Pub Date : 2010-07-07DOI: 10.1109/IPTA.2010.5586744
Damien Simonnet, S. Velastín
This paper presents a new algorithm for pedestrian detection for a fixed camera using the cluster boosted tree (CBT) structure of Wu and Nevatia for building a multi-view tree classifier based on edgelet features. The main advantage of this structure is that it is less sensitive to camera view changes compared to the cascade structure of Viola and Jones. The approach presented in this paper uses geometrical information in the image to estimate pedestrian size for a given pixel position. This we call pseudo camera calibration. Thereby, we combine the CBT classifier trained on the INRIA datasets and the pedestrian size estimator to detect pedestrians. The performance of this algorithm is also evaluated on images captured at a real metro station for several camera positions.
{"title":"Pedestrian detection based on adaboost algorithm with a pseudo-calibrated camera","authors":"Damien Simonnet, S. Velastín","doi":"10.1109/IPTA.2010.5586744","DOIUrl":"https://doi.org/10.1109/IPTA.2010.5586744","url":null,"abstract":"This paper presents a new algorithm for pedestrian detection for a fixed camera using the cluster boosted tree (CBT) structure of Wu and Nevatia for building a multi-view tree classifier based on edgelet features. The main advantage of this structure is that it is less sensitive to camera view changes compared to the cascade structure of Viola and Jones. The approach presented in this paper uses geometrical information in the image to estimate pedestrian size for a given pixel position. This we call pseudo camera calibration. Thereby, we combine the CBT classifier trained on the INRIA datasets and the pedestrian size estimator to detect pedestrians. The performance of this algorithm is also evaluated on images captured at a real metro station for several camera positions.","PeriodicalId":236574,"journal":{"name":"2010 2nd International Conference on Image Processing Theory, Tools and Applications","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-07-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126860428","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}
Pub Date : 2010-07-07DOI: 10.1109/IPTA.2010.5586779
K. Kasiri, K. Kazemi, M. Dehghani, M. Helfroush
This study presents an automatic model based technique for brain tissue segmentation from cerebral magnetic resonance (MR) images. In this paper, support vector machine (SVM) based classifier, as a new and powerful kind of supervised machine learning with high generalization characteristics, is employed. Here, least-square SVM (LS-SVM) in conjunction with brain probabilistic atlas as a priori information is applied to obtain class probabilities for three tissues of cerebrospinal fluid (CSF), white matter (WM) and grey matter (GM). The entire process of brain segmentation is performed in an iterative procedure, so that the probabilistic maps of brain tissues will be updated at any iteration. The quantitative and qualitative results indicate excellent performance of the applied method.
{"title":"Atlas-based segmentation of brain MR images using least square support vector machines","authors":"K. Kasiri, K. Kazemi, M. Dehghani, M. Helfroush","doi":"10.1109/IPTA.2010.5586779","DOIUrl":"https://doi.org/10.1109/IPTA.2010.5586779","url":null,"abstract":"This study presents an automatic model based technique for brain tissue segmentation from cerebral magnetic resonance (MR) images. In this paper, support vector machine (SVM) based classifier, as a new and powerful kind of supervised machine learning with high generalization characteristics, is employed. Here, least-square SVM (LS-SVM) in conjunction with brain probabilistic atlas as a priori information is applied to obtain class probabilities for three tissues of cerebrospinal fluid (CSF), white matter (WM) and grey matter (GM). The entire process of brain segmentation is performed in an iterative procedure, so that the probabilistic maps of brain tissues will be updated at any iteration. The quantitative and qualitative results indicate excellent performance of the applied method.","PeriodicalId":236574,"journal":{"name":"2010 2nd International Conference on Image Processing Theory, Tools and Applications","volume":"66 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-07-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127753220","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}
Pub Date : 2010-07-01DOI: 10.1109/IPTA.2010.5586750
Huanzhang Fu, A. Pujol, E. Dellandréa, Liming Chen
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.
{"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}
Pub Date : 2010-07-01DOI: 10.1109/IPTA.2010.5586773
S. Yoon, Arjan Kuijper
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.
{"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}
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