2010 2nd International Conference on Image Processing Theory, Tools and Applications最新文献_第10页

Pedestrian detection based on adaboost algorithm with a pseudo-calibrated camera 基于adaboost算法的伪标定相机行人检测

2010 2nd International Conference on Image Processing Theory, Tools and Applications Pub Date : 2010-07-07 DOI: 10.1109/IPTA.2010.5586744

Damien Simonnet, S. Velastín

引用次数: 3

Atlas-based segmentation of brain MR images using least square support vector machines 基于最小二乘支持向量机的脑磁共振图像分割

2010 2nd International Conference on Image Processing Theory, Tools and Applications Pub Date : 2010-07-07 DOI: 10.1109/IPTA.2010.5586779

K. Kasiri, K. Kazemi, M. Dehghani, M. Helfroush

引用次数: 21

Image modeling using statistical measures for visual object categorization 使用统计方法进行视觉对象分类的图像建模

2010 2nd International Conference on Image Processing Theory, Tools and Applications Pub Date : 2010-07-01 DOI: 10.1109/IPTA.2010.5586750

Huanzhang Fu, A. Pujol, E. Dellandréa, Liming Chen

{"title":"Image modeling using statistical measures for visual object categorization","authors":"Huanzhang Fu, A. Pujol, E. Dellandréa, Liming Chen","doi":"10.1109/IPTA.2010.5586750","DOIUrl":"https://doi.org/10.1109/IPTA.2010.5586750","url":null,"abstract":"Since the challenging visual object categorization has attracted more and more attention in recent years, we present in this paper a novel approach called statistical measures based image modeling for this problem, thus avoiding the major difficulty of the popular “bag-of-visual words” approach which needs to fix a visual vocabulary size. We use a series of statistical measures over our proper region based color and segment features as well as the popular SIFT, extracted from an image, to model its visual content. Then this new image modeling will be fed to a certain classifier to accomplish the object categorization task. Several classification schemes combined with some feature selection techniques and fusion strategies have also been implemented and compared within the experimentation carried out on a subset of Pascal VOC dataset. The results show that merging the region based features and SIFT which are from different sources using an early fusion can actually improve classification performance, suggesting that these features managed to extract information which is complementary to each other.","PeriodicalId":236574,"journal":{"name":"2010 2nd International Conference on Image Processing Theory, Tools and Applications","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121662491","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

Query-by-sketch based image retrieval using diffusion tensor fields 基于草图查询的基于扩散张量场的图像检索

2010 2nd International Conference on Image Processing Theory, Tools and Applications Pub Date : 2010-07-01 DOI: 10.1109/IPTA.2010.5586773

S. Yoon, Arjan Kuijper

{"title":"Query-by-sketch based image retrieval using diffusion tensor fields","authors":"S. Yoon, Arjan Kuijper","doi":"10.1109/IPTA.2010.5586773","DOIUrl":"https://doi.org/10.1109/IPTA.2010.5586773","url":null,"abstract":"A user-drawn sketch is one of the most intuitive forms of Human Computer Interaction. Users can express their intention by sketching the specific characteristics of a target object as a rough and simple black and white hand-drawn draft image. Recent advances of tablet PC and multi-touch screen technology raised increasing interest on how users might search and retrieve the desired images in databases from a simple sketched image. In this paper, we present a new approach for content based image retrieval from a query by sketchy draft images which are not in the database. Our innovation to sketch based image retrieval systems consists of three steps: (i) Image database configuration using size normalization, edge detection, and hierarchical image classification, (ii) Tensorial feature extraction of query and image data in the topology of second-order symmetric diffusion tensor fields, and (iii) Similarity measure using eigen-features between sketched query and databases to retrieve the most similar target object. Experiments are conducted to evaluate the performance of our methodology showing an efficient and mature image retrieval system.","PeriodicalId":236574,"journal":{"name":"2010 2nd International Conference on Image Processing Theory, Tools and Applications","volume":"120 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128028114","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