Pub Date : 2000-06-13DOI: 10.1109/CVBVS.2000.855250
B. Bhanu, G. Jones
This paper outlines an approach and experimental results for synthetic aperture radar (SAR) object recognition using the MSTAR data. With SAR scattering center locations and magnitudes as features, the invariance of these features is shown with object articulation (e.g., rotation of a tank turret) and with external configuration variants. This scatterer location and magnitude quasi-invariance is used as a basis for development of a SAR recognition system that successfully identifies articulated and non-standard configuration vehicles based on non-articulated, standard recognition models. The forced recognition results and pose accuracy are given. The effect of different confusers on the receiver operating characteristic (ROC) curves are illustrated along with ROC curves for configuration variants, articulations and small changes in depression angle. Results are given that show that integrating the results of multiple recognizers can lead to significantly improved performance over the single best recognizer.
{"title":"Object recognition results using MSTAR synthetic aperture radar data","authors":"B. Bhanu, G. Jones","doi":"10.1109/CVBVS.2000.855250","DOIUrl":"https://doi.org/10.1109/CVBVS.2000.855250","url":null,"abstract":"This paper outlines an approach and experimental results for synthetic aperture radar (SAR) object recognition using the MSTAR data. With SAR scattering center locations and magnitudes as features, the invariance of these features is shown with object articulation (e.g., rotation of a tank turret) and with external configuration variants. This scatterer location and magnitude quasi-invariance is used as a basis for development of a SAR recognition system that successfully identifies articulated and non-standard configuration vehicles based on non-articulated, standard recognition models. The forced recognition results and pose accuracy are given. The effect of different confusers on the receiver operating characteristic (ROC) curves are illustrated along with ROC curves for configuration variants, articulations and small changes in depression angle. Results are given that show that integrating the results of multiple recognizers can lead to significantly improved performance over the single best recognizer.","PeriodicalId":231063,"journal":{"name":"Proceedings IEEE Workshop on Computer Vision Beyond the Visible Spectrum: Methods and Applications (Cat. No.PR00640)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2000-06-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125789764","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 : 1900-01-01DOI: 10.1109/CVBVS.2000.855249
E. Yfantis, A. Popovich, A. Angelopoulos, G. Bebis
An algorithm of cancer recognition in ultrasound images is developed in this paper. In order for cancer to survive it develops its own blood supply system, which is different than the supply system of normal tissue. The velocity of the blood flowing through the cancerous blood vessels is different than the velocity of the blood flowing through blood vessels of normal tissue. Due to this fact the ultrasound signal is absorbed differently in the cancerous areas than in the normal tissue areas. The energy of the signal, the continuity of the signal, the autocorrelation function and frequency domain properties are different in the normal tissue than in cancerous tissue. All of these indicators are weighted here for the purpose of classifying the image of the tissue as being cancerous or non-cancerous. Preliminary results based on limited number of ultrasound images show that our method has the ability to recognize cancer in ultrasound images.
{"title":"On cancer recognition of ultrasound images","authors":"E. Yfantis, A. Popovich, A. Angelopoulos, G. Bebis","doi":"10.1109/CVBVS.2000.855249","DOIUrl":"https://doi.org/10.1109/CVBVS.2000.855249","url":null,"abstract":"An algorithm of cancer recognition in ultrasound images is developed in this paper. In order for cancer to survive it develops its own blood supply system, which is different than the supply system of normal tissue. The velocity of the blood flowing through the cancerous blood vessels is different than the velocity of the blood flowing through blood vessels of normal tissue. Due to this fact the ultrasound signal is absorbed differently in the cancerous areas than in the normal tissue areas. The energy of the signal, the continuity of the signal, the autocorrelation function and frequency domain properties are different in the normal tissue than in cancerous tissue. All of these indicators are weighted here for the purpose of classifying the image of the tissue as being cancerous or non-cancerous. Preliminary results based on limited number of ultrasound images show that our method has the ability to recognize cancer in ultrasound images.","PeriodicalId":231063,"journal":{"name":"Proceedings IEEE Workshop on Computer Vision Beyond the Visible Spectrum: Methods and Applications (Cat. No.PR00640)","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121333384","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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