2013 2nd IAPR Asian Conference on Pattern Recognition最新文献_第9页

Novel Keypoint Registration for Fast and Robust Pose Detection on Mobile Phones 一种基于手机的快速鲁棒姿态检测关键点配准方法

2013 2nd IAPR Asian Conference on Pattern Recognition Pub Date : 2013-11-05 DOI: 10.1109/ACPR.2013.67

Tatsuya Kobayashi, H. Kato, H. Yanagihara

引用次数: 5

Learning Based Reconstruction of Grayscale Face Image from Far-Infrared Image 基于学习的远红外灰度人脸图像重建

2013 2nd IAPR Asian Conference on Pattern Recognition Pub Date : 2013-11-05 DOI: 10.1109/ACPR.2013.74

Brahmastro Kresnaraman, Y. Mekada, Tomokazu Takahashi, H. Murase

引用次数: 0

Common Landmark Discovery for Object-Level View Image Retrieval: Modeling and Matching of Scenes via Bag-of-Bounding-Boxes 面向对象级视图图像检索的常见地标发现:基于边界盒的场景建模与匹配

2013 2nd IAPR Asian Conference on Pattern Recognition Pub Date : 2013-11-05 DOI: 10.1109/ACPR.2013.19

Masatoshi Ando, Kanji Tanaka, Yousuke Inagaki, Yuuto Chokushi, Shogo Hanada

{"title":"Common Landmark Discovery for Object-Level View Image Retrieval: Modeling and Matching of Scenes via Bag-of-Bounding-Boxes","authors":"Masatoshi Ando, Kanji Tanaka, Yousuke Inagaki, Yuuto Chokushi, Shogo Hanada","doi":"10.1109/ACPR.2013.19","DOIUrl":"https://doi.org/10.1109/ACPR.2013.19","url":null,"abstract":"Object-level view image retrieval for robot vision applications has been actively studied recently, as they can provide semantic and compact method for efficient scene matching. In existing frameworks, landmark objects are extracted from an input view image by a pool of pretrained object detectors, and used as an image representation. To improve the compactness and autonomy of object-level view image retrieval, we here present a novel method called ``common landmark discovery\". Under this method, landmark objects are mined through common pattern discovery (CPD) between an input image and known reference images. This approach has three distinct advantages. First, the CPD-based object detection is unsupervised, and does not require pretrained object detector. Second, the method attempts to find fewer and larger object patterns, which leads to a compact and semantically robust view image descriptor. Third, the scene matching problem is efficiently solved as a lower-dimensional problem of computing region overlaps between landmark objects, using a compact image representation in a form of bag-of-bounding-boxes (BoBB).","PeriodicalId":365633,"journal":{"name":"2013 2nd IAPR Asian Conference on Pattern Recognition","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-11-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117144640","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: 3

Performance Evaluation of Image Feature Detectors and Descriptors for Outdoor-Scene Visual Navigation 户外场景视觉导航中图像特征检测器和描述符的性能评价

2013 2nd IAPR Asian Conference on Pattern Recognition Pub Date : 2013-11-05 DOI: 10.1109/ACPR.2013.159

Dzulfahmi, N. Ohta

{"title":"Performance Evaluation of Image Feature Detectors and Descriptors for Outdoor-Scene Visual Navigation","authors":"Dzulfahmi, N. Ohta","doi":"10.1109/ACPR.2013.159","DOIUrl":"https://doi.org/10.1109/ACPR.2013.159","url":null,"abstract":"Scene image matching is often used for positioning of a visually navigated autonomous robot. The robot memorizes the scene as an image at each navigation point in the teaching mode, and knows being at the same position when the outside scene is matched to the image in the playback mode. The scene matching is usually accomplished by feature-based image matching methods, such as SIFT or SURF. However the problem is that matching results of such methods are greatly affected by changes in illumination condition. Therefore, it is important to know which method is robust to the illumination change. Several performance evaluation results of these matching methods have been reported, but they are not focusing on illumination change problem. In this paper, we present performance comparison results of these feature-based image matching methods against illumination change in outdoor scenes assuming usage for visual navigation purpose. We also encounter another problem when conducting such the comparison for visual navigation. In this application, the matching score gradually increases as approaching the matching point, and gradually decreases as being apart from that point. This impedes to define the right matching response (ground truth). We present one method by which giving the right response.","PeriodicalId":365633,"journal":{"name":"2013 2nd IAPR Asian Conference on Pattern Recognition","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-11-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115606512","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

Combining Iris and Periocular Recognition Using Light Field Camera 结合虹膜和眼周识别的光场相机

2013 2nd IAPR Asian Conference on Pattern Recognition Pub Date : 2013-11-05 DOI: 10.1109/ACPR.2013.22

Ramachandra Raghavendra, K. Raja, Bian Yang, C. Busch

{"title":"Combining Iris and Periocular Recognition Using Light Field Camera","authors":"Ramachandra Raghavendra, K. Raja, Bian Yang, C. Busch","doi":"10.1109/ACPR.2013.22","DOIUrl":"https://doi.org/10.1109/ACPR.2013.22","url":null,"abstract":"Iris and Periocular biometrics has proved its effectiveness in accurately verifying the subject of interest. Recent improvements in visible spectrum Iris and Periocular verification have further boosted its application to unconstrained scenarios. However existing visible Iris verification systems suffer from low quality samples because of the limited depth-of-field exhibited by the conventional Iris capture systems. In this work, we propose a robust Iris and Periocular erification scheme in visible spectrum using Light Field Camera (LFC). Since the light field camera can provide multiple focus images in single capture, we are motivated to investigate its applicability for robust Iris and Periocular verification by exploring its all-in-focus property. Further, the use of all-in-focus property will extend the depth-of-focus and overcome the problem of focus that plays a predominant role in robust Iris and Periocular verification. We first collect a new Iris and Periocular biometric database using both light field and conventional camera by simulating real life scenarios. We then propose a new scheme for feature extraction and classification by exploring the combination of Local Binary Patterns (LBP) and Sparse Reconstruction Classifier (SRC). Extensive experiments are carried out on the newly collected database to bring out the merits and demerits on applicability of light field camera for Iris and Periocular verification. Finally, we also present the results on combining the information from Iris and Periocular biometrics using weighted sum rule.","PeriodicalId":365633,"journal":{"name":"2013 2nd IAPR Asian Conference on Pattern Recognition","volume":"193 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-11-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122546824","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: 46

Robust Angle Invariant 1D Barcode Detection 鲁棒角度不变一维条码检测

2013 2nd IAPR Asian Conference on Pattern Recognition Pub Date : 2013-11-05 DOI: 10.1109/ACPR.2013.17

Alessandro Zamberletti, I. Gallo, S. Albertini

{"title":"Robust Angle Invariant 1D Barcode Detection","authors":"Alessandro Zamberletti, I. Gallo, S. Albertini","doi":"10.1109/ACPR.2013.17","DOIUrl":"https://doi.org/10.1109/ACPR.2013.17","url":null,"abstract":"Barcode reading mobile applications that identify products from pictures taken using mobile devices are widely used by customers to perform online price comparisons or to access reviews written by others. Most of the currently available barcode reading approaches focus on decoding degraded barcodes and treat the underlying barcode detection task as a side problem that can be addressed using appropriate object detection methods. However, the majority of modern mobile devices do not meet the minimum working requirements of complex general purpose object detection algorithms and most of the efficient specifically designed barcode detection algorithms require user interaction to work properly. In this paper, we present a novel method for barcode detection in camera captured images based on a supervised machine learning algorithm that identifies one-dimensional barcodes in the two-dimensional Hough Transform space. Our model is angle invariant, requires no user interaction and can be executed on a modern mobile device. It achieves excellent results for two standard one-dimensional barcode datasets: WWU Muenster Barcode Database and ArTe-Lab 1D Medium Barcode Dataset. Moreover, we prove that it is possible to enhance the overall performance of a state-of-the-art barcode reading algorithm by combining it with our detection method.","PeriodicalId":365633,"journal":{"name":"2013 2nd IAPR Asian Conference on Pattern Recognition","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-11-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123224973","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: 51

Traffic Sign Recognition Using Complementary Features 利用互补特征识别交通标志

2013 2nd IAPR Asian Conference on Pattern Recognition Pub Date : 2013-11-05 DOI: 10.1109/ACPR.2013.63

Suisui Tang, Lin-Lin Huang

引用次数: 28

A Majorization-Minimization Approach to Lq Norm Multiple Kernel Learning Lq范数多核学习的最大化-最小化方法

2013 2nd IAPR Asian Conference on Pattern Recognition Pub Date : 2013-11-05 DOI: 10.1109/ACPR.2013.54

Zhizhen Liang, Shixiong Xia, Jin Liu, Yong Zhou, Lei Zhang

引用次数: 1

Gait Verification System for Supporting Criminal Investigation 支持刑事侦查的步态验证系统

2013 2nd IAPR Asian Conference on Pattern Recognition Pub Date : 2013-11-05 DOI: 10.1109/ACPR.2013.195

D. Muramatsu, Yasushi Makihara, Haruyuki Iwama, Takuya Tanoue, Y. Yagi

引用次数: 18

Set-Based Feature Learning for Person Re-identification via Third-Party Images 基于集合特征学习的第三方图像人物再识别

2013 2nd IAPR Asian Conference on Pattern Recognition Pub Date : 2013-11-05 DOI: 10.1109/ACPR.2013.87

Yanna Zhao, Lei Wang, Yuncai Liu

引用次数: 0