作者: 補(bǔ)助 時(shí)間: 2025-3-21 22:52
Malcolm N. MacDonald,Duncan Hunterignment classifiers to further improve the accuracy. Extensive evaluations were performed on several datasets including the challenging Labeled Faces in the Wild (LFW). Face parts descriptors were also evaluated, including the recently proposed Minimum Output Sum of Squared Error (MOSSE) filter. The作者: tympanometry 時(shí)間: 2025-3-22 03:26
https://doi.org/10.1007/978-1-349-10452-9cing consistent annotations over similar visual patterns. Our model is optimized by efficient belief propagation algorithms embedded in an expectation-maximization (EM) scheme. Extensive experiments are conducted to evaluate the performance on several standard large-scale image datasets, showing tha作者: FIR 時(shí)間: 2025-3-22 07:10 作者: 牽索 時(shí)間: 2025-3-22 12:17
https://doi.org/10.1057/9781137310903ide a weighted regret bound as a theoretical guarantee of performance. The proposed novel online learning framework can handle examples with different importance weights for binary, multiclass, and even structured output labels in both linear and non-linear kernels. Applying the method to tracking r作者: FAST 時(shí)間: 2025-3-22 13:54
The Discovery of Chinese Literature (Wenxue)solve this constrained minimization problem. In particular, manually annotated segmentation on a very small set of 2D slices are taken as constraints and incorporated into the whole clustering process. Experimental results demonstrate that the proposed CMEWCVT algorithm significantly improve the pre作者: FAST 時(shí)間: 2025-3-22 18:40
God, Group, and Blame Psychology,elated methods. The experimental evaluation demonstrates that state-of-the-art detection and segmentation results are achieved and that our method is inherently able to handle overlapping instances and an increased range of articulations, aspect ratios and scales.作者: 破裂 時(shí)間: 2025-3-22 21:47 作者: 斷言 時(shí)間: 2025-3-23 04:58 作者: CRAFT 時(shí)間: 2025-3-23 05:59
Annotation Propagation in Large Image Databases via Dense Image Correspondencecing consistent annotations over similar visual patterns. Our model is optimized by efficient belief propagation algorithms embedded in an expectation-maximization (EM) scheme. Extensive experiments are conducted to evaluate the performance on several standard large-scale image datasets, showing tha作者: Implicit 時(shí)間: 2025-3-23 10:31 作者: 發(fā)誓放棄 時(shí)間: 2025-3-23 15:00 作者: Implicit 時(shí)間: 2025-3-23 20:09
Grain Segmentation of 3D Superalloy Images Using Multichannel EWCVT under Human Annotation Constrainsolve this constrained minimization problem. In particular, manually annotated segmentation on a very small set of 2D slices are taken as constraints and incorporated into the whole clustering process. Experimental results demonstrate that the proposed CMEWCVT algorithm significantly improve the pre作者: 決定性 時(shí)間: 2025-3-24 00:17 作者: cataract 時(shí)間: 2025-3-24 04:13 作者: Amendment 時(shí)間: 2025-3-24 09:05
,Pragmatics and Reviewers’ Reports,an polynomial regression to shape analysis in Kendall shape space. Results are presented, showing the power of polynomial regression on the classic rat skull growth data of Bookstein and the analysis of the shape changes associated with aging of the corpus callosum from the OASIS Alzheimer’s study.作者: Preserve 時(shí)間: 2025-3-24 12:29
,Pragmatics and Reviewers’ Reports, employ approximate nearest neighbor search to speed-up the E-step and exploit its iterative nature to make search incremental, boosting both speed and precision. We achieve superior performance in large scale retrieval, being as fast as the best known approximate .-means.作者: 震驚 時(shí)間: 2025-3-24 18:39 作者: 弄臟 時(shí)間: 2025-3-24 21:11
Polynomial Regression on Riemannian Manifoldsan polynomial regression to shape analysis in Kendall shape space. Results are presented, showing the power of polynomial regression on the classic rat skull growth data of Bookstein and the analysis of the shape changes associated with aging of the corpus callosum from the OASIS Alzheimer’s study.作者: 魯莽 時(shí)間: 2025-3-25 02:26
Approximate Gaussian Mixtures for Large Scale Vocabularies employ approximate nearest neighbor search to speed-up the E-step and exploit its iterative nature to make search incremental, boosting both speed and precision. We achieve superior performance in large scale retrieval, being as fast as the best known approximate .-means.作者: Etching 時(shí)間: 2025-3-25 04:33
A Discrete Chain Graph Model for 3d+t Cell Tracking with High Misdetection Robustnesss from the maximum a-posteriori configuration. The model is evaluated on two challenging four-dimensional data sets from developmental biology. Compared to previous work, we obtain improved tracks due to an increased robustness against false positive detections and the incorporation of temporal domain knowledge.作者: ANNUL 時(shí)間: 2025-3-25 07:45
Malcolm N. MacDonald,Duncan Hunterssing, such as noise removal and correct patch normalization, dramatically improves our results. Perhaps surprisingly, even better results are achieved on a variety of real test scenes by providing our algorithm with only . training depth data.作者: 規(guī)章 時(shí)間: 2025-3-25 14:34
Studies in History and Philosophy of Scienceations: Eichner and Ferrari [5], Sapp .?[16], Andriluka . [2] and Yang and Ramanan [22]. We demonstrate that in each case the evaluator is able to predict if the algorithm has correctly estimated the pose or not.作者: intolerance 時(shí)間: 2025-3-25 18:55
Asylum Policy Responsiveness in Scandinaviag object trajectories and cast the relational weight learning task as an online latent SVM problem. Extensive experiments on challenging real world video sequences demonstrate the efficiency and effectiveness of our framework.作者: 廣告 時(shí)間: 2025-3-25 22:16 作者: 軌道 時(shí)間: 2025-3-26 00:30
Patch Based Synthesis for Single Depth Image Super-Resolutionssing, such as noise removal and correct patch normalization, dramatically improves our results. Perhaps surprisingly, even better results are achieved on a variety of real test scenes by providing our algorithm with only . training depth data.作者: 慢慢啃 時(shí)間: 2025-3-26 04:25
Has My Algorithm Succeeded? An Evaluator for Human Pose Estimatorsations: Eichner and Ferrari [5], Sapp .?[16], Andriluka . [2] and Yang and Ramanan [22]. We demonstrate that in each case the evaluator is able to predict if the algorithm has correctly estimated the pose or not.作者: Adrenal-Glands 時(shí)間: 2025-3-26 11:06
Group Tracking: Exploring Mutual Relations for Multiple Object Trackingg object trajectories and cast the relational weight learning task as an online latent SVM problem. Extensive experiments on challenging real world video sequences demonstrate the efficiency and effectiveness of our framework.作者: 有權(quán)威 時(shí)間: 2025-3-26 13:32 作者: anarchist 時(shí)間: 2025-3-26 18:09
Fast Regularization of Matrix-Valued Imageslarization of matrix valued images on a graphic processing unit..We demonstrate the effectiveness of our method for smoothing several group-valued image types, with applications in directions diffusion, motion analysis from depth sensors, and DT-MRI denoising.作者: 無(wú)效 時(shí)間: 2025-3-26 23:26 作者: 白楊魚(yú) 時(shí)間: 2025-3-27 02:37
0302-9743 es, 3D reconstruction, visual recognition and classification, visual features and image matching, visual monitoring: action and activities, models, optimisation, learning, visual tracking and image registration, photometry: lighting and colour, and image segmentation.978-3-642-33711-6978-3-642-33712-3Series ISSN 0302-9743 Series E-ISSN 1611-3349 作者: Carcinoma 時(shí)間: 2025-3-27 06:28
Conference proceedings 2012 are organized in topical sections on geometry, 2D and 3D shapes, 3D reconstruction, visual recognition and classification, visual features and image matching, visual monitoring: action and activities, models, optimisation, learning, visual tracking and image registration, photometry: lighting and colour, and image segmentation.作者: 我就不公正 時(shí)間: 2025-3-27 12:25
Umut Korkut,Jonas Hinnfors,Helen Drakelarization of matrix valued images on a graphic processing unit..We demonstrate the effectiveness of our method for smoothing several group-valued image types, with applications in directions diffusion, motion analysis from depth sensors, and DT-MRI denoising.作者: maverick 時(shí)間: 2025-3-27 16:17 作者: ARY 時(shí)間: 2025-3-27 21:26
Conference proceedings 2012012, held in Florence, Italy, in October 2012. The 408 revised papers presented were carefully reviewed and selected from 1437 submissions. The papers are organized in topical sections on geometry, 2D and 3D shapes, 3D reconstruction, visual recognition and classification, visual features and image 作者: elastic 時(shí)間: 2025-3-27 23:55
The Discourse of Politics in Actions the local estimates between multiple images, and a global shape constraint, which couples landmarks and images across the image set. In video sequences, our method greatly improves the temporal stability of landmark estimates without compromising accuracy relative to ground truth.作者: 流利圓滑 時(shí)間: 2025-3-28 02:36 作者: 心神不寧 時(shí)間: 2025-3-28 09:06 作者: Pde5-Inhibitors 時(shí)間: 2025-3-28 12:20 作者: VICT 時(shí)間: 2025-3-28 16:06 作者: 堅(jiān)毅 時(shí)間: 2025-3-28 22:43
Approximate Gaussian Mixtures for Large Scale Vocabulariess for image retrieval. It is a variant of expectation-maximization that can converge rapidly while dynamically estimating the number of components. We employ approximate nearest neighbor search to speed-up the E-step and exploit its iterative nature to make search incremental, boosting both speed an作者: 不透明 時(shí)間: 2025-3-29 00:27 作者: Glossy 時(shí)間: 2025-3-29 05:13
Joint Face Alignment with Non-parametric Shape Models as output. Our method is an extension of the recent localization method of Belhumeur . [1], which combines the output of local detectors with a non-parametric set of face shape models. We are inspired by the recent joint alignment method of Zhao . [20], which employs a modified Active Appearance Mo作者: 東西 時(shí)間: 2025-3-29 09:25
Discriminative Bayesian Active Shape Models) face model and a set of discriminant local detectors, one for each facial landmark. The patch responses can be embedded into a Bayesian inference problem, where the posterior distribution of the global warp is inferred in a ?maximum a posteriori (MAP) sense. However, previous formulations do not m作者: APRON 時(shí)間: 2025-3-29 14:40
Patch Based Synthesis for Single Depth Image Super-Resolutione sensors measure depths with non-Gaussian noise and at lower starting resolutions than typical visible-light cameras. While patch based approaches for upsampling intensity images continue to improve, this is the first exploration of patching for depth images..We match against the height field of ea作者: obnoxious 時(shí)間: 2025-3-29 17:01
Annotation Propagation in Large Image Databases via Dense Image Correspondenceaches rely on a corpus of training images where each pixel is labeled. However, for large image databases, pixel labels are expensive to obtain and are often unavailable. Furthermore, when classifying multiple images, each image is typically solved for independently, which often results in inconsist作者: 把手 時(shí)間: 2025-3-29 23:38
Numerically Stable Optimization of Polynomial Solvers for Minimal Problemsl problems, but also for finding stationary points for overdetermined problems. The state-of-the-art is based on the use of numerical linear algebra on the large but sparse coefficient matrix that represents the original equations multiplied with a set of monomials. The key observation in this paper作者: habile 時(shí)間: 2025-3-30 00:18 作者: 獨(dú)裁政府 時(shí)間: 2025-3-30 05:56
Group Tracking: Exploring Mutual Relations for Multiple Object Trackingbjects in mutual context with each other to benefit robust and accurate tracking. We introduce a unified framework to combine both Individual Object Models (IOMs) and Mutual Relation Models (MRMs). The MRMs consist of three components, the relational graph to indicate related objects, the mutual rel作者: 天真 時(shí)間: 2025-3-30 11:43 作者: 先兆 時(shí)間: 2025-3-30 14:56 作者: 過(guò)份好問(wèn) 時(shí)間: 2025-3-30 19:46
Fast Regularization of Matrix-Valued Imagesor fast regularization of matrix group-valued images..Using the augmented Lagrangian framework we separate total- variation regularization of matrix-valued images into a regularization and a projection steps. Both steps are computationally efficient and easily parallelizable, allowing real-time regu作者: Exploit 時(shí)間: 2025-3-30 21:53 作者: 沐浴 時(shí)間: 2025-3-31 02:25 作者: menopause 時(shí)間: 2025-3-31 07:48 作者: AXIS 時(shí)間: 2025-3-31 13:12 作者: 喊叫 時(shí)間: 2025-3-31 13:54 作者: ADOPT 時(shí)間: 2025-3-31 19:35 作者: 不在灌木叢中 時(shí)間: 2025-4-1 01:22
Computer Vision – ECCV 2012978-3-642-33712-3Series ISSN 0302-9743 Series E-ISSN 1611-3349 作者: Redundant 時(shí)間: 2025-4-1 04:42 作者: DEVIL 時(shí)間: 2025-4-1 08:19
https://doi.org/10.1007/978-3-642-33712-3Markov random fields; activity recognition; machine learning; object detectors; saliency models; algorith作者: arabesque 時(shí)間: 2025-4-1 13:58
978-3-642-33711-6Springer-Verlag Berlin Heidelberg 2012作者: 健忘癥 時(shí)間: 2025-4-1 17:09
