发布求助
文献互助 智能选刊 最新文献

2015 3rd IAPR Asian Conference on Pattern Recognition (ACPR)最新文献

筛选
英文 中文
Improved shape code based word matching for multi-script documents 改进的基于形状代码的多脚本文档单词匹配
2015 3rd IAPR Asian Conference on Pattern Recognition (ACPR) Pub Date : 2015-11-03 DOI: 10.1109/ACPR.2015.7486490
T. Mondal, Arundhati Tarafdar, N. Ragot, Jean-Yves Ramel, U. Pal
{"title":"Improved shape code based word matching for multi-script documents","authors":"T. Mondal, Arundhati Tarafdar, N. Ragot, Jean-Yves Ramel, U. Pal","doi":"10.1109/ACPR.2015.7486490","DOIUrl":"https://doi.org/10.1109/ACPR.2015.7486490","url":null,"abstract":"In this paper, we propose a shape code based wordimage matching (word-spotting) technique for word retrieval in multilingual documents, written in Indian languages. Each query word image to be searched is represented by a sequence of shape codes that corresponds to primitives. Then an inexact string matching technique is applied for measuring the similarity between the codes generated from the query word image and each candidate word images, obtained from the document. Based on the similarity score, we retrieve the document where the query image is found. Experimental results on Bangla, Devanagari scripts document image databases confirms the feasibility and efficiency of our proposed approach.","PeriodicalId":240902,"journal":{"name":"2015 3rd IAPR Asian Conference on Pattern Recognition (ACPR)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-11-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128828650","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
Explicit foreground and background modeling in the classification of text blocks in scene images 场景图像文本块分类中明确的前景和背景建模
2015 3rd IAPR Asian Conference on Pattern Recognition (ACPR) Pub Date : 2015-11-03 DOI: 10.1109/ACPR.2015.7486604
B. Sriman, Lambert Schomaker
{"title":"Explicit foreground and background modeling in the classification of text blocks in scene images","authors":"B. Sriman, Lambert Schomaker","doi":"10.1109/ACPR.2015.7486604","DOIUrl":"https://doi.org/10.1109/ACPR.2015.7486604","url":null,"abstract":"Achieving high accuracy for classifying foreground and background is an interesting challenge in the field of scene image analysis because of the wide range of illumination, complex background, and scale changes. Classifying foreground and background using bag-of-feature model gives a good result. However, the performance of the classifier depends on designed features. Therefore, this paper presents an alternative classification method based on three categories of object-attributes features namely object description, color distribution and gradient strength. Each feature is computed to a classifier model. The robustness of the method has been tested on the ICDAR2015 dataset. The experimental results show that the performance of the proposed method performs competitively against the results of existing methods in term of precision and recall.","PeriodicalId":240902,"journal":{"name":"2015 3rd IAPR Asian Conference on Pattern Recognition (ACPR)","volume":"57 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-11-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117019184","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
Supervised spectral subspace clustering for visual dictionary creation in the context of image classification 基于监督谱子空间聚类的图像分类视觉字典创建
2015 3rd IAPR Asian Conference on Pattern Recognition (ACPR) Pub Date : 2015-11-03 DOI: 10.1109/ACPR.2015.7486525
Imtiaz Masud Ziko, É. Fromont, Damien Muselet, M. Sebban
{"title":"Supervised spectral subspace clustering for visual dictionary creation in the context of image classification","authors":"Imtiaz Masud Ziko, É. Fromont, Damien Muselet, M. Sebban","doi":"10.1109/ACPR.2015.7486525","DOIUrl":"https://doi.org/10.1109/ACPR.2015.7486525","url":null,"abstract":"When building traditional Bag of Visual Words (BOW) for image classification, the K-means algorithm is usually used on a large set of high dimensional local descriptors to build the visual dictionary. However, it is very likely that, to find a good visual vocabulary, only a sub-part of the descriptor space of each visual word is truly relevant. We propose a novel framework for creating the visual dictionary based on a spectral subspace clustering method instead of the traditional K-means algorithm. A strategy for adding supervised information during the subspace clustering process is formulated to obtain more discriminative visual words. Experimental results on real world image dataset show that the proposed framework for dictionary creation improves the classification accuracy compared to using traditionally built BOW.","PeriodicalId":240902,"journal":{"name":"2015 3rd IAPR Asian Conference on Pattern Recognition (ACPR)","volume":"196 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-11-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132517849","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
Sketch-based image retrieval using sketch tokens 使用草图标记的基于草图的图像检索
2015 3rd IAPR Asian Conference on Pattern Recognition (ACPR) Pub Date : 2015-11-01 DOI: 10.1109/ACPR.2015.7486533
Shu Wang, Z. Miao
{"title":"Sketch-based image retrieval using sketch tokens","authors":"Shu Wang, Z. Miao","doi":"10.1109/ACPR.2015.7486533","DOIUrl":"https://doi.org/10.1109/ACPR.2015.7486533","url":null,"abstract":"One fundamental challenge of Sketch-based Image Retrieval (SBIR) is the appearance gap between sketches and natural images. To bridge the gap, we propose a framework that describes both types of images based on sketch tokens. Sketch tokens are mid-level representations of local edge structures. Compared with describing images with pixel-level features, describing images with sketch tokens is more accurate and robust. We compute the responses of image patches to sketch tokens, and propose a local descriptor to describe object shape by capturing the sketch token responses. Bag-of-visual-word mode is utilized to represent images, and inverse indexing is built to accelerate the retrieval process. We compared the proposed work with state-of-the-art methods (SHoG, GF-HOG) on two public datasets. The experimental results show that our method outperforms them and significantly improves SBIR performance.","PeriodicalId":240902,"journal":{"name":"2015 3rd IAPR Asian Conference on Pattern Recognition (ACPR)","volume":"87 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123113297","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
A real-time LIDAR and vision based pedestrian detection system for unmanned ground vehicles 一种用于无人地面车辆的实时激光雷达和基于视觉的行人检测系统
2015 3rd IAPR Asian Conference on Pattern Recognition (ACPR) Pub Date : 2015-11-01 DOI: 10.1109/ACPR.2015.7486580
Xiaofeng Han, Jianfeng Lu, Ying Tai, Chunxia Zhao
{"title":"A real-time LIDAR and vision based pedestrian detection system for unmanned ground vehicles","authors":"Xiaofeng Han, Jianfeng Lu, Ying Tai, Chunxia Zhao","doi":"10.1109/ACPR.2015.7486580","DOIUrl":"https://doi.org/10.1109/ACPR.2015.7486580","url":null,"abstract":"In this work, we present a real-time pedestrian detection system using LIDAR and Vision in-vehicle. We get regions of interest by clustering lidar point clouds and project them onto the images. After that we use black mask to replace those image areas which has no lidar points projected onto. Then we extract HOG and lidar point clouds features and use those features to detect pedestrians by a linear SVM classifier. The main contributions are that we proposed a method that can select ROIs on image automatically and then enhanced the HOG descriptor with the lidar points' projections. Finally we fuse HOG and lidar based features to train a linear SVM to detect pedestrian. The above method we proposed can satisfy real-time requirement. We apply our pedestrian detection system to our own dataset and KITTI dataset, and show that we outperform the primitive HOG based methods.","PeriodicalId":240902,"journal":{"name":"2015 3rd IAPR Asian Conference on Pattern Recognition (ACPR)","volume":"15 3‐6","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120836477","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: 11
Structure-driven facade parsing with irregular patterns 使用不规则模式的结构驱动facade解析
2015 3rd IAPR Asian Conference on Pattern Recognition (ACPR) Pub Date : 2015-11-01 DOI: 10.1109/ACPR.2015.7486462
Jinglu Wang, Chun Liu, Tianwei Shen, Long Quan
{"title":"Structure-driven facade parsing with irregular patterns","authors":"Jinglu Wang, Chun Liu, Tianwei Shen, Long Quan","doi":"10.1109/ACPR.2015.7486462","DOIUrl":"https://doi.org/10.1109/ACPR.2015.7486462","url":null,"abstract":"We propose a novel method for recognizing irregular patterns in facades. An irregular pattern is an incomplete 2D grid, representing the placements of repetitive structural architectural objects (e.g., windows), which is capable of being generalized to a variety of facade structures. To effectively recognize such a pattern, we jointly model objects and object structures in a unified Marked Point Process framework, where the architectural objects are abstracted as sparsely populated geometric entities and the pairwise spatially interactions are modeled as elliptical repulsion fields. To optimize the proposed model, we introduce a structure-driven Monte Carlo Markov Chain (MCMC) sampler, by which the irregular pattern hypotheses are iteratively constructed in a bottom-up manner and verified in a top-down manner. The solution space is explored more efficiently for fast convergence. Extensive experiments have shown the efficiency and accuracy of our method of parsing a large category of facades.","PeriodicalId":240902,"journal":{"name":"2015 3rd IAPR Asian Conference on Pattern Recognition (ACPR)","volume":"0 163 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121032837","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: 5
Appearance-based multiple fish tracking for collective motion analysis 基于外观的多鱼集体运动跟踪分析
2015 3rd IAPR Asian Conference on Pattern Recognition (ACPR) Pub Date : 2015-11-01 DOI: 10.1109/ACPR.2015.7486526
Kei Terayama, Koki Hongo, H. Habe, M. Sakagami
{"title":"Appearance-based multiple fish tracking for collective motion analysis","authors":"Kei Terayama, Koki Hongo, H. Habe, M. Sakagami","doi":"10.1109/ACPR.2015.7486526","DOIUrl":"https://doi.org/10.1109/ACPR.2015.7486526","url":null,"abstract":"We propose a visual tracking method for dense fish schools in which occlusions occur frequently. Although much progress has been made for tracking multiple objects in video images, it is challenging to track individuals in highly dense groups. For occluded fishes, estimation of their positions and directions is difficult. However, if we know the number of fishes in a local area, we can accurately estimate their states by matching all of the combinations of possible parameters on the basis of our appearance model. We apply the idea to track multiple fishes in a school. Experimental results show that multiple fishes are practically tracked with our method compared to a well-known tracking method, and the average difference is less than 4%b of the mean body length of the school.","PeriodicalId":240902,"journal":{"name":"2015 3rd IAPR Asian Conference on Pattern Recognition (ACPR)","volume":"60 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121115105","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: 9
Lung segmentation with improved graph cuts on chest CT images 基于改进图切割的胸部CT图像肺分割
2015 3rd IAPR Asian Conference on Pattern Recognition (ACPR) Pub Date : 2015-11-01 DOI: 10.1109/ACPR.2015.7486502
Shuangfeng Dai, K. Lu, Jiyang Dong
{"title":"Lung segmentation with improved graph cuts on chest CT images","authors":"Shuangfeng Dai, K. Lu, Jiyang Dong","doi":"10.1109/ACPR.2015.7486502","DOIUrl":"https://doi.org/10.1109/ACPR.2015.7486502","url":null,"abstract":"Lung segmentation is often performed as a preprocessing step on chest Computed Tomography (CT) images because it is important for identifying lung diseases in clinical evaluation. Hence, researches on lung segmentation have received much attention. In this paper, we propose a new lung segmentation method based on an improved graph cuts algorithm from the energy function. First, the lung CT images is modeled with Gaussian mixture models (GMMs), and the optimized distribution parameters can be obtained with expectation maximization (EM) algorithm. With that parameters, we can construct the improved regional penalty item in the graph cuts energy function. Second, considering the image edge information, the Sobel operator is adopted to detect and extract the lung image edges, and the lung image edges information is used to improve the boundary penalty item of graph cuts energy function. Finally, the improved energy function of graph cuts algorithm is obtained, then the corresponding graph is created, and lung is segmented with the minimum cut theory. The experiments demonstrate that the proposed method is very accurate and efficient for lung segmentation.","PeriodicalId":240902,"journal":{"name":"2015 3rd IAPR Asian Conference on Pattern Recognition (ACPR)","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124915666","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
Multi-cut light field depth estimation 多切口光场深度估计
2015 3rd IAPR Asian Conference on Pattern Recognition (ACPR) Pub Date : 2015-11-01 DOI: 10.1109/ACPR.2015.7486518
Thoma Papadhimitri, O. Urfalioglu
{"title":"Multi-cut light field depth estimation","authors":"Thoma Papadhimitri, O. Urfalioglu","doi":"10.1109/ACPR.2015.7486518","DOIUrl":"https://doi.org/10.1109/ACPR.2015.7486518","url":null,"abstract":"We investigate the problem of depth map estimation of a scene from 4D light-field data. Unlike prior work, we process all the input images (or sub-images) of the light-field. Indeed, for each point of the scene, different depth candidates are estimated by considering all possible 3D light-field cuts instead of only 2, i.e. the horizontal and vertical one. Then, the optimal candidates are chosen by finding the labeling with minimum energy. The main motivation of our approach is that by processing multiple cuts of the light-field, the matching ambiguities are reduced. Our meta-method is of broad interest as it can be applied and enhance the performance of various state-of-the-art light-field depth estimation techniques. For the sake of demonstration we apply it on the work from Wanner and Goldluecke [19] and significantly improve their results.","PeriodicalId":240902,"journal":{"name":"2015 3rd IAPR Asian Conference on Pattern Recognition (ACPR)","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125836868","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
On coupled regularization for non-convex variational image enhancement 非凸变分图像增强的耦合正则化研究
2015 3rd IAPR Asian Conference on Pattern Recognition (ACPR) Pub Date : 2015-11-01 DOI: 10.1109/ACPR.2015.7486610
Freddie Åström, C. Schnörr
{"title":"On coupled regularization for non-convex variational image enhancement","authors":"Freddie Åström, C. Schnörr","doi":"10.1109/ACPR.2015.7486610","DOIUrl":"https://doi.org/10.1109/ACPR.2015.7486610","url":null,"abstract":"A natural continuation from conventional convex methods for image enhancement is the transition to non-convex formulations. However, strictly non-convex models do not admit traditional tools from convex optimization to be used. To resolve this drawback, non-convex problems are often cast into convex formulations by relaxing stringent assumptions on model properties. In this work we present an alternative approach. We study when an energy functional is convex given a non-convex penalty term. Key to our formulation is the introduction of a novel coupling between the discretization scheme and a non-local weight function in the data term. We interpret the non-local weights for the finite difference operators. In a denoising application we study a class of non-convex ℓp-norms. The resulting energies are globally minimized using the popular ADMM.","PeriodicalId":240902,"journal":{"name":"2015 3rd IAPR Asian Conference on Pattern Recognition (ACPR)","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114184795","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
下一页 尾页
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整，为了账户安全，请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
请完成安全验证×
相关产品
×
本文献相关产品
联系我们：info@booksci.cn Book学术提供免费学术资源搜索服务，方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号：604180095
Book学术官方微信