IEEE Winter Conference on Applications of Computer Vision. IEEE Winter Conference on Applications of Computer Vision最新文献_第10页

Bayesian Optimization with an Empirical Hardness Model for approximate Nearest Neighbour Search 基于经验硬度模型的近似最近邻搜索贝叶斯优化

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.2014.6836049

Julieta Martinez, J. Little, Nando de Freitas

引用次数: 2

A lp-norm MTMKL framework for simultaneous detection of multiple facial action units 同时检测多个面部动作单元的lp范数MTMKL框架

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.2014.6835735

Xiao Zhang, M. Mahoor, S. Mavadati, J. Cohn

{"title":"A lp-norm MTMKL framework for simultaneous detection of multiple facial action units","authors":"Xiao Zhang, M. Mahoor, S. Mavadati, J. Cohn","doi":"10.1109/WACV.2014.6835735","DOIUrl":"https://doi.org/10.1109/WACV.2014.6835735","url":null,"abstract":"Facial action unit (AU) detection is a challenging topic in computer vision and pattern recognition. Most existing approaches design classifiers to detect AUs individually or AU combinations without considering the intrinsic relations among AUs. This paper presents a novel method, lp-norm multi-task multiple kernel learning (MTMKL), that jointly learns the classifiers for detecting the absence and presence of multiple AUs. lp-norm MTMKL is an extension of the regularized multi-task learning, which learns shared kernels from a given set of base kernels among all the tasks within Support Vector Machines (SVM). Our approach has several advantages over existing methods: (1) AU detection work is transformed to a MTL problem, where given a specific frame, multiple AUs are detected simultaneously by exploiting their inter-relations; (2) lp-norm multiple kernel learning is applied to increase the discriminant power of classifiers. Our experimental results on the CK+ and DISFA databases show that the proposed method outperforms the state-of-the-art methods for AU detection.","PeriodicalId":73325,"journal":{"name":"IEEE Winter Conference on Applications of Computer Vision. IEEE Winter Conference on Applications of Computer Vision","volume":"17 1","pages":"1104-1111"},"PeriodicalIF":0.0,"publicationDate":"2014-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73480128","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: 38

Coupling video segmentation and action recognition 视频分割与动作识别的耦合

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.2014.6836045

Amir Ghodrati, M. Pedersoli, T. Tuytelaars

引用次数: 1

Finger-knuckle-print verification based on vector consistency of corresponding interest points 基于相应兴趣点向量一致性的指关节指纹验证

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.2014.6835996

Min-Ki Kim, P. Flynn

引用次数: 2

Linear Local Distance coding for classification of HEp-2 staining patterns 线性局部距离编码用于HEp-2染色模式分类

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.2014.6836073

Xiang Xu, F. Lin, Carol Ng, K. Leong

{"title":"Linear Local Distance coding for classification of HEp-2 staining patterns","authors":"Xiang Xu, F. Lin, Carol Ng, K. Leong","doi":"10.1109/WACV.2014.6836073","DOIUrl":"https://doi.org/10.1109/WACV.2014.6836073","url":null,"abstract":"Indirect Immunofluorescence (IIF) on Human Epithelial-2 (HEp-2) cells is the recommended methodology for detecting some specific autoimmune diseases by searching for antinuclear antibodies (ANAs) within a patient's serum. Due to the limitations of IIF such as subjective evaluation, automated Computer-Aided Diagnosis (CAD) system is required for diagnostic purposes. In particular, staining patterns classification of HEp-2 cells is a challenging task. In this paper, we adopt a feature extraction-coding-pooling framework which has shown impressive performance in image classification tasks, because it can obtain discriminative and effective image representation. However, the information loss is inevitable in the coding process. Therefore, we propose a Linear Local Distance (LLD) coding method to capture more discriminative information. LLD transforms original local feature to local distance vector by searching for local nearest few neighbors of local feature in the class-specific manifolds. The obtained local distance vector is further encoded and pooled together to get salient image representation. We demonstrate the effectiveness of LLD method on a public HEp-2 cells dataset containing six major staining patterns. Experimental results show that our approach has a superior performance to the state-of-the-art coding methods for staining patterns classification of HEp-2 cells.","PeriodicalId":73325,"journal":{"name":"IEEE Winter Conference on Applications of Computer Vision. IEEE Winter Conference on Applications of Computer Vision","volume":"8 1","pages":"393-400"},"PeriodicalIF":0.0,"publicationDate":"2014-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81445168","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: 6

Structure-aware keypoint tracking for partial occlusion handling 部分遮挡处理的结构感知关键点跟踪

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.2014.6836011

W. Bouachir, Guillaume-Alexandre Bilodeau

{"title":"Structure-aware keypoint tracking for partial occlusion handling","authors":"W. Bouachir, Guillaume-Alexandre Bilodeau","doi":"10.1109/WACV.2014.6836011","DOIUrl":"https://doi.org/10.1109/WACV.2014.6836011","url":null,"abstract":"This paper introduces a novel keypoint-based method for visual object tracking. To represent the target, we use a new model combining color distribution with keypoints. The appearance model also incorporates the spatial layout of the keypoints, encoding the object structure learned during tracking. With this multi-feature appearance model, our Structure-Aware Tracker (SAT) estimates accurately the target location using three main steps. First, the search space is reduced to the most likely image regions with a probabilistic approach. Second, the target location is estimated in the reduced search space using deterministic keypoint matching. Finally, the location prediction is corrected by exploiting the keypoint structural model with a voting-based method. By applying our SAT on several tracking problems, we show that location correction based on structural constraints is a key technique to improve prediction in moderately crowded scenes, even if only a small part of the target is visible. We also conduct comparison with a number of state-of-the-art trackers and demonstrate the competitiveness of the proposed method.","PeriodicalId":73325,"journal":{"name":"IEEE Winter Conference on Applications of Computer Vision. IEEE Winter Conference on Applications of Computer Vision","volume":"28 1","pages":"877-884"},"PeriodicalIF":0.0,"publicationDate":"2014-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90531344","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: 20

Multi-view action recognition one camera at a time 多视角动作识别，一次一个摄像头

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.2014.6836047

Scott Spurlock, Richard Souvenir

引用次数: 4

Interactively test driving an object detector: Estimating performance on unlabeled data 交互式测试驱动对象检测器:评估未标记数据上的性能

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.2014.6836104

Rushil Anirudh, P. Turaga

引用次数: 5

Comparison of face detection and image classification for detecting front seat passengers in vehicles 车辆前座乘客人脸检测与图像分类的比较

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.2014.6835994

Y. Artan, P. Paul, F. Perronnin, A. Burry

{"title":"Comparison of face detection and image classification for detecting front seat passengers in vehicles","authors":"Y. Artan, P. Paul, F. Perronnin, A. Burry","doi":"10.1109/WACV.2014.6835994","DOIUrl":"https://doi.org/10.1109/WACV.2014.6835994","url":null,"abstract":"Due to the high volume of traffic on modern roadways, transportation agencies have proposed High Occupancy Vehicle (HOV) lanes and High Occupancy Tolling (HOT) lanes to promote car pooling. However, enforcement of the rules of these lanes is currently performed by roadside enforcement officers using visual observation. Manual roadside enforcement is known to be inefficient, costly, potentially dangerous, and ultimately ineffective. Violation rates up to 50%-80% have been reported, while manual enforcement rates of less than 10% are typical. Therefore, there is a need for automated vehicle occupancy detection to support HOV/HOT lane enforcement. A key component of determining vehicle occupancy is to determine whether or not the vehicle's front passenger seat is occupied. In this paper, we examine two methods of determining vehicle front seat occupancy using a near infrared (NIR) camera system pointed at the vehicle's front windshield. The first method examines a state-of-the-art deformable part model (DPM) based face detection system that is robust to facial pose. The second method examines state-of-the-art local aggregation based image classification using bag-of-visual-words (BOW) and Fisher vectors (FV). A dataset of 3000 images was collected on a public roadway and is used to perform the comparison. From these experiments it is clear that the image classification approach is superior for this problem.","PeriodicalId":73325,"journal":{"name":"IEEE Winter Conference on Applications of Computer Vision. IEEE Winter Conference on Applications of Computer Vision","volume":"1 1","pages":"1006-1012"},"PeriodicalIF":0.0,"publicationDate":"2014-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74341594","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: 6

Scale-Space SIFT flow 尺度空间SIFT流

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.2014.6835734

Weichao Qiu, Xinggang Wang, X. Bai, A. Yuille, Z. Tu

引用次数: 37