2013 IEEE International Conference on Computer Vision最新文献_第10页

A Unified Probabilistic Approach Modeling Relationships between Attributes and Objects 一种统一的概率方法来建模属性和对象之间的关系

2013 IEEE International Conference on Computer Vision Pub Date : 2013-12-01 DOI: 10.1109/ICCV.2013.264

Xiaoyang Wang, Q. Ji

引用次数: 79

Heterogeneous Auto-similarities of Characteristics (HASC): Exploiting Relational Information for Classification 异构自相似特征(HASC):利用关系信息进行分类

2013 IEEE International Conference on Computer Vision Pub Date : 2013-12-01 DOI: 10.1109/ICCV.2013.105

Marco San-Biagio, M. Crocco, M. Cristani, Samuele Martelli, Vittorio Murino

{"title":"Heterogeneous Auto-similarities of Characteristics (HASC): Exploiting Relational Information for Classification","authors":"Marco San-Biagio, M. Crocco, M. Cristani, Samuele Martelli, Vittorio Murino","doi":"10.1109/ICCV.2013.105","DOIUrl":"https://doi.org/10.1109/ICCV.2013.105","url":null,"abstract":"Capturing the essential characteristics of visual objects by considering how their features are inter-related is a recent philosophy of object classification. In this paper, we embed this principle in a novel image descriptor, dubbed Heterogeneous Auto-Similarities of Characteristics (HASC). HASC is applied to heterogeneous dense features maps, encoding linear relations by co variances and nonlinear associations through information-theoretic measures such as mutual information and entropy. In this way, highly complex structural information can be expressed in a compact, scale invariant and robust manner. The effectiveness of HASC is tested on many diverse detection and classification scenarios, considering objects, textures and pedestrians, on widely known benchmarks (Caltech-101, Brodatz, Daimler Multi-Cue). In all the cases, the results obtained with standard classifiers demonstrate the superiority of HASC with respect to the most adopted local feature descriptors nowadays, such as SIFT, HOG, LBP and feature co variances. In addition, HASC sets the state-of-the-art on the Brodatz texture dataset and the Daimler Multi-Cue pedestrian dataset, without exploiting ad-hoc sophisticated classifiers.","PeriodicalId":6351,"journal":{"name":"2013 IEEE International Conference on Computer Vision","volume":"48 1","pages":"809-816"},"PeriodicalIF":0.0,"publicationDate":"2013-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87623164","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: 41

Fingerspelling Recognition with Semi-Markov Conditional Random Fields 基于半马尔可夫条件随机场的指纹拼写识别

2013 IEEE International Conference on Computer Vision Pub Date : 2013-12-01 DOI: 10.1109/ICCV.2013.192

Taehwan Kim, Gregory Shakhnarovich, Karen Livescu

{"title":"Fingerspelling Recognition with Semi-Markov Conditional Random Fields","authors":"Taehwan Kim, Gregory Shakhnarovich, Karen Livescu","doi":"10.1109/ICCV.2013.192","DOIUrl":"https://doi.org/10.1109/ICCV.2013.192","url":null,"abstract":"Recognition of gesture sequences is in general a very difficult problem, but in certain domains the difficulty may be mitigated by exploiting the domain's ``grammar''. One such grammatically constrained gesture sequence domain is sign language. In this paper we investigate the case of finger spelling recognition, which can be very challenging due to the quick, small motions of the fingers. Most prior work on this task has assumed a closed vocabulary of finger spelled words, here we study the more natural open-vocabulary case, where the only domain knowledge is the possible finger spelled letters and statistics of their sequences. We develop a semi-Markov conditional model approach, where feature functions are defined over segments of video and their corresponding letter labels. We use classifiers of letters and linguistic hand shape features, along with expected motion profiles, to define segmental feature functions. This approach improves letter error rate (Levenshtein distance between hypothesized and correct letter sequences) from 16.3% using a hidden Markov model baseline to 11.6% using the proposed semi-Markov model.","PeriodicalId":6351,"journal":{"name":"2013 IEEE International Conference on Computer Vision","volume":"33 1","pages":"1521-1528"},"PeriodicalIF":0.0,"publicationDate":"2013-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84428479","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: 24

Efficient Image Dehazing with Boundary Constraint and Contextual Regularization 基于边界约束和上下文正则化的高效图像去雾

2013 IEEE International Conference on Computer Vision Pub Date : 2013-12-01 DOI: 10.1109/ICCV.2013.82

Gaofeng Meng, Ying Wang, Jiangyong Duan, Shiming Xiang, Chunhong Pan

引用次数: 897

Coupling Alignments with Recognition for Still-to-Video Face Recognition 静态到视频人脸识别的耦合对齐与识别

2013 IEEE International Conference on Computer Vision Pub Date : 2013-12-01 DOI: 10.1109/ICCV.2013.409

Zhiwu Huang, Xiaowei Zhao, S. Shan, Ruiping Wang, Xilin Chen

{"title":"Coupling Alignments with Recognition for Still-to-Video Face Recognition","authors":"Zhiwu Huang, Xiaowei Zhao, S. Shan, Ruiping Wang, Xilin Chen","doi":"10.1109/ICCV.2013.409","DOIUrl":"https://doi.org/10.1109/ICCV.2013.409","url":null,"abstract":"The Still-to-Video (S2V) face recognition systems typically need to match faces in low-quality videos captured under unconstrained conditions against high quality still face images, which is very challenging because of noise, image blur, low face resolutions, varying head pose, complex lighting, and alignment difficulty. To address the problem, one solution is to select the frames of `best quality' from videos (hereinafter called quality alignment in this paper). Meanwhile, the faces in the selected frames should also be geometrically aligned to the still faces offline well-aligned in the gallery. In this paper, we discover that the interactions among the three tasks-quality alignment, geometric alignment and face recognition-can benefit from each other, thus should be performed jointly. With this in mind, we propose a Coupling Alignments with Recognition (CAR) method to tightly couple these tasks via low-rank regularized sparse representation in a unified framework. Our method makes the three tasks promote mutually by a joint optimization in an Augmented Lagrange Multiplier routine. Extensive experiments on two challenging S2V datasets demonstrate that our method outperforms the state-of-the-art methods impressively.","PeriodicalId":6351,"journal":{"name":"2013 IEEE International Conference on Computer Vision","volume":"50 1","pages":"3296-3303"},"PeriodicalIF":0.0,"publicationDate":"2013-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86659637","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: 35

No Matter Where You Are: Flexible Graph-Guided Multi-task Learning for Multi-view Head Pose Classification under Target Motion 无论你在哪里:目标运动下多视角头部姿势分类的灵活图形引导多任务学习

2013 IEEE International Conference on Computer Vision Pub Date : 2013-12-01 DOI: 10.1109/ICCV.2013.150

Yan Yan, E. Ricci, Subramanian Ramanathan, O. Lanz, N. Sebe

{"title":"No Matter Where You Are: Flexible Graph-Guided Multi-task Learning for Multi-view Head Pose Classification under Target Motion","authors":"Yan Yan, E. Ricci, Subramanian Ramanathan, O. Lanz, N. Sebe","doi":"10.1109/ICCV.2013.150","DOIUrl":"https://doi.org/10.1109/ICCV.2013.150","url":null,"abstract":"We propose a novel Multi-Task Learning framework (FEGA-MTL) for classifying the head pose of a person who moves freely in an environment monitored by multiple, large field-of-view surveillance cameras. As the target (person) moves, distortions in facial appearance owing to camera perspective and scale severely impede performance of traditional head pose classification methods. FEGA-MTL operates on a dense uniform spatial grid and learns appearance relationships across partitions as well as partition-specific appearance variations for a given head pose to build region-specific classifiers. Guided by two graphs which a-priori model appearance similarity among (i) grid partitions based on camera geometry and (ii) head pose classes, the learner efficiently clusters appearance wise related grid partitions to derive the optimal partitioning. For pose classification, upon determining the target's position using a person tracker, the appropriate region specific classifier is invoked. Experiments confirm that FEGA-MTL achieves state-of-the-art classification with few training data.","PeriodicalId":6351,"journal":{"name":"2013 IEEE International Conference on Computer Vision","volume":"57 1","pages":"1177-1184"},"PeriodicalIF":0.0,"publicationDate":"2013-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87041528","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: 118

Learning to Predict Gaze in Egocentric Video 学习在自我中心视频中预测凝视

2013 IEEE International Conference on Computer Vision Pub Date : 2013-12-01 DOI: 10.1109/ICCV.2013.399

Yin Li, A. Fathi, James M. Rehg

引用次数: 240

Efficient and Robust Large-Scale Rotation Averaging 高效鲁棒的大规模旋转平均

2013 IEEE International Conference on Computer Vision Pub Date : 2013-12-01 DOI: 10.1109/ICCV.2013.70

Avishek Chatterjee, V. Govindu

{"title":"Efficient and Robust Large-Scale Rotation Averaging","authors":"Avishek Chatterjee, V. Govindu","doi":"10.1109/ICCV.2013.70","DOIUrl":"https://doi.org/10.1109/ICCV.2013.70","url":null,"abstract":"In this paper we address the problem of robust and efficient averaging of relative 3D rotations. Apart from having an interesting geometric structure, robust rotation averaging addresses the need for a good initialization for large scale optimization used in structure-from-motion pipelines. Such pipelines often use unstructured image datasets harvested from the internet thereby requiring an initialization method that is robust to outliers. Our approach works on the Lie group structure of 3D rotations and solves the problem of large-scale robust rotation averaging in two ways. Firstly, we use modern ℓ1 optimizers to carry out robust averaging of relative rotations that is efficient, scalable and robust to outliers. In addition, we also develop a two step method that uses the ℓ1 solution as an initialisation for an iteratively reweighted least squares (IRLS) approach. These methods achieve excellent results on large-scale, real world datasets and significantly outperform existing methods, i.e. the state-of-the-art discrete-continuous optimization method of [3] as well as the Weiszfeld method of [8]. We demonstrate the efficacy of our method on two large scale real world datasets and also provide the results of the two aforementioned methods for comparison.","PeriodicalId":6351,"journal":{"name":"2013 IEEE International Conference on Computer Vision","volume":"11 1","pages":"521-528"},"PeriodicalIF":0.0,"publicationDate":"2013-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85786140","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: 217

High Quality Shape from a Single RGB-D Image under Uncalibrated Natural Illumination 在未校准的自然照明下，从单个RGB-D图像获得高质量形状

2013 IEEE International Conference on Computer Vision Pub Date : 2013-12-01 DOI: 10.1109/ICCV.2013.204

Yudeog Han, Joon-Young Lee, In-So Kweon

{"title":"High Quality Shape from a Single RGB-D Image under Uncalibrated Natural Illumination","authors":"Yudeog Han, Joon-Young Lee, In-So Kweon","doi":"10.1109/ICCV.2013.204","DOIUrl":"https://doi.org/10.1109/ICCV.2013.204","url":null,"abstract":"We present a novel framework to estimate detailed shape of diffuse objects with uniform albedo from a single RGB-D image. To estimate accurate lighting in natural illumination environment, we introduce a general lighting model consisting of two components: global and local models. The global lighting model is estimated from the RGB-D input using the low-dimensional characteristic of a diffuse reflectance model. The local lighting model represents spatially varying illumination and it is estimated by using the smoothly-varying characteristic of illumination. With both the global and local lighting model, we can estimate complex lighting variations in uncontrolled natural illumination conditions accurately. For high quality shape capture, a shape-from-shading approach is applied with the estimated lighting model. Since the entire process is done with a single RGB-D input, our method is capable of capturing the high quality shape details of a dynamic object under natural illumination. Experimental results demonstrate the feasibility and effectiveness of our method that dramatically improves shape details of the rough depth input.","PeriodicalId":6351,"journal":{"name":"2013 IEEE International Conference on Computer Vision","volume":"47 1","pages":"1617-1624"},"PeriodicalIF":0.0,"publicationDate":"2013-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86319904","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: 97

Supervised Binary Hash Code Learning with Jensen Shannon Divergence 基于Jensen Shannon散度的监督二进制哈希码学习

2013 IEEE International Conference on Computer Vision Pub Date : 2013-12-01 DOI: 10.1109/ICCV.2013.325

Lixin Fan

引用次数: 13