作者: 險(xiǎn)代理人 時(shí)間: 2025-3-21 21:51 作者: Iatrogenic 時(shí)間: 2025-3-22 03:48 作者: VOK 時(shí)間: 2025-3-22 05:24 作者: OTHER 時(shí)間: 2025-3-22 09:43
A High Performance CRF Model for Clothes Parsing,riors for each garment as well as similarities between segments, and symmetries between different human body parts. We demonstrate the effectiveness of our approach on the Fashionista dataset?[.] and show that we can obtain a significant improvement over the state-of-the-art.作者: 相信 時(shí)間: 2025-3-22 14:41
https://doi.org/10.1007/978-1-4899-6036-8ely define the common-object labels. We then use cooperative cut to segment the common objects according to the common-object labels. Experimental results demonstrate that the proposed method outperforms the state-of-the-art co-segmentation methods in the segmentation accuracy of the common objects in the images.作者: 相信 時(shí)間: 2025-3-22 20:12
Distribution and economic importance,hich improves the performance significantly. Furthermore, we train an efficient network in a multi-task way which can do age estimation, gender classification and ethnicity classification well simultaneously. The experiments on MORPH Album 2 illustrate the superiorities of the proposed multi-scale CNN over other state-of-the-art methods.作者: Nuance 時(shí)間: 2025-3-22 22:23 作者: Host142 時(shí)間: 2025-3-23 01:35 作者: 溫順 時(shí)間: 2025-3-23 09:16
Unsupervised Image Co-segmentation Based on Cooperative Game,ely define the common-object labels. We then use cooperative cut to segment the common objects according to the common-object labels. Experimental results demonstrate that the proposed method outperforms the state-of-the-art co-segmentation methods in the segmentation accuracy of the common objects in the images.作者: 巧思 時(shí)間: 2025-3-23 13:33 作者: 相互影響 時(shí)間: 2025-3-23 17:52 作者: 泰然自若 時(shí)間: 2025-3-23 20:36
Image Restoration via Multi-prior Collaboration,lar prior methods are applied to evaluate the effectiveness of the proposed multi-prior collaboration framework. Compared with the state-of-the-art image restoration approaches, the proposed framework improves the restoration performance significantly.作者: 勤勉 時(shí)間: 2025-3-23 23:03
Conference proceedings 2015ACCV 2014, held in Singapore, Singapore, in November 2014..The total of 227 contributions presented in these volumes was carefully reviewed and selected from 814 submissions. The papers are organized in topical sections on recognition; 3D vision; low-level vision and features; segmentation; face and作者: Abjure 時(shí)間: 2025-3-24 04:36
Accurate Vessel Segmentation with Progressive Contrast Enhancement and Canny Refinement,ntal results on a public retinal dataset and our clinical cerebral data demonstrate that our approach outperforms state-of-the-art methods including the vesselness based method [.] and the optimally oriented flux (OOF) based method [.].作者: 有發(fā)明天才 時(shí)間: 2025-3-24 07:53 作者: 長(zhǎng)矛 時(shí)間: 2025-3-24 10:42
Local Generic Representation for Face Recognition with Single Sample per Person,ation dictionary, and it uses correntropy to measure the representation residual of each patch. Half-quadratic analysis is adopted to solve the optimization problem. LGR takes the advantages of patch based local representation and generic variation representation, showing leading performance in face作者: ENACT 時(shí)間: 2025-3-24 14:53
Real-Time Head Orientation from a Monocular Camera Using Deep Neural Network,based post-processing to enhance stability of the estimation further in video sequences. We compare the performance with the state-of-the-art algorithm which utilizes depth sensor and we validate our head orientation estimator on Internet photos and video.作者: Cerumen 時(shí)間: 2025-3-24 20:16 作者: 瘋狂 時(shí)間: 2025-3-25 00:53
Visual Salience Learning via Low Rank Matrix Recovery,rix plus a sparse?matrix. We aim at learning a unified sparse matrix that represents the salient regions using these obtained individual saliency maps. The sparse matrix can be inferred by conducting low rank matrix recovery using the robust principal component analysis technique. Experiments show t作者: Nomogram 時(shí)間: 2025-3-25 06:34
A New Framework for Multiclass Classification Using Multiview Assisted Adaptive Boosting,ne classifiers. Finally, decisions of baseline classifiers are agglomerated based on a novel algorithm of reward assignment. The paper then presents classification comparisons on benchmark UCI datasets and eye samples collected from FERET database. Kappa-error diversity diagrams are also studied. In作者: 甜瓜 時(shí)間: 2025-3-25 09:10
,Photorealistic Face De-Identification by Aggregating Donors’ Face Components,nents with the donors’ ones, in such a way that an automatic face matcher is fooled while the appearance of the generated faces are as close as possible to original faces. Experiments on several datasets validate the approach and show its ability both in terms of privacy preservation and visual qual作者: Hyperopia 時(shí)間: 2025-3-25 15:14
Modeling the Temporality of Saliency,Our inter-trajectory saliency formulation also represents the first attempt among computational saliency works to look beyond the immediate neighborhood in space and time, utilizing the perceptual organization rule of common fate (temporal synchrony) to make a group of trajectories stand out from th作者: Pathogen 時(shí)間: 2025-3-25 17:43 作者: scrutiny 時(shí)間: 2025-3-25 23:43 作者: 決定性 時(shí)間: 2025-3-26 03:10
A Three-Color Coupled Level-Set Algorithm for Simultaneous Multiple Cell Segmentation and Tracking,S using a new volume conservation constraint (VCC) to prevent shrinkage or expansion on whole cell boundaries and produce more accurate segmentation and tracking of touching cells. When tested on four different time-lapse image sequences, the 3LS outperforms the original nLS and other relevant state作者: floaters 時(shí)間: 2025-3-26 05:50
OR-PCA with MRF for Robust Foreground Detection in Highly Dynamic Backgrounds,f foreground detection and the computation time as well. Moreover, solving MRF with graph-cuts exploits structural information using spatial neighborhood system and similarities to further improve the foreground segmentation in highly dynamic backgrounds. Experimental results on challenging datasets作者: 四目在模仿 時(shí)間: 2025-3-26 11:57 作者: figurine 時(shí)間: 2025-3-26 12:53
https://doi.org/10.1007/978-1-4899-6036-8ntal results on a public retinal dataset and our clinical cerebral data demonstrate that our approach outperforms state-of-the-art methods including the vesselness based method [.] and the optimally oriented flux (OOF) based method [.].作者: 碎石頭 時(shí)間: 2025-3-26 17:20 作者: synchronous 時(shí)間: 2025-3-26 22:28
https://doi.org/10.1007/978-1-4899-6036-8ation dictionary, and it uses correntropy to measure the representation residual of each patch. Half-quadratic analysis is adopted to solve the optimization problem. LGR takes the advantages of patch based local representation and generic variation representation, showing leading performance in face作者: 厭倦嗎你 時(shí)間: 2025-3-27 02:42
https://doi.org/10.1007/978-1-4899-6036-8based post-processing to enhance stability of the estimation further in video sequences. We compare the performance with the state-of-the-art algorithm which utilizes depth sensor and we validate our head orientation estimator on Internet photos and video.作者: 復(fù)習(xí) 時(shí)間: 2025-3-27 06:19 作者: 頑固 時(shí)間: 2025-3-27 11:15
https://doi.org/10.1007/978-3-030-13777-9rix plus a sparse?matrix. We aim at learning a unified sparse matrix that represents the salient regions using these obtained individual saliency maps. The sparse matrix can be inferred by conducting low rank matrix recovery using the robust principal component analysis technique. Experiments show t作者: Axon895 時(shí)間: 2025-3-27 16:22
https://doi.org/10.1007/978-3-030-13777-9ne classifiers. Finally, decisions of baseline classifiers are agglomerated based on a novel algorithm of reward assignment. The paper then presents classification comparisons on benchmark UCI datasets and eye samples collected from FERET database. Kappa-error diversity diagrams are also studied. In作者: multiply 時(shí)間: 2025-3-27 20:35 作者: Acetaminophen 時(shí)間: 2025-3-28 01:54
The Genetics of Renal Cystic DiseaseOur inter-trajectory saliency formulation also represents the first attempt among computational saliency works to look beyond the immediate neighborhood in space and time, utilizing the perceptual organization rule of common fate (temporal synchrony) to make a group of trajectories stand out from th作者: Fillet,Filet 時(shí)間: 2025-3-28 04:05
The Genetics of Renal Cystic Diseasevide vast robust background candidate regions specified by SLSM. Then the background contrast saliency map (BCSM) is computed based on low-level image stimuli features. SLSM and BCSM are finally integrated to a pixel-accurate saliency map. Extensive experiments show that our approach achieves the st作者: 胡言亂語 時(shí)間: 2025-3-28 10:20 作者: Mammal 時(shí)間: 2025-3-28 13:06 作者: OVERT 時(shí)間: 2025-3-28 16:33
Introduction to Cytochemical Bioassay,f foreground detection and the computation time as well. Moreover, solving MRF with graph-cuts exploits structural information using spatial neighborhood system and similarities to further improve the foreground segmentation in highly dynamic backgrounds. Experimental results on challenging datasets作者: Obligatory 時(shí)間: 2025-3-28 20:18 作者: Brochure 時(shí)間: 2025-3-28 22:57 作者: 盡忠 時(shí)間: 2025-3-29 03:05
Local Generic Representation for Face Recognition with Single Sample per Person,ations of a query sample by the gallery samples. Considering the fact that different parts of human faces have different importance to face recognition, and the fact that the intra-class facial variations can be shared across different subjects, we propose a local generic representation (LGR) based 作者: APNEA 時(shí)間: 2025-3-29 09:39
Unsupervised Image Co-segmentation Based on Cooperative Game,ation algorithms have the assumptions that the common objects are singletons or with the similar size. In addition, they might assume that the background features are simple or discriminative. This paper presents a cooperative co-segmentation without these assumptions. In the proposed cooperative co作者: 駭人 時(shí)間: 2025-3-29 11:44 作者: obeisance 時(shí)間: 2025-3-29 18:29
Real-Time Head Orientation from a Monocular Camera Using Deep Neural Network,and we exploit the architecture in a data regression manner to learn the mapping function between visual appearance and three dimensional head orientation angles. Therefore, in contrast to classification based approaches, our system outputs continuous head orientation. The algorithm uses convolution作者: incarcerate 時(shí)間: 2025-3-29 20:41 作者: 金哥占卜者 時(shí)間: 2025-3-30 00:31
Visual Salience Learning via Low Rank Matrix Recovery,ehave differently over an individual image, and these saliency detection results often complement each other. To make full use of the advantages of the existing saliency detection methods, in this paper, we propose a salience learning model which combines various saliency detection methods such that作者: 緩解 時(shí)間: 2025-3-30 04:34
A New Framework for Multiclass Classification Using Multiview Assisted Adaptive Boosting,classifiers on each view and finally conglomerate them using weighted summation. Such approaches are void from inter-view communications and thus do not guarantee to yield the best possible ensemble classifier on the given sample-view space. This paper proposes a new algorithm for multiclass classif作者: 和平 時(shí)間: 2025-3-30 08:26 作者: Irritate 時(shí)間: 2025-3-30 14:24 作者: Morbid 時(shí)間: 2025-3-30 20:35 作者: Autobiography 時(shí)間: 2025-3-30 20:59 作者: 藝術(shù) 時(shí)間: 2025-3-31 03:41
Modeling the Temporality of Saliency,rames. The evolution of stimuli over a period longer than two frames has been largely ignored in saliency research. We argue that considering temporal evolution of trajectory even for a relatively short period can significantly extend the kind of meaningful regions that can be extracted from videos,作者: 越自我 時(shí)間: 2025-3-31 05:56
Salient Object Detection Using Window Mask Transferring with Multi-layer Background Contrast,e first one automatically encodes object location prior to predict visual saliency without the requirement of center-biased assumption, while the second one estimates image saliency using contrast with respect to background regions. The proposed framework consists of the following three basic steps:作者: 主講人 時(shí)間: 2025-3-31 12:04
Large Margin Multi-metric Learning for Face and Kinship Verification in the Wild,, most existing metric learning methods only learn one Mahalanobis distance metric from a single feature representation for each face image and cannot deal with multiple feature representations directly. In many face verification applications, we have access to extract multiple features for each fac作者: Aromatic 時(shí)間: 2025-3-31 14:20
A Three-Color Coupled Level-Set Algorithm for Simultaneous Multiple Cell Segmentation and Tracking,introduce “3LS”, an algorithm using only three level sets to segment and track arbitrary number of cells in time-lapse microscopic images. The cell number and positions are determined in the first frame by extracting concave points and fitting ellipses after initial segmentation. We construct a grap作者: Anthology 時(shí)間: 2025-3-31 18:51
OR-PCA with MRF for Robust Foreground Detection in Highly Dynamic Backgrounds,e shows more variations, such as water surface, waving trees, varying illumination conditions, etc. Recently, . (RPCA) shows a very nice framework for moving object detection. The background sequence is modeled by a low-dimensional subspace called . matrix and . constitutes the foreground objects. B作者: 變異 時(shí)間: 2025-4-1 00:32 作者: biosphere 時(shí)間: 2025-4-1 03:12 作者: 巫婆 時(shí)間: 2025-4-1 07:36 作者: declamation 時(shí)間: 2025-4-1 10:29
https://doi.org/10.1007/978-1-4899-6036-8planning. This paper describes an automatic vessel segmentation framework which can achieve highly accurate segmentation even in regions of low contrast and signal-to-noise-ratios (SNRs) and at vessel boundaries with disturbance induced by adjacent non-vessel pixels. There are two key contributions 作者: LAST 時(shí)間: 2025-4-1 16:17 作者: 集聚成團(tuán) 時(shí)間: 2025-4-1 21:40
