A illuminance-reflectance model based video stream enhancement algorithm is proposed for improving the visual quality of digital video streams captured by surveillance camera under insufficient and/or nonuniform lighting conditions. The paper presents computational methods for estimation of scene illuminance and reflectance, adaptive dynamic range compression of illuminance, and adaptive enhancement for mid-tone frequency components. The images are processed in a similar way as human eyes sensing a scene. The algorithm demonstrates high quality of enhanced images, robust performance and fast processing speed. Compared with Retinex and multi-scale retinex with color restoration (MSRCR), the proposed method shows a better balance between luminance enhancement and contrast enhancement as well as a more consistent and reliable color rendition without introducing incorrect colors. This is an effective technique for image enhancement with simple computational procedures, which makes real-time enhancement for homeland security application successfully realized. The application of this image enhancement technique to the FRGC images yields improved face recognition results
{"title":"An illuminance-reflectance nonlinear video enhancement model for homeland security applications","authors":"Li Tao, R. Tompkins, V. Asari","doi":"10.1109/AIPR.2005.14","DOIUrl":"https://doi.org/10.1109/AIPR.2005.14","url":null,"abstract":"A illuminance-reflectance model based video stream enhancement algorithm is proposed for improving the visual quality of digital video streams captured by surveillance camera under insufficient and/or nonuniform lighting conditions. The paper presents computational methods for estimation of scene illuminance and reflectance, adaptive dynamic range compression of illuminance, and adaptive enhancement for mid-tone frequency components. The images are processed in a similar way as human eyes sensing a scene. The algorithm demonstrates high quality of enhanced images, robust performance and fast processing speed. Compared with Retinex and multi-scale retinex with color restoration (MSRCR), the proposed method shows a better balance between luminance enhancement and contrast enhancement as well as a more consistent and reliable color rendition without introducing incorrect colors. This is an effective technique for image enhancement with simple computational procedures, which makes real-time enhancement for homeland security application successfully realized. The application of this image enhancement technique to the FRGC images yields improved face recognition results","PeriodicalId":130204,"journal":{"name":"34th Applied Imagery and Pattern Recognition Workshop (AIPR'05)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2005-10-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131199132","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}
In this paper, we analyze the perturbation in the reconstructed optical absorption images, resulting from the discretization of the forward and inverse problems. We show that the perturbation due to each problem is a function of both the forward and inverse problem solutions and can be reduced by proper refinement of the discretization mesh. Based on the perturbation analysis, we devise an adaptive discretization scheme for forward and inverse problems, which reduces the perturbation on the reconstructed image. Such a discretization scheme leads to an adaptively refined composite mesh sufficient to approximate the forward and inverse problem solutions within a desired level of accuracy while keeping the computational complexity within the computational power limits
{"title":"Discretization error based mesh generation for diffuse optical tomography","authors":"M. Guven, B. Yazıcı, Kiwoon Kwon, E. Giladi","doi":"10.1109/AIPR.2005.26","DOIUrl":"https://doi.org/10.1109/AIPR.2005.26","url":null,"abstract":"In this paper, we analyze the perturbation in the reconstructed optical absorption images, resulting from the discretization of the forward and inverse problems. We show that the perturbation due to each problem is a function of both the forward and inverse problem solutions and can be reduced by proper refinement of the discretization mesh. Based on the perturbation analysis, we devise an adaptive discretization scheme for forward and inverse problems, which reduces the perturbation on the reconstructed image. Such a discretization scheme leads to an adaptively refined composite mesh sufficient to approximate the forward and inverse problem solutions within a desired level of accuracy while keeping the computational complexity within the computational power limits","PeriodicalId":130204,"journal":{"name":"34th Applied Imagery and Pattern Recognition Workshop (AIPR'05)","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2005-10-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131308441","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}
Diffuse optical tomography (DOT) poses a typical ill-posed inverse problem with limited number of measurements and inherently low spatial resolution. In this paper, we propose a hierarchical Bayesian approach to improve spatial resolution and quantitative accuracy by using a priori information provided by a secondary high resolution anatomical imaging modality, such as magnetic resonance (MR) or X-ray. The proposed hierarchical Bayesian approach allows incorporation of partial a priori knowledge about the noise and unknown optical image models, thereby capturing the function-anatomy correlation effectively. Numerical simulations demonstrate that the proposed method avoids undesirable bias towards anatomical prior information and leads to significantly improved spatial resolution and quantitative accuracy
{"title":"Hierarchical Bayesian algorithm for diffuse optical tomography","authors":"M. Guven, B. Yazıcı, X. Intes, B. Chance","doi":"10.1109/AIPR.2005.30","DOIUrl":"https://doi.org/10.1109/AIPR.2005.30","url":null,"abstract":"Diffuse optical tomography (DOT) poses a typical ill-posed inverse problem with limited number of measurements and inherently low spatial resolution. In this paper, we propose a hierarchical Bayesian approach to improve spatial resolution and quantitative accuracy by using a priori information provided by a secondary high resolution anatomical imaging modality, such as magnetic resonance (MR) or X-ray. The proposed hierarchical Bayesian approach allows incorporation of partial a priori knowledge about the noise and unknown optical image models, thereby capturing the function-anatomy correlation effectively. Numerical simulations demonstrate that the proposed method avoids undesirable bias towards anatomical prior information and leads to significantly improved spatial resolution and quantitative accuracy","PeriodicalId":130204,"journal":{"name":"34th Applied Imagery and Pattern Recognition Workshop (AIPR'05)","volume":"87 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2005-10-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126022364","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}
There is an immediate requirement for law enforcement and homeland security to identify concealed weapons which may present a threat to official personnel and the general public. This involves suicide bomb vests, handguns, knife blades, and other threatening weapons. The weapons may be composed of a large range of materials such as metals, nonmetals, plastics and explosives. The Homeland Security Advanced Research Projects Agency (HSARPA) and the National Institute of Justice (NIJ) are presently funding programs collectively covering all relevant portions of the electromagnetic spectrum and ultrasound in order to detect these weapons through the various materials that may be used to conceal these weapons This paper outlines the various imaging techniques being investigated and present results where available
{"title":"An overview of concealed weapons detection for homeland security","authors":"Peter J. Costianes","doi":"10.1109/AIPR.2005.17","DOIUrl":"https://doi.org/10.1109/AIPR.2005.17","url":null,"abstract":"There is an immediate requirement for law enforcement and homeland security to identify concealed weapons which may present a threat to official personnel and the general public. This involves suicide bomb vests, handguns, knife blades, and other threatening weapons. The weapons may be composed of a large range of materials such as metals, nonmetals, plastics and explosives. The Homeland Security Advanced Research Projects Agency (HSARPA) and the National Institute of Justice (NIJ) are presently funding programs collectively covering all relevant portions of the electromagnetic spectrum and ultrasound in order to detect these weapons through the various materials that may be used to conceal these weapons This paper outlines the various imaging techniques being investigated and present results where available","PeriodicalId":130204,"journal":{"name":"34th Applied Imagery and Pattern Recognition Workshop (AIPR'05)","volume":"87 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2005-10-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123301488","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}
E. Uzgiris, Deborah Lee, A. Sood, Kathleen Bove, Stephen J. Lomnes
The multimodal polymeric contrast agents for MRI and fluorescence imaging in the management of cancer are discussed. The paper presents preliminary data that suggest that a simple experimental protocol can provide at least an index of the permeability parameter if not the absolute permeability itself.
{"title":"Multimodal polymeric contrast agents for MRI and fluorescence imaging in the management of cancer","authors":"E. Uzgiris, Deborah Lee, A. Sood, Kathleen Bove, Stephen J. Lomnes","doi":"10.1109/AIPR.2005.36","DOIUrl":"https://doi.org/10.1109/AIPR.2005.36","url":null,"abstract":"The multimodal polymeric contrast agents for MRI and fluorescence imaging in the management of cancer are discussed. The paper presents preliminary data that suggest that a simple experimental protocol can provide at least an index of the permeability parameter if not the absolute permeability itself.","PeriodicalId":130204,"journal":{"name":"34th Applied Imagery and Pattern Recognition Workshop (AIPR'05)","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2005-10-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125348849","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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