IEEE Winter Conference on Applications of Computer Vision. IEEE Winter Conference on Applications of Computer Vision最新文献_第6页

Robust tracking and mapping with a handheld RGB-D camera 强大的跟踪和映射与手持RGB-D相机

IEEE Winter Conference on Applications of Computer Vision. IEEE Winter Conference on Applications of Computer Vision Pub Date : 2014-03-24 DOI: 10.1109/WACV.2014.6835732

Kyoung-Rok Lee, Truong Q. Nguyen

引用次数: 5

Unsupervised iterative manifold alignment via local feature histograms 基于局部特征直方图的无监督迭代流形对齐

IEEE Winter Conference on Applications of Computer Vision. IEEE Winter Conference on Applications of Computer Vision Pub Date : 2014-03-24 DOI: 10.1109/WACV.2014.6836051

Ke Fan, A. Mian, Wanquan Liu, Lin Li

{"title":"Unsupervised iterative manifold alignment via local feature histograms","authors":"Ke Fan, A. Mian, Wanquan Liu, Lin Li","doi":"10.1109/WACV.2014.6836051","DOIUrl":"https://doi.org/10.1109/WACV.2014.6836051","url":null,"abstract":"We propose a new unsupervised algorithm for the automatic alignment of two manifolds of different datasets with possibly different dimensionalities. Alignment is performed automatically without any assumptions on the correspondences between the two manifolds. The proposed algorithm automatically establishes an initial set of sparse correspondences between the two datasets by matching their underlying manifold structures. Local feature histograms are extracted at each point of the manifolds and matched using a robust algorithm to find the initial correspondences. Based on these sparse correspondences, an embedding space is estimated where the distance between the two manifolds is minimized while maximally retaining the original structure of the manifolds. The problem is formulated as a generalized eigenvalue problem and solved efficiently. Dense correspondences are then established between the two manifolds and the process is iteratively implemented until the two manifolds are correctly aligned consequently revealing their joint structure. We demonstrate the effectiveness of our algorithm on aligning protein structures, facial images of different subjects under pose variations and RGB and Depth data from Kinect. Comparison with an state-of-the-art algorithm shows the superiority of the proposed manifold alignment algorithm in terms of accuracy and computational time.","PeriodicalId":73325,"journal":{"name":"IEEE Winter Conference on Applications of Computer Vision. IEEE Winter Conference on Applications of Computer Vision","volume":"112 1","pages":"572-579"},"PeriodicalIF":0.0,"publicationDate":"2014-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74683469","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Fully automatic 3D facial expression recognition using local depth features 使用局部深度特征的全自动3D面部表情识别

IEEE Winter Conference on Applications of Computer Vision. IEEE Winter Conference on Applications of Computer Vision Pub Date : 2014-03-24 DOI: 10.1109/WACV.2014.6835736

Mingliang Xue, A. Mian, Wanquan Liu, Ling Li

{"title":"Fully automatic 3D facial expression recognition using local depth features","authors":"Mingliang Xue, A. Mian, Wanquan Liu, Ling Li","doi":"10.1109/WACV.2014.6835736","DOIUrl":"https://doi.org/10.1109/WACV.2014.6835736","url":null,"abstract":"Facial expressions form a significant part of our nonverbal communications and understanding them is essential for effective human computer interaction. Due to the diversity of facial geometry and expressions, automatic expression recognition is a challenging task. This paper deals with the problem of person-independent facial expression recognition from a single 3D scan. We consider only the 3D shape because facial expressions are mostly encoded in facial geometry deformations rather than textures. Unlike the majority of existing works, our method is fully automatic including the detection of landmarks. We detect the four eye corners and nose tip in real time on the depth image and its gradients using Haar-like features and AdaBoost classifier. From these five points, another 25 heuristic points are defined to extract local depth features for representing facial expressions. The depth features are projected to a lower dimensional linear subspace where feature selection is performed by maximizing their relevance and minimizing their redundancy. The selected features are then used to train a multi-class SVM for the final classification. Experiments on the benchmark BU-3DFE database show that the proposed method outperforms existing automatic techniques, and is comparable even to the approaches using manual landmarks.","PeriodicalId":73325,"journal":{"name":"IEEE Winter Conference on Applications of Computer Vision. IEEE Winter Conference on Applications of Computer Vision","volume":"36 1","pages":"1096-1103"},"PeriodicalIF":0.0,"publicationDate":"2014-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75177414","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 14

Simultaneous recognition of facial expression and identity via sparse representation 基于稀疏表示的面部表情和身份的同时识别

IEEE Winter Conference on Applications of Computer Vision. IEEE Winter Conference on Applications of Computer Vision Pub Date : 2014-03-24 DOI: 10.1109/WACV.2014.6835986

M. Mohammadi, E. Fatemizadeh, M. Mahoor

引用次数: 9

Selection of universal features for image classification 用于图像分类的通用特征选择

IEEE Winter Conference on Applications of Computer Vision. IEEE Winter Conference on Applications of Computer Vision Pub Date : 2014-03-24 DOI: 10.1109/WACV.2014.6836078

Pedro A. Rodriguez, Nathan G. Drenkow, D. DeMenthon, Zachary H. Koterba, Kathleen Kauffman, Duane C. Cornish, Bart Paulhamus, R. J. Vogelstein

引用次数: 3

Active Clustering with Ensembles for Social structure extraction 基于集成的主动聚类社会结构提取

IEEE Winter Conference on Applications of Computer Vision. IEEE Winter Conference on Applications of Computer Vision Pub Date : 2014-03-24 DOI: 10.1109/WACV.2014.6835999

Jeremiah R. Barr, Leonardo A. Cament, K. Bowyer, P. Flynn

引用次数: 23

Video alignment to a common reference 视频对齐到一个共同的参考

IEEE Winter Conference on Applications of Computer Vision. IEEE Winter Conference on Applications of Computer Vision Pub Date : 2014-03-24 DOI: 10.1109/WACV.2014.6836020

Rahul Dutta, B. Draper, J. Beveridge

{"title":"Video alignment to a common reference","authors":"Rahul Dutta, B. Draper, J. Beveridge","doi":"10.1109/WACV.2014.6836020","DOIUrl":"https://doi.org/10.1109/WACV.2014.6836020","url":null,"abstract":"Handheld videos include unintentional motion (jitter) and often intentional motion (pan and/or zoom). Human viewers prefer to see jitter removed, creating a smoothly moving camera. For video analysis, in contrast, aligning to a fixed stable background is sometimes preferable. This paper presents an algorithm that removes both forms of motion using a novel and efficient way of tracking background points while ignoring moving foreground points. The approach is related to image mosaicing, but the result is a video rather than an enlarged still image. It is also related to multiple object tracking approaches, but simpler since moving objects need not be explicitly tracked. The algorithm presented takes as input a video and returns one or several stabilized videos. Videos are broken into parts when the algorithm detects the background changing and it becomes necessary to fix upon a new background. Our approach assumes the person holding the camera is standing in one place and that objects in motion do not dominate the image. Our algorithm performs better than several previously published approaches when compared on 1,401 handheld videos from the recently released Point-and-Shoot Face Recognition Challenge (PASC). The source code for this algorithm is being made available.","PeriodicalId":73325,"journal":{"name":"IEEE Winter Conference on Applications of Computer Vision. IEEE Winter Conference on Applications of Computer Vision","volume":"6 1","pages":"808-815"},"PeriodicalIF":0.0,"publicationDate":"2014-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78767555","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 4

AutoCaption: Automatic caption generation for personal photos AutoCaption:自动生成个人照片的说明文字

IEEE Winter Conference on Applications of Computer Vision. IEEE Winter Conference on Applications of Computer Vision Pub Date : 2014-03-24 DOI: 10.1109/WACV.2014.6835988

Krishnan Ramnath, Simon Baker, Lucy Vanderwende, M. El-Saban, Sudipta N. Sinha, A. Kannan, N. Hassan, Michel Galley, Yi Yang, Deva Ramanan, Alessandro Bergamo, L. Torresani

引用次数: 26

A fully implicit alternating direction method of multipliers for the minimization of convex problems with an application to motion segmentation 一种用于最小化凸问题的乘法器的完全隐式交替方向方法及其在运动分割中的应用

IEEE Winter Conference on Applications of Computer Vision. IEEE Winter Conference on Applications of Computer Vision Pub Date : 2014-03-24 DOI: 10.1109/WACV.2014.6836018

Karin Tichmann, O. Junge

引用次数: 0

Interactive video segmentation using occlusion boundaries and temporally coherent superpixels 使用遮挡边界和时间相干超像素的交互式视频分割

IEEE Winter Conference on Applications of Computer Vision. IEEE Winter Conference on Applications of Computer Vision Pub Date : 2014-03-24 DOI: 10.1109/WACV.2014.6836023

Radu Dondera, Vlad I. Morariu, Yulu Wang, L. Davis

引用次数: 15