標(biāo)題: Titlebook: Computer Vision; Detection, Recogniti Roberto Cipolla,Sebastiano Battiato,Giovanni Maria Book 20101st edition Springer-Verlag Berlin Heidel [打印本頁] 作者: 外表 時(shí)間: 2025-3-21 16:40
書目名稱Computer Vision影響因子(影響力)
書目名稱Computer Vision影響因子(影響力)學(xué)科排名
書目名稱Computer Vision網(wǎng)絡(luò)公開度
書目名稱Computer Vision網(wǎng)絡(luò)公開度學(xué)科排名
書目名稱Computer Vision被引頻次
書目名稱Computer Vision被引頻次學(xué)科排名
書目名稱Computer Vision年度引用
書目名稱Computer Vision年度引用學(xué)科排名
書目名稱Computer Vision讀者反饋
書目名稱Computer Vision讀者反饋學(xué)科排名
作者: Glaci冰 時(shí)間: 2025-3-21 21:30 作者: 付出 時(shí)間: 2025-3-22 01:22
Discriminative Graphical Models for Context-Based Classification,tandard binary CRFs to handle problems with multiclass labels or hierarchical context are also discussed. Finally, application of CRFs on contextual object detection, scene segmentation and texture recognition tasks is demonstrated.作者: OVER 時(shí)間: 2025-3-22 08:28
Towards a New Regional Policy in Spain, track-before-detect approach that consistently detects and recognizes multiple simultaneous objects in a common view, based on motion models. This approach estimates the temporal evolution of objects from noisy data, given their motion model, without an explicit object detection stage.作者: 偽證 時(shí)間: 2025-3-22 11:03
The Need for an Anthropology of Wealthimation from images, and surface extraction from photo-consistency. In this Chapter we will put more emphasis on the latter two: namely how to extract geometric information from a set of photographs without explicit camera visibility, and how to combine different geometry estimates in an optimal way.作者: 全面 時(shí)間: 2025-3-22 15:04 作者: 全面 時(shí)間: 2025-3-22 20:46
https://doi.org/10.1007/978-3-031-34370-4with the aim to provide an answer to such questions. Does machine vision still have anything to learn from human vision? I identify a number of basic principles of biological vision that are likely to be of interest to the machine vision community.作者: ETCH 時(shí)間: 2025-3-22 21:34
https://doi.org/10.1007/978-3-031-34370-4 a novel mathematical characterisation of the co-variance properties of the features which accounts for deviation from the usual idealised image affine (de)formation model. We propose novel metrics to evaluate the features and we show how these can be used to automatically design improved features.作者: 太空 時(shí)間: 2025-3-23 03:17
Is Human Vision Any Good?,with the aim to provide an answer to such questions. Does machine vision still have anything to learn from human vision? I identify a number of basic principles of biological vision that are likely to be of interest to the machine vision community.作者: dragon 時(shí)間: 2025-3-23 08:56
Knowing a Good Feature When You See It: Ground Truth and Methodology to Evaluate Local Features for a novel mathematical characterisation of the co-variance properties of the features which accounts for deviation from the usual idealised image affine (de)formation model. We propose novel metrics to evaluate the features and we show how these can be used to automatically design improved features.作者: AXIS 時(shí)間: 2025-3-23 10:11
Multi-view Multi-object Detection and Tracking, track-before-detect approach that consistently detects and recognizes multiple simultaneous objects in a common view, based on motion models. This approach estimates the temporal evolution of objects from noisy data, given their motion model, without an explicit object detection stage.作者: 期滿 時(shí)間: 2025-3-23 15:03
Shape from Photographs: A Multi-view Stereo Pipeline,imation from images, and surface extraction from photo-consistency. In this Chapter we will put more emphasis on the latter two: namely how to extract geometric information from a set of photographs without explicit camera visibility, and how to combine different geometry estimates in an optimal way.作者: 小平面 時(shí)間: 2025-3-23 19:36
https://doi.org/10.1007/978-3-031-34370-4es to solve new instances resulting in a substantial improvement in the running time. We will present the results of using this approach on the problems of interactive image segmentation, image segmentation in video, human pose estimation and segmentation, and measuring uncertainty of solutions obtained by minimizing energy functions.作者: paradigm 時(shí)間: 2025-3-24 01:11 作者: 冬眠 時(shí)間: 2025-3-24 03:51
Dynamic Graph Cuts and Their Applications in Computer Vision,es to solve new instances resulting in a substantial improvement in the running time. We will present the results of using this approach on the problems of interactive image segmentation, image segmentation in video, human pose estimation and segmentation, and measuring uncertainty of solutions obtained by minimizing energy functions.作者: rheumatism 時(shí)間: 2025-3-24 08:49 作者: Alienated 時(shí)間: 2025-3-24 11:00
Roberto Cipolla,Sebastiano Battiato,Giovanni MariaRecent Results in Detection, Recognition and Reconstruction in Computer Vision.Writte by experts in this field作者: 詼諧 時(shí)間: 2025-3-24 18:52 作者: Conscientious 時(shí)間: 2025-3-24 19:24 作者: 細(xì)胞膜 時(shí)間: 2025-3-25 00:21 作者: LATER 時(shí)間: 2025-3-25 05:53
https://doi.org/10.1007/978-3-031-34370-4rical. In this Chapter we propose to tie the design of local features to their systematic evaluation on a realistic ground-truthed dataset. We propose a novel mathematical characterisation of the co-variance properties of the features which accounts for deviation from the usual idealised image affin作者: AMITY 時(shí)間: 2025-3-25 10:12
https://doi.org/10.1007/978-3-031-34370-4 been the successes of efficient graph cut based minimization algorithms in solving many low level vision problems such as image segmentation, object reconstruction, image restoration and disparity estimation. The scale and form of computer vision problems introduce many challenges in energy minimiz作者: thalamus 時(shí)間: 2025-3-25 14:46
Globalization and White-Collar Crimereferred to as . in Vision. This chapter describes Conditional Random Fields (CRFs) based discriminative models for incorporating context in a principled manner. Unlike the traditional generative Markov Random Fields (MRFs), CRFs allow the use of arbitrarily complex dependencies in the observed data作者: lanugo 時(shí)間: 2025-3-25 15:59
Sally S. Simpson,David Weisburd shown that the human visual system is particularly efficient and effective in perceiving high-level meanings in cluttered real-world scenes, such as objects, scene classes, activities and the stories in the images. In this chapter, we discuss a generativemodel approach for classifying complex human作者: 群居男女 時(shí)間: 2025-3-26 00:04
Globalization and White-Collar Crimen trees that act directly on image pixels, semantic texton forests do not need the expensive computation of filter-bank responses or local descriptors. They are extremely fast to both train and test, especially compared with k-means clustering and nearest-neighbor assignment of feature descriptors. 作者: 迫擊炮 時(shí)間: 2025-3-26 00:40 作者: intelligible 時(shí)間: 2025-3-26 07:33 作者: Banister 時(shí)間: 2025-3-26 09:03
Towards a New Regional Policy in Spain,can be represented by object features (such as position, color and silhouette) or by object trajectories in each view. In this Chapter, we classify and survey state-of-the art multi-view tracking algorithms and discuss their applications and algorithmic limitations. Moreover, we present a multi-view作者: 真 時(shí)間: 2025-3-26 13:34 作者: Psa617 時(shí)間: 2025-3-26 20:00
Resocialising Finance to Exit the Crisisver, in its classic form, Photometric Stereo suffers from two main limitations: Firstly, one needs to obtain images of the 3D scene under multiple different illuminations. As a result the 3D scene needs to remain static during illumination changes, which prohibits the reconstruction of deforming obj作者: Crepitus 時(shí)間: 2025-3-26 23:07 作者: monochromatic 時(shí)間: 2025-3-27 04:40 作者: Constituent 時(shí)間: 2025-3-27 09:15
Is Human Vision Any Good?,” human vision, so the questions arise: Is human vision any good, will it be supplanted by machine vision for most tasks soon? I analyze human vision with the aim to provide an answer to such questions. Does machine vision still have anything to learn from human vision? I identify a number of basic 作者: MURKY 時(shí)間: 2025-3-27 11:46
Knowing a Good Feature When You See It: Ground Truth and Methodology to Evaluate Local Features forrical. In this Chapter we propose to tie the design of local features to their systematic evaluation on a realistic ground-truthed dataset. We propose a novel mathematical characterisation of the co-variance properties of the features which accounts for deviation from the usual idealised image affin作者: Aerophagia 時(shí)間: 2025-3-27 13:35
Dynamic Graph Cuts and Their Applications in Computer Vision, been the successes of efficient graph cut based minimization algorithms in solving many low level vision problems such as image segmentation, object reconstruction, image restoration and disparity estimation. The scale and form of computer vision problems introduce many challenges in energy minimiz作者: Amplify 時(shí)間: 2025-3-27 20:53
Discriminative Graphical Models for Context-Based Classification,referred to as . in Vision. This chapter describes Conditional Random Fields (CRFs) based discriminative models for incorporating context in a principled manner. Unlike the traditional generative Markov Random Fields (MRFs), CRFs allow the use of arbitrarily complex dependencies in the observed data作者: ARC 時(shí)間: 2025-3-28 00:49
What, Where and Who? Telling the Story of an Image by Activity Classification, Scene Recognition an shown that the human visual system is particularly efficient and effective in perceiving high-level meanings in cluttered real-world scenes, such as objects, scene classes, activities and the stories in the images. In this chapter, we discuss a generativemodel approach for classifying complex human作者: 他姓手中拿著 時(shí)間: 2025-3-28 02:22 作者: muscle-fibers 時(shí)間: 2025-3-28 09:58
Multi-view Object Categorization and Pose Estimation,e object may show tremendous variability in appearance and structure under various photometric and geometric conditions. In addition, members of the same class may differ from each other due to various degrees of intra-class variability. Recently, researchers have proposed new models towards the goa作者: granite 時(shí)間: 2025-3-28 12:07
A Vision-Based Remote Control, is pointing towards the user. An attention mechanism allows the user to start the interaction and control a screen pointer by moving their hand in a fist pose directed at the camera. On-screen items can be chosen by a selection mechanism. Current sample applications include browsing video collectio作者: 表否定 時(shí)間: 2025-3-28 14:55
Multi-view Multi-object Detection and Tracking,can be represented by object features (such as position, color and silhouette) or by object trajectories in each view. In this Chapter, we classify and survey state-of-the art multi-view tracking algorithms and discuss their applications and algorithmic limitations. Moreover, we present a multi-view作者: 接觸 時(shí)間: 2025-3-28 22:44
Shape from Photographs: A Multi-view Stereo Pipeline,deas matured enough to provide highly accurate results. We present a complete algorithm to reconstruct 3D objects from images using the stereo correspondence cue. The technique can be described as a pipeline of four basic building blocks: camera calibration, image segmentation, photo-consistency est作者: 愚笨 時(shí)間: 2025-3-29 00:48
Practical 3D Reconstruction Based on Photometric Stereo,ver, in its classic form, Photometric Stereo suffers from two main limitations: Firstly, one needs to obtain images of the 3D scene under multiple different illuminations. As a result the 3D scene needs to remain static during illumination changes, which prohibits the reconstruction of deforming obj作者: 沒血色 時(shí)間: 2025-3-29 03:26 作者: Dorsal-Kyphosis 時(shí)間: 2025-3-29 08:50
Book 20101st editionound at: http://www.dmi.unict.it/icvssThis edited volume contains a selection of articles covering some of the talks and tutorials held during the first two editions of the school on topics such as Recognition, Registration and Reconstruction. The chapters provide an in-depth overview of these chall作者: CEDE 時(shí)間: 2025-3-29 13:46
Sally S. Simpson,David Weisburdhways. For evaluating the robustness of our algorithm, we have assembled a challenging dataset consisting real-world images of eight different sport events, most of them collected from the Internet. Experimental results show that our hierarchical model performs better than existing methods.作者: gustation 時(shí)間: 2025-3-29 16:26 作者: 變形 時(shí)間: 2025-3-29 22:08 作者: BINGE 時(shí)間: 2025-3-30 03:14 作者: 牌帶來 時(shí)間: 2025-3-30 06:35 作者: 向下 時(shí)間: 2025-3-30 08:29 作者: 褻瀆 時(shí)間: 2025-3-30 12:27
