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Image Processing: Machine Vision Applications最新文献

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Marker-Less Augmented Reality Framework Using On-Site 3D Line-Segment-based Model Generation 使用现场3D线段模型生成的无标记增强现实框架
Image Processing: Machine Vision Applications Pub Date : 2016-03-01 DOI: 10.2352/ISSN.2470-1173.2016.14.IPMVA-382
Yusuke Nakayama, H. Saito, M. Shimizu, Nobuyasu Yamaguchi
{"title":"Marker-Less Augmented Reality Framework Using On-Site 3D Line-Segment-based Model Generation","authors":"Yusuke Nakayama, H. Saito, M. Shimizu, Nobuyasu Yamaguchi","doi":"10.2352/ISSN.2470-1173.2016.14.IPMVA-382","DOIUrl":"https://doi.org/10.2352/ISSN.2470-1173.2016.14.IPMVA-382","url":null,"abstract":"","PeriodicalId":262142,"journal":{"name":"Image Processing: Machine Vision Applications","volume":"126 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132971513","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
Validation of a Novel Geometric Coordination Registration using Manual and Semi-automatic Registration in Cone-beam Computed Tomogram 锥束计算机层析图手工与半自动配准的几何配准方法的验证
Image Processing: Machine Vision Applications Pub Date : 2016-02-14 DOI: 10.2352/ISSN.2470-1173.2016.14.IPMVA-373
W. Y. Lam, H. Luk, Henry Y. T. Ngan, R. T. Hsung, T. Goto, E. Pow
{"title":"Validation of a Novel Geometric Coordination Registration using Manual and Semi-automatic Registration in Cone-beam Computed Tomogram","authors":"W. Y. Lam, H. Luk, Henry Y. T. Ngan, R. T. Hsung, T. Goto, E. Pow","doi":"10.2352/ISSN.2470-1173.2016.14.IPMVA-373","DOIUrl":"https://doi.org/10.2352/ISSN.2470-1173.2016.14.IPMVA-373","url":null,"abstract":"Cartesian coordinates define on a physical cubic corner (CC) with the corner tip as the origin and three corresponding line angles as (x, y, z)-axes. In its image (virtual) domains such as these obtained by cone-beam computed tomography (CBCT) and optical surface scanning, a single coordinate can then be registered based on the CC. The advantage of using a CC in registration is simple and accurate physical coordinate measurement. The accuracy of image-to-physical (IP) and imageto-image (II) transformations, measured by target registration error (TRE), can then be validated by comparing coordinates of target points in the virtual domains to that of the physical control. For the CBCT, the registration may be performed manually using a surgical planning software SimPlant Pro (manual registration (MR)) or semi-automatically using MeshLab and 3D Slicer (semiautomatic registration (SR)) matching the virtual display axes to the corresponding (x-y-z)-axes. This study aims to validate the use of CC as a surgical stereotactic marker by measuring TRE in MR and SR respectively. Mean TRE is 0.56 +/0.24 mm for MR and 0.39 +/0.21 mm for SR. The SR results in a more accurate registration than the MR and point-based registration with 20 fiducial points. TRE of the MR is less than 1.0 mm and still acceptable clinically.","PeriodicalId":262142,"journal":{"name":"Image Processing: Machine Vision Applications","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-02-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124870626","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
Dense Correspondence using Multilevel Segmentation and Affine Transformation 基于多级分割和仿射变换的密集对应
Image Processing: Machine Vision Applications Pub Date : 2016-02-14 DOI: 10.2352/ISSN.2470-1173.2016.14.IPMVA-384
Sungil Choi, Kihong Park, Seungryong Kim, K. Sohn
{"title":"Dense Correspondence using Multilevel Segmentation and Affine Transformation","authors":"Sungil Choi, Kihong Park, Seungryong Kim, K. Sohn","doi":"10.2352/ISSN.2470-1173.2016.14.IPMVA-384","DOIUrl":"https://doi.org/10.2352/ISSN.2470-1173.2016.14.IPMVA-384","url":null,"abstract":"","PeriodicalId":262142,"journal":{"name":"Image Processing: Machine Vision Applications","volume":"488 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-02-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125219540","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: 0
STABLE: Stochastic Binary Local Descriptor for High-performance Dense Stereo Matching STABLE:用于高性能密集立体匹配的随机二进制局部描述子
Image Processing: Machine Vision Applications Pub Date : 2016-02-14 DOI: 10.2352/ISSN.2470-1173.2016.14.IPMVA-387
S. Stolc, K. Valentín, R. Huber-Mörk
{"title":"STABLE: Stochastic Binary Local Descriptor for High-performance Dense Stereo Matching","authors":"S. Stolc, K. Valentín, R. Huber-Mörk","doi":"10.2352/ISSN.2470-1173.2016.14.IPMVA-387","DOIUrl":"https://doi.org/10.2352/ISSN.2470-1173.2016.14.IPMVA-387","url":null,"abstract":"","PeriodicalId":262142,"journal":{"name":"Image Processing: Machine Vision Applications","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-02-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121594210","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
Automated Lane Detection by K-means Clustering: A Machine Learning Approach 基于k均值聚类的自动车道检测:一种机器学习方法
Image Processing: Machine Vision Applications Pub Date : 2016-02-14 DOI: 10.2352/ISSN.2470-1173.2016.14.IPMVA-386
R. Ajaykumar, Arpit Gupta, S. Merchant
{"title":"Automated Lane Detection by K-means Clustering: A Machine Learning Approach","authors":"R. Ajaykumar, Arpit Gupta, S. Merchant","doi":"10.2352/ISSN.2470-1173.2016.14.IPMVA-386","DOIUrl":"https://doi.org/10.2352/ISSN.2470-1173.2016.14.IPMVA-386","url":null,"abstract":"","PeriodicalId":262142,"journal":{"name":"Image Processing: Machine Vision Applications","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-02-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122139934","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: 21
Data-driven Approach to Aesthetic Enhancement 数据驱动的美学增强方法
Image Processing: Machine Vision Applications Pub Date : 2016-02-14 DOI: 10.2352/ISSN.2470-1173.2016.14.IPMVA-374
Jihye Choi, S. Koh, Jongwoo Kwack, Yonghun Kwon, Hyunjung Shim
{"title":"Data-driven Approach to Aesthetic Enhancement","authors":"Jihye Choi, S. Koh, Jongwoo Kwack, Yonghun Kwon, Hyunjung Shim","doi":"10.2352/ISSN.2470-1173.2016.14.IPMVA-374","DOIUrl":"https://doi.org/10.2352/ISSN.2470-1173.2016.14.IPMVA-374","url":null,"abstract":"Traditional image enhancement techniques revise the distribution of pixels or local structure and achieve the impressive performance in image denoising, contrast enhancement and color adjustment. However, they are not effective to improve the overall aesthetic image quality because it may involve contextual modifications, including the removal of disturbing objects, inclusion of appealing visual elements or relocation of the target object. In this paper, we propose a new aesthetic enhancement technique that edits the structural image element guided by a large collection of good exemplars. More specifically, we remove/insert image elements and resize/relocate objects based on good exemplars. Additionally, we remove undesirable regions determined by user interaction and fill these holes seamlessly guided by the exemplars. Based on the experimental evaluation on the database of two landmarks, we observe the considerable improvement in aesthetic quality.","PeriodicalId":262142,"journal":{"name":"Image Processing: Machine Vision Applications","volume":"35 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-02-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122502696","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
Bit Depth Expansion via Estimation of Bit Value Expectation 基于位值期望估计的位深度扩展
Image Processing: Machine Vision Applications Pub Date : 2016-02-14 DOI: 10.2352/ISSN.2470-1173.2016.14.IPMVA-383
Jihwan Woo, Seo-Young Lee, Wonhee Choe
{"title":"Bit Depth Expansion via Estimation of Bit Value Expectation","authors":"Jihwan Woo, Seo-Young Lee, Wonhee Choe","doi":"10.2352/ISSN.2470-1173.2016.14.IPMVA-383","DOIUrl":"https://doi.org/10.2352/ISSN.2470-1173.2016.14.IPMVA-383","url":null,"abstract":"","PeriodicalId":262142,"journal":{"name":"Image Processing: Machine Vision Applications","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-02-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115487396","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: 7
Learning based hole filling method using deep convolutional neural network for view synthesis 基于深度卷积神经网络的基于学习的孔洞填充方法进行视图合成
Image Processing: Machine Vision Applications Pub Date : 2016-02-14 DOI: 10.2352/ISSN.2470-1173.2016.14.IPMVA-376
Heoun-taek Lim, Hak Gu Kim, Yong Man Ro
{"title":"Learning based hole filling method using deep convolutional neural network for view synthesis","authors":"Heoun-taek Lim, Hak Gu Kim, Yong Man Ro","doi":"10.2352/ISSN.2470-1173.2016.14.IPMVA-376","DOIUrl":"https://doi.org/10.2352/ISSN.2470-1173.2016.14.IPMVA-376","url":null,"abstract":"","PeriodicalId":262142,"journal":{"name":"Image Processing: Machine Vision Applications","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-02-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131189157","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
Low-Level Track Finding and Completion using Random Fields 低级跟踪查找和完成使用随机字段
Image Processing: Machine Vision Applications Pub Date : 2016-02-14 DOI: 10.2352/ISSN.2470-1173.2016.14.IPMVA-378
T. Quach, Rebecca Malinas, M. W. Koch
{"title":"Low-Level Track Finding and Completion using Random Fields","authors":"T. Quach, Rebecca Malinas, M. W. Koch","doi":"10.2352/ISSN.2470-1173.2016.14.IPMVA-378","DOIUrl":"https://doi.org/10.2352/ISSN.2470-1173.2016.14.IPMVA-378","url":null,"abstract":"","PeriodicalId":262142,"journal":{"name":"Image Processing: Machine Vision Applications","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-02-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115170020","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
Sudoku Texture Classification 数独纹理分类
Image Processing: Machine Vision Applications Pub Date : 2016-02-14 DOI: 10.2352/ISSN.2470-1173.2016.14.IPMVA-389
G. Finlayson, S. Nixon
{"title":"Sudoku Texture Classification","authors":"G. Finlayson, S. Nixon","doi":"10.2352/ISSN.2470-1173.2016.14.IPMVA-389","DOIUrl":"https://doi.org/10.2352/ISSN.2470-1173.2016.14.IPMVA-389","url":null,"abstract":"","PeriodicalId":262142,"journal":{"name":"Image Processing: Machine Vision Applications","volume":"45 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-02-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129932297","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
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