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

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

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

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

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

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

Joint hierarchical learning for efficient multi-class object detection 高效多类目标检测的联合分层学习

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.2014.6836090

Hamidreza Odabai Fard, M. Chaouch, Q. Pham, A. Vacavant, T. Chateau

引用次数: 2

Mining discriminative 3D Poselet for cross-view action recognition 面向交叉视角动作识别的判别性三维波selet挖掘

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.2014.6836043

Jiang Wang, Xiaohan Nie, Yin Xia, Ying Wu

引用次数: 2

Transfer learning via attributes for improved on-the-fly classification 通过属性迁移学习改进动态分类

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.2014.6836097

Praveen Kulkarni, Gaurav Sharma, J. Zepeda, Louis Chevallier

{"title":"Transfer learning via attributes for improved on-the-fly classification","authors":"Praveen Kulkarni, Gaurav Sharma, J. Zepeda, Louis Chevallier","doi":"10.1109/WACV.2014.6836097","DOIUrl":"https://doi.org/10.1109/WACV.2014.6836097","url":null,"abstract":"Retrieving images for an arbitrary user query, provided in textual form, is a challenging problem. A recently proposed method addresses this by constructing a visual classifier with images returned by an internet image search engine, based on the user query, as positive images while using a fixed pool of negative images. However, in practice, not all the images obtained from internet image search are always pertinent to the query; some might contain abstract or artistic representation of the content and some might have artifacts. Such images degrade the performance of on-the-fly constructed classifier. We propose a method for improving the performance of on-the-fly classifiers by using transfer learning via attributes. We first map the textual query to a set of known attributes and then use those attributes to prune the set of images downloaded from the internet. This pruning step can be seen as zero-shot learning of the visual classifier for the textual user query, which transfers knowledge from the attribute domain to the query domain. We also use the attributes along with the on-the-fly classifier to score the database images and obtain a hybrid ranking. We show interesting qualitative results and demonstrate by experiments with standard datasets that the proposed method improves upon the baseline on-the-fly classification system.","PeriodicalId":73325,"journal":{"name":"IEEE Winter Conference on Applications of Computer Vision. IEEE Winter Conference on Applications of Computer Vision","volume":"168 1","pages":"220-226"},"PeriodicalIF":0.0,"publicationDate":"2014-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86887252","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: 8

Optical filter selection for automatic visual inspection 光学滤光片选择自动目视检查

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.2014.6836110

Matthias Richter, J. Beyerer

{"title":"Optical filter selection for automatic visual inspection","authors":"Matthias Richter, J. Beyerer","doi":"10.1109/WACV.2014.6836110","DOIUrl":"https://doi.org/10.1109/WACV.2014.6836110","url":null,"abstract":"The color of a material is one of the most frequently used features in automated visual inspection systems. While this is sufficient for many “easy” tasks, mixed and organic materials usually require more complex features. Spectral signatures, especially in the near infrared range, have been proven useful in many cases. However, hyperspectral imaging devices are still very costly and too slow to use them in practice. As a work-around, off-the-shelve cameras and optical filters are used to extract few characteristic features from the spectra. Often, these filters are selected by a human expert in a time consuming and error prone process; surprisingly few works are concerned with automatic selection of suitable filters. We approach this problem by stating filter selection as feature selection problem. In contrast to existing techniques that are mainly concerned with filter design, our approach explicitly selects the best out of a large set of given filters. Our method becomes most appealing for use in an industrial setting, when this selection represents (physically) available filters. We show the application of our technique by implementing six different selection strategies and applying each to two real-world sorting problems.","PeriodicalId":73325,"journal":{"name":"IEEE Winter Conference on Applications of Computer Vision. IEEE Winter Conference on Applications of Computer Vision","volume":"2021 1","pages":"123-128"},"PeriodicalIF":0.0,"publicationDate":"2014-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87954008","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: 2