作者: 拱墻 時(shí)間: 2025-3-21 21:54
978-3-319-86059-6Springer International Publishing AG 2017作者: 攤位 時(shí)間: 2025-3-22 00:47 作者: 民間傳說(shuō) 時(shí)間: 2025-3-22 06:05 作者: Harness 時(shí)間: 2025-3-22 08:49 作者: HALL 時(shí)間: 2025-3-22 15:49
Qing Zhu,Xiaoxia Yang,Haifeng Li appearance data as well as those inferred from both 2D or 3D tracking data. These sub-units are then combined using a sign level classifier; here, two options are presented. The first uses Markov Models to encode the temporal changes between sub-units. The second makes use of Sequential Pattern Boo作者: HALL 時(shí)間: 2025-3-22 18:09
Spatial Epidemiology and Public Health,to create gestures easily distinguishable from users’ normal movements. Our tool MAGIC Summoning addresses this problem. Given a specific platform and task, we gather a large database of unlabeled sensor data captured in the environments in which the system will be used (an “Everyday Gesture Library作者: 燒烤 時(shí)間: 2025-3-22 21:56 作者: dandruff 時(shí)間: 2025-3-23 04:31
G. B. M. Heuvelink,F. M. van Egmonde continuous and the categorical model. The continuous model defines each facial expression of emotion as a feature vector in a face space. This model explains, for example, how expressions of emotion can be seen at different intensities. In contrast, the categorical model consists of . classifiers,作者: commensurate 時(shí)間: 2025-3-23 08:40 作者: 世俗 時(shí)間: 2025-3-23 10:44
A. Menafoglio,A. Guadagnini,P. Secchition in sign language (SL) videos. Aff-SAM offers a compact and descriptive representation of hand configurations as well as regularized model-fitting, assisting hand tracking and extracting handshape features. We construct SA images representing the hand’s shape and appearance . landmark points. We作者: Suggestions 時(shí)間: 2025-3-23 14:20
Richard F. Link,George S. Koch Jr. models only considers rigid parts (e.g., torso, head, half limbs) guided by human anatomy. We argue that this representation of parts is not necessarily appropriate. In this paper, we introduce hierarchical poselets—a new representation for modeling the pose configuration of human bodies. Hierarchi作者: Gullible 時(shí)間: 2025-3-23 21:04 作者: hematuria 時(shí)間: 2025-3-24 00:30 作者: 本土 時(shí)間: 2025-3-24 04:08 作者: 誘使 時(shí)間: 2025-3-24 08:46 作者: 頂點(diǎn) 時(shí)間: 2025-3-24 14:10 作者: troponins 時(shí)間: 2025-3-24 18:11
https://doi.org/10.1007/978-3-642-58567-8t accuracy and multi-class support. In this paper, we present a novel method for transfer learning which uses decision forests, and we apply it to recognize gestures and characters. We introduce two mechanisms into the decision forest framework in order to transfer knowledge from the source tasks to作者: 癡呆 時(shí)間: 2025-3-24 22:31
Geostatistik in der Baugrundmodellierungh a demanding Kinect-based multimodal dataset, introduced in a recent gesture recognition challenge (CHALEARN 2013), where multiple subjects freely perform multimodal gestures. We employ multiple modalities, that is, visual cues, such as skeleton data, color and depth images, as well as audio, and w作者: Nerve-Block 時(shí)間: 2025-3-24 23:36
GEO SAR System Analysis and Design,ccessible for non-specialists. Emphasis is placed on ease of use, with a consistent, minimalist design that promotes accessibility while supporting flexibility and customization for advanced users. The toolkit features a broad range of classification and regression algorithms and has extensive suppo作者: 指數(shù) 時(shí)間: 2025-3-25 06:17
L. F. Liu,R. H. Li,X. J. Yang,J. Z. Renfer from “noise” such as mislabeling, or inaccurate identification of start and end time of gesture instances. In this paper we present SegmentedLCSS and WarpingLCSS, two template-matching methods offering robustness when trained with noisy crowdsourced annotations to spot gestures from wearable mot作者: 濃縮 時(shí)間: 2025-3-25 10:39
Trevor L. L. Orr PhD,Eric R. Farrell PhDion domains for this type of technology. As in many other computer vision areas, deep learning based methods have quickly become a reference methodology for obtaining state-of-the-art performance in both tasks. This chapter is a survey of current deep learning based methodologies for action and gest作者: CHECK 時(shí)間: 2025-3-25 14:09 作者: 懶惰人民 時(shí)間: 2025-3-25 16:14 作者: SHRIK 時(shí)間: 2025-3-25 20:00
Human Gesture Recognition on Product Manifolds,e geometry of tensor space is often ignored. The aim of this paper is to demonstrate the importance of the intrinsic geometry of tensor space which yields a very discriminating structure for action recognition. We characterize data tensors as points on a product manifold and model it statistically u作者: 陳腐思想 時(shí)間: 2025-3-26 03:26
Sign Language Recognition Using Sub-units, appearance data as well as those inferred from both 2D or 3D tracking data. These sub-units are then combined using a sign level classifier; here, two options are presented. The first uses Markov Models to encode the temporal changes between sub-units. The second makes use of Sequential Pattern Boo作者: Aviary 時(shí)間: 2025-3-26 04:49 作者: wreathe 時(shí)間: 2025-3-26 11:17
Language-Motivated Approaches to Action Recognition,sight into the underlying patterns of motions in activities, we develop a dynamic, hierarchical Bayesian model which connects low-level visual features in videos with poses, motion patterns and classes of activities. This process is somewhat analogous to the method of detecting topics or categories 作者: 山崩 時(shí)間: 2025-3-26 16:22 作者: ABOUT 時(shí)間: 2025-3-26 17:29 作者: Hangar 時(shí)間: 2025-3-27 00:37 作者: Acetabulum 時(shí)間: 2025-3-27 03:45 作者: 有節(jié)制 時(shí)間: 2025-3-27 09:02 作者: Decline 時(shí)間: 2025-3-27 10:35
One-Shot Learning Gesture Recognition from RGB-D Data Using Bag of Features,rom only one training sample per gesture class. For feature extraction, a new spatio-temporal feature representation called 3D enhanced motion scale-invariant feature transform is proposed, which fuses RGB-D data. Compared with other features, the new feature set is invariant to scale and rotation, 作者: OTHER 時(shí)間: 2025-3-27 17:16 作者: micronutrients 時(shí)間: 2025-3-27 20:02 作者: chiropractor 時(shí)間: 2025-3-28 01:08
Bayesian Co-Boosting for Multi-modal Gesture Recognition, critical issues for multi-modal gesture recognition: how to select discriminative features for recognition and how to fuse features from different modalities. In this paper, we propose a novel Bayesian Co-Boosting framework for multi-modal gesture recognition. Inspired by boosting learning and co-t作者: 領(lǐng)先 時(shí)間: 2025-3-28 03:43 作者: 顯微鏡 時(shí)間: 2025-3-28 08:00 作者: COM 時(shí)間: 2025-3-28 14:20 作者: 沖擊力 時(shí)間: 2025-3-28 16:37 作者: AVOID 時(shí)間: 2025-3-28 21:04 作者: 先兆 時(shí)間: 2025-3-28 23:02
Qing Zhu,Xiaoxia Yang,Haifeng Listing to apply discriminative feature selection at the same time as encoding temporal information. This approach is more robust to noise and performs well in signer independent tests, improving results from the 54% achieved by the Markov Chains to 76%.作者: evince 時(shí)間: 2025-3-29 07:06 作者: 錯(cuò) 時(shí)間: 2025-3-29 09:44
2520-131X methods for gesture recognition.Presents an open-source C++.This book presents a selection of chapters, written by leading international researchers, related to the automatic analysis of gestures from still images and multi-modal RGB-Depth image sequences. It offers a comprehensive review of vision作者: 閃光你我 時(shí)間: 2025-3-29 13:05
GEO SAR System Analysis and Design,exibility and customization for advanced users. The toolkit features a broad range of classification and regression algorithms and has extensive support for building real-time systems. This includes algorithms for signal processing, feature extraction and automatic gesture spotting.作者: Osteons 時(shí)間: 2025-3-29 17:46
P. Krishna Krishnamurthy,L. Krishnamurthyilable several datasets we recorded for that purpose, including tens of thousands of videos, which are available to conduct further research. We also overview recent state of the art works on gesture recognition based on a proposed taxonomy for gesture recognition, discussing challenges and future lines of research.作者: slow-wave-sleep 時(shí)間: 2025-3-29 22:13
Cumulative Distribution Estimators,together with variants of a Dynamic Time Warping technique. Both methods outperform other published methods and help narrow the gap between human performance and algorithms on this task. The code is publicly available in the MLOSS repository.作者: 熔巖 時(shí)間: 2025-3-30 03:50
https://doi.org/10.1007/978-3-642-58567-8e manifold structure of the feature space. We show that both of them are important to achieve higher accuracy. Our experiments demonstrate improvements over traditional decision forests in the ChaLearn Gesture Challenge and MNIST data set. They also compare favorably against other state-of-the-art classifiers.作者: POINT 時(shí)間: 2025-3-30 04:03 作者: 首創(chuàng)精神 時(shí)間: 2025-3-30 10:38
One-Shot-Learning Gesture Recognition Using HOG-HOF Features,together with variants of a Dynamic Time Warping technique. Both methods outperform other published methods and help narrow the gap between human performance and algorithms on this task. The code is publicly available in the MLOSS repository.作者: 徹底檢查 時(shí)間: 2025-3-30 13:29
Transfer Learning Decision Forests for Gesture Recognition,e manifold structure of the feature space. We show that both of them are important to achieve higher accuracy. Our experiments demonstrate improvements over traditional decision forests in the ChaLearn Gesture Challenge and MNIST data set. They also compare favorably against other state-of-the-art classifiers.作者: Neutral-Spine 時(shí)間: 2025-3-30 18:36
Book 2017ages and multi-modal RGB-Depth image sequences. It offers a comprehensive review of vision-based approaches for supervised gesture recognition methods that have been validated by various challenges. Several aspects of gesture recognition are reviewed, including data acquisition from different source作者: Cloudburst 時(shí)間: 2025-3-30 23:50
2520-131X e reviewed, including data acquisition from different sources, feature extraction, learning, and recognition of gestures..978-3-319-86059-6978-3-319-57021-1Series ISSN 2520-131X Series E-ISSN 2520-1328 作者: 熄滅 時(shí)間: 2025-3-31 01:54
Richard F. Link,George S. Koch Jr.ferring the optimal labeling of this hierarchical model. The pose information captured by this hierarchical model can also be used as a intermediate representation for other high-level tasks. We demonstrate it in action recognition from static images.作者: Noisome 時(shí)間: 2025-3-31 07:36 作者: reception 時(shí)間: 2025-3-31 12:04
Discriminative Hierarchical Part-Based Models for Human Parsing and Action Recognition,ferring the optimal labeling of this hierarchical model. The pose information captured by this hierarchical model can also be used as a intermediate representation for other high-level tasks. We demonstrate it in action recognition from static images.作者: CRATE 時(shí)間: 2025-3-31 13:33 作者: MENT 時(shí)間: 2025-3-31 20:43 作者: 品嘗你的人 時(shí)間: 2025-3-31 22:14
Book 2017 that have been validated by various challenges. Several aspects of gesture recognition are reviewed, including data acquisition from different sources, feature extraction, learning, and recognition of gestures..作者: 討好女人 時(shí)間: 2025-4-1 03:26
Human Gesture Recognition on Product Manifolds,s performed using geodesic distance on a product manifold where each factor manifold is Grassmannian. Our method exploits appearance and motion without explicitly modeling the shapes and dynamics. We assess the proposed method using three gesture databases, namely the Cambridge hand-gesture, the UMD作者: pancreas 時(shí)間: 2025-4-1 09:35 作者: Pessary 時(shí)間: 2025-4-1 14:01
Language-Motivated Approaches to Action Recognition,ies (or gestures) in a video sequence, analogous to the use of filler models for keyword detection in speech processing. We demonstrate the robustness of our classification model and our spotting framework by recognizing activities in unconstrained real-life video sequences and by spotting gestures 作者: scrutiny 時(shí)間: 2025-4-1 15:27
