5
Yang J Zhang D Yang JY Niu B 《IEEE transactions on pattern analysis and machine intelligence》2007,29(4):650-664
This paper develops an unsupervised discriminant projection (UDP) technique for dimensionality reduction of high-dimensional data in small sample size cases. UDP can be seen as a linear approximation of a multimanifolds-based learning framework which takes into account both the local and nonlocal quantities. UDP characterizes the local scatter as well as the nonlocal scatter, seeking to find a projection that simultaneously maximizes the nonlocal scatter and minimizes the local scatter. This characteristic makes UDP more intuitive and more powerful than the most up-to-date method, locality preserving projection (LPP), which considers only the local scatter for clustering or classification tasks. The proposed method is applied to face and palm biometrics and is examined using the Yale, FERET, and AR face image databases and the PolyU palmprint database. The experimental results show that UDP consistently outperforms LPP and PCA and outperforms LDA when the training sample size per class is small. This demonstrates that UDP is a good choice for real-world biometrics applications 相似文献