Pub Date : 2016-09-01DOI: 10.1109/ICIP.2016.7532808
Wei-Feng Ou, Chun-Ling Yang, Wen-Hao Li, Li-Hong Ma
Existing multi-hypothesis (MH) prediction algorithms in compressed video sensing (CVS) are all deployed in measurement domain, which restricts the flexibility of block partitioning in the reconstruction process and decreases the reconstruction accuracy. To address this issue, this paper proposes a two-stage multi-hypothesis reconstruction (2sMHR) scheme which deploys the MH prediction in measurement domain and pixel domain successively. Two implementation schemes, GOP-wise and frame-wise scheme, are developed for the 2sMHR. Furthermore, a new weighted metric combining the Euclidean distance and correlation coefficient is designed for the Tikhonov-regularized MH prediction model. Simulation results show that the proposed two-stage MH reconstruction scheme obtains higher reconstruction accuracy than the state-of-the-art CVS prediction methods.
{"title":"A two-stage multi-hypothesis reconstruction scheme in compressed video sensing","authors":"Wei-Feng Ou, Chun-Ling Yang, Wen-Hao Li, Li-Hong Ma","doi":"10.1109/ICIP.2016.7532808","DOIUrl":"https://doi.org/10.1109/ICIP.2016.7532808","url":null,"abstract":"Existing multi-hypothesis (MH) prediction algorithms in compressed video sensing (CVS) are all deployed in measurement domain, which restricts the flexibility of block partitioning in the reconstruction process and decreases the reconstruction accuracy. To address this issue, this paper proposes a two-stage multi-hypothesis reconstruction (2sMHR) scheme which deploys the MH prediction in measurement domain and pixel domain successively. Two implementation schemes, GOP-wise and frame-wise scheme, are developed for the 2sMHR. Furthermore, a new weighted metric combining the Euclidean distance and correlation coefficient is designed for the Tikhonov-regularized MH prediction model. Simulation results show that the proposed two-stage MH reconstruction scheme obtains higher reconstruction accuracy than the state-of-the-art CVS prediction methods.","PeriodicalId":6521,"journal":{"name":"2016 IEEE International Conference on Image Processing (ICIP)","volume":"28 1","pages":"2494-2498"},"PeriodicalIF":0.0,"publicationDate":"2016-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83625969","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 : 2016-09-01DOI: 10.1109/ICIP.2016.7532945
M. U. Sadiq, J. Simmons, C. Bouman
Computed tomography is increasingly enabling scientists to study physical processes of materials at micron scales. The MBIR framework provides a powerful method for CT reconstruction by incorporating both a measurement model and prior model. Classically, the choice of prior has been limited to models enforcing local similarity in the image data. In some material science problems, however, much more may be known about the underlying physical process being imaged. Moreover, recent work in Plug-And-Play decoupling of the MBIR problem has enabled researchers to look beyond classical prior models, and innovations in methods of data acquisition such as interlaced view sampling have also shown promise for imaging of dynamic physical processes. In this paper, we propose an MBIR framework with a physics based prior model - namely the Cahn-Hilliard equation. The Cahn-Hilliard equation can be used to describe the spatiotemporal evolution of binary alloys. After formulating the MBIR cost with Cahn-Hilliard prior, we use Plug-And-Play algorithm with ICD optimization to minimize this cost. We apply this method to simulated data using the interlaced-view sampling method of data acquisition. Results show superior reconstruction quality compared to the Filtered Back Projection. Though we use Cahn-Hilliard equation as one instance, the method can be easily extended to use any other physics-based prior model for a different set of applications.
{"title":"Model based image reconstruction with physics based priors","authors":"M. U. Sadiq, J. Simmons, C. Bouman","doi":"10.1109/ICIP.2016.7532945","DOIUrl":"https://doi.org/10.1109/ICIP.2016.7532945","url":null,"abstract":"Computed tomography is increasingly enabling scientists to study physical processes of materials at micron scales. The MBIR framework provides a powerful method for CT reconstruction by incorporating both a measurement model and prior model. Classically, the choice of prior has been limited to models enforcing local similarity in the image data. In some material science problems, however, much more may be known about the underlying physical process being imaged. Moreover, recent work in Plug-And-Play decoupling of the MBIR problem has enabled researchers to look beyond classical prior models, and innovations in methods of data acquisition such as interlaced view sampling have also shown promise for imaging of dynamic physical processes. In this paper, we propose an MBIR framework with a physics based prior model - namely the Cahn-Hilliard equation. The Cahn-Hilliard equation can be used to describe the spatiotemporal evolution of binary alloys. After formulating the MBIR cost with Cahn-Hilliard prior, we use Plug-And-Play algorithm with ICD optimization to minimize this cost. We apply this method to simulated data using the interlaced-view sampling method of data acquisition. Results show superior reconstruction quality compared to the Filtered Back Projection. Though we use Cahn-Hilliard equation as one instance, the method can be easily extended to use any other physics-based prior model for a different set of applications.","PeriodicalId":6521,"journal":{"name":"2016 IEEE International Conference on Image Processing (ICIP)","volume":"153 1","pages":"3176-3179"},"PeriodicalIF":0.0,"publicationDate":"2016-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86155506","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 : 2016-09-01DOI: 10.1109/ICIP.2016.7532678
Xue Li, J. Zhou, Lei Tong, Xun Yu, Jianhui Guo, Chunxia Zhao
Hyperspectral unmixing is an important technique for identifying the constituent spectra and estimating their corresponding fractions in an image. Nonnegative Matrix Factorization (NMF) has recently been widely used for hyperspectral unmixing. However, due to the complex distribution of hyperspectral data, most existing NMF algorithms cannot adequately reflect the intrinsic relationship of the data. In this paper, we propose a novel method, Structured Discriminative Nonnegative Matrix Factorization (SDNMF), to preserve the structural information of hyperspectral data. This is achieved by introducing structured discriminative regularization terms to model both local affinity and distant repulsion of observed spectral responses. Moreover, considering that the abundances of most materials are sparse, a sparseness constraint is also introduced into SDNMF. Experimental results on both synthetic and real data have validated the effectiveness of the proposed method which achieves better unmixing performance than several alternative approaches.
{"title":"Structured Discriminative Nonnegative Matrix Factorization for hyperspectral unmixing","authors":"Xue Li, J. Zhou, Lei Tong, Xun Yu, Jianhui Guo, Chunxia Zhao","doi":"10.1109/ICIP.2016.7532678","DOIUrl":"https://doi.org/10.1109/ICIP.2016.7532678","url":null,"abstract":"Hyperspectral unmixing is an important technique for identifying the constituent spectra and estimating their corresponding fractions in an image. Nonnegative Matrix Factorization (NMF) has recently been widely used for hyperspectral unmixing. However, due to the complex distribution of hyperspectral data, most existing NMF algorithms cannot adequately reflect the intrinsic relationship of the data. In this paper, we propose a novel method, Structured Discriminative Nonnegative Matrix Factorization (SDNMF), to preserve the structural information of hyperspectral data. This is achieved by introducing structured discriminative regularization terms to model both local affinity and distant repulsion of observed spectral responses. Moreover, considering that the abundances of most materials are sparse, a sparseness constraint is also introduced into SDNMF. Experimental results on both synthetic and real data have validated the effectiveness of the proposed method which achieves better unmixing performance than several alternative approaches.","PeriodicalId":6521,"journal":{"name":"2016 IEEE International Conference on Image Processing (ICIP)","volume":"113 1","pages":"1848-1852"},"PeriodicalIF":0.0,"publicationDate":"2016-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86225580","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 : 2016-09-01DOI: 10.1109/ICIP.2016.7533037
Ilker Buzcu, Aydin Alatan
An enhancement to one of the existing visual object detection approaches is proposed for generating candidate windows that improves detection accuracy at no additional computational cost. Hypothesis windows for object detection are obtained based on Fisher Vector representations over initially obtained superpixels. In order to obtain new window hypotheses, hierarchical merging of superpixel regions are applied, depending upon improvements on some objectiveness measures with no additional cost due to additivity of Fisher Vectors. The proposed technique is further improved by concatenating these representations with that of deep networks. Based on the results of the simulations on typical data sets, it can be argued that the approach is quite promising for its use of handcrafted features left to dust due to the rise of deep learning.
{"title":"Fisher-selective search for object detection","authors":"Ilker Buzcu, Aydin Alatan","doi":"10.1109/ICIP.2016.7533037","DOIUrl":"https://doi.org/10.1109/ICIP.2016.7533037","url":null,"abstract":"An enhancement to one of the existing visual object detection approaches is proposed for generating candidate windows that improves detection accuracy at no additional computational cost. Hypothesis windows for object detection are obtained based on Fisher Vector representations over initially obtained superpixels. In order to obtain new window hypotheses, hierarchical merging of superpixel regions are applied, depending upon improvements on some objectiveness measures with no additional cost due to additivity of Fisher Vectors. The proposed technique is further improved by concatenating these representations with that of deep networks. Based on the results of the simulations on typical data sets, it can be argued that the approach is quite promising for its use of handcrafted features left to dust due to the rise of deep learning.","PeriodicalId":6521,"journal":{"name":"2016 IEEE International Conference on Image Processing (ICIP)","volume":"20 1","pages":"3633-3637"},"PeriodicalIF":0.0,"publicationDate":"2016-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85903620","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 : 2016-09-01DOI: 10.1109/ICIP.2016.7532327
Edward T. Scott, S. Hemami
Traditional quality estimators evaluate an image's resemblance to a reference image. However, quality estimators are not well suited to the similar but somewhat different task of utility estimation, where an image is judged instead by how useful it would be in comparison to a reference in the context of accomplishing some task. Multi-Scale Difference of Gaussian Utility (MS-DGU), a reduced-reference algorithm for image utility estimation, relies on matching image contours across scales tuned to spatial frequencies important for utility estimation. MS-DGU estimates utility with greater accuracy than previous techniques. A fast algorithm for utility-optimized image compression was developed through rate-utility optimization for MS-DGU. By simple scaling of JPEG quantization step sizes according to a “utility factor,” data rates were reduced by an average of 24% (and up to 30%) compared to standard JPEG while maintaining utility.
{"title":"Image utility estimation using difference-of-Gaussian scale space","authors":"Edward T. Scott, S. Hemami","doi":"10.1109/ICIP.2016.7532327","DOIUrl":"https://doi.org/10.1109/ICIP.2016.7532327","url":null,"abstract":"Traditional quality estimators evaluate an image's resemblance to a reference image. However, quality estimators are not well suited to the similar but somewhat different task of utility estimation, where an image is judged instead by how useful it would be in comparison to a reference in the context of accomplishing some task. Multi-Scale Difference of Gaussian Utility (MS-DGU), a reduced-reference algorithm for image utility estimation, relies on matching image contours across scales tuned to spatial frequencies important for utility estimation. MS-DGU estimates utility with greater accuracy than previous techniques. A fast algorithm for utility-optimized image compression was developed through rate-utility optimization for MS-DGU. By simple scaling of JPEG quantization step sizes according to a “utility factor,” data rates were reduced by an average of 24% (and up to 30%) compared to standard JPEG while maintaining utility.","PeriodicalId":6521,"journal":{"name":"2016 IEEE International Conference on Image Processing (ICIP)","volume":"44 1","pages":"101-105"},"PeriodicalIF":0.0,"publicationDate":"2016-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82561100","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 : 2016-09-01DOI: 10.1109/ICIP.2016.7532496
R. Hebbalaguppe, Kevin McGuinness, J. Kuklyte, Rami Albatal, C. Direkoğlu, N. O’Connor
The percentage of false alarms caused by spiders in automated surveillance can range from 20-50%. False alarms increase the workload of surveillance personnel validating the alarms and the maintenance labor cost associated with regular cleaning of webs. We propose a novel, cost effective method to detect false alarms triggered by spiders/webs in surveillance camera networks. This is accomplished by building a spider classifier intended to be a part of the surveillance video processing pipeline. The proposed method uses a feature descriptor obtained by early fusion of blur and texture. The approach is sufficiently efficient for real-time processing and yet comparable in performance with more computationally costly approaches like SIFT with bag of visual words aggregation. The proposed method can eliminate 98.5% of false alarms caused by spiders in a data set supplied by an industry partner, with a false positive rate of less than 1%.
{"title":"Reduction of false alarms triggered by spiders/cobwebs in surveillance camera networks","authors":"R. Hebbalaguppe, Kevin McGuinness, J. Kuklyte, Rami Albatal, C. Direkoğlu, N. O’Connor","doi":"10.1109/ICIP.2016.7532496","DOIUrl":"https://doi.org/10.1109/ICIP.2016.7532496","url":null,"abstract":"The percentage of false alarms caused by spiders in automated surveillance can range from 20-50%. False alarms increase the workload of surveillance personnel validating the alarms and the maintenance labor cost associated with regular cleaning of webs. We propose a novel, cost effective method to detect false alarms triggered by spiders/webs in surveillance camera networks. This is accomplished by building a spider classifier intended to be a part of the surveillance video processing pipeline. The proposed method uses a feature descriptor obtained by early fusion of blur and texture. The approach is sufficiently efficient for real-time processing and yet comparable in performance with more computationally costly approaches like SIFT with bag of visual words aggregation. The proposed method can eliminate 98.5% of false alarms caused by spiders in a data set supplied by an industry partner, with a false positive rate of less than 1%.","PeriodicalId":6521,"journal":{"name":"2016 IEEE International Conference on Image Processing (ICIP)","volume":"1 1","pages":"943-947"},"PeriodicalIF":0.0,"publicationDate":"2016-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79484024","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 : 2016-09-01DOI: 10.1109/ICIP.2016.7533056
Lokender Tiwari, Saket Anand
We propose a fast and efficient two-stage hypothesis filtering technique that can improve performance of clustering based robust multi-model fitting algorithms. Sampling based hypothesis generation is nondeterministic and permits little control over generating poor model hypotheses, often leading to a significant proportion of bad hypotheses. Our novel filtering approach leverages the asymmetry in the distributions of points around the inlier/outlier boundary via the sample skewness computed in the residual space. The output is a set of promising hypotheses which aid multi-model fitting algorithms in improving accuracy as well as running time. We validate our approach on the AdelaideRMF dataset and show favorable results along with comparisons to state-of-the-art.
{"title":"Fast hypothesis filtering for multi-structure geometric model fitting","authors":"Lokender Tiwari, Saket Anand","doi":"10.1109/ICIP.2016.7533056","DOIUrl":"https://doi.org/10.1109/ICIP.2016.7533056","url":null,"abstract":"We propose a fast and efficient two-stage hypothesis filtering technique that can improve performance of clustering based robust multi-model fitting algorithms. Sampling based hypothesis generation is nondeterministic and permits little control over generating poor model hypotheses, often leading to a significant proportion of bad hypotheses. Our novel filtering approach leverages the asymmetry in the distributions of points around the inlier/outlier boundary via the sample skewness computed in the residual space. The output is a set of promising hypotheses which aid multi-model fitting algorithms in improving accuracy as well as running time. We validate our approach on the AdelaideRMF dataset and show favorable results along with comparisons to state-of-the-art.","PeriodicalId":6521,"journal":{"name":"2016 IEEE International Conference on Image Processing (ICIP)","volume":"127 1","pages":"3728-3732"},"PeriodicalIF":0.0,"publicationDate":"2016-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79546294","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 : 2016-09-01DOI: 10.1109/ICIP.2016.7532775
Y. Matviychuk, B. Mailhé, Xiao Chen, Qiu Wang, A. Kiraly, N. Strobel, M. Nadar
Denoising is an indispensable step in processing low-dose X-ray fluoroscopic images that requires development of specialized high-quality algorithms able to operate in near real-time. We address this problem with an efficient deep learning approach based on the process-centric view of traditional iterative thresholding methods. We develop a novel trainable patch-based multiscale framework for sparse image representation. In a computationally efficient way, it allows us to accurately reconstruct important image features on multiple levels of decomposition with patch dictionaries of reduced size and complexity. The flexibility of the chosen machine learning approach allows us to tailor the learned basis for preserving important structural information in the image and noticeably minimize the amount of artifacts. Our denoising results obtained with real clinical data demonstrate significant quality improvement and are computed much faster in comparison with the BM3D algorithm.
{"title":"Learning a multiscale patch-based representation for image denoising in X-RAY fluoroscopy","authors":"Y. Matviychuk, B. Mailhé, Xiao Chen, Qiu Wang, A. Kiraly, N. Strobel, M. Nadar","doi":"10.1109/ICIP.2016.7532775","DOIUrl":"https://doi.org/10.1109/ICIP.2016.7532775","url":null,"abstract":"Denoising is an indispensable step in processing low-dose X-ray fluoroscopic images that requires development of specialized high-quality algorithms able to operate in near real-time. We address this problem with an efficient deep learning approach based on the process-centric view of traditional iterative thresholding methods. We develop a novel trainable patch-based multiscale framework for sparse image representation. In a computationally efficient way, it allows us to accurately reconstruct important image features on multiple levels of decomposition with patch dictionaries of reduced size and complexity. The flexibility of the chosen machine learning approach allows us to tailor the learned basis for preserving important structural information in the image and noticeably minimize the amount of artifacts. Our denoising results obtained with real clinical data demonstrate significant quality improvement and are computed much faster in comparison with the BM3D algorithm.","PeriodicalId":6521,"journal":{"name":"2016 IEEE International Conference on Image Processing (ICIP)","volume":"6 2 1","pages":"2330-2334"},"PeriodicalIF":0.0,"publicationDate":"2016-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78370389","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 : 2016-09-01DOI: 10.1109/ICIP.2016.7533030
Jiaqi Yang, Qian Zhang, Ke Xian, Yang Xiao, ZHIGUO CAO
This paper presents a novel local surface descriptor called rotational contour signatures (RCS) for 3D rigid objects. RCS comprises several signatures that characterize the 2D contour information derived from 3D-to-2D projection of the local surface. The inspiration of our encoding technique comes from that, viewing towards an object, its contour is an effective and robust cue for representing its shape. In order to achieve a comprehensive geometry encoding, the local surface is continually rotated in a predefined local reference frame (LRF) so that multi-view information is obtained. Experiments on two publicly available datasets demonstrate the effectiveness and robustness of the proposed descriptor. Further, comparisons with five state-of-the-art descriptors show the superiority of our RCS descriptor.
{"title":"Rotational contour signatures for robust local surface description","authors":"Jiaqi Yang, Qian Zhang, Ke Xian, Yang Xiao, ZHIGUO CAO","doi":"10.1109/ICIP.2016.7533030","DOIUrl":"https://doi.org/10.1109/ICIP.2016.7533030","url":null,"abstract":"This paper presents a novel local surface descriptor called rotational contour signatures (RCS) for 3D rigid objects. RCS comprises several signatures that characterize the 2D contour information derived from 3D-to-2D projection of the local surface. The inspiration of our encoding technique comes from that, viewing towards an object, its contour is an effective and robust cue for representing its shape. In order to achieve a comprehensive geometry encoding, the local surface is continually rotated in a predefined local reference frame (LRF) so that multi-view information is obtained. Experiments on two publicly available datasets demonstrate the effectiveness and robustness of the proposed descriptor. Further, comparisons with five state-of-the-art descriptors show the superiority of our RCS descriptor.","PeriodicalId":6521,"journal":{"name":"2016 IEEE International Conference on Image Processing (ICIP)","volume":"76 1","pages":"3598-3602"},"PeriodicalIF":0.0,"publicationDate":"2016-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83861193","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 : 2016-09-01DOI: 10.1109/ICIP.2016.7532986
Yongjian Yu, Jue Wang, S. Acton
We present a histogram-based real-time solution to detecting directly irradiated regions in digital fluoroscopic images. Our method leverages the power of model matching, machine learning and domain knowledge to characterize and segment images using histograms. The input image is automatically identified as containing partial, all, or null direct radiation. The regions with direct radiation are segmented out via global thresholding according to image characterizations. The algorithm involves only one-dimensional processing. The test results achieved 99.82% accurate detection rate on a dataset of 9256 clinical images.
{"title":"Automatic detection of direct radiation for digital fluoroscopy optimization","authors":"Yongjian Yu, Jue Wang, S. Acton","doi":"10.1109/ICIP.2016.7532986","DOIUrl":"https://doi.org/10.1109/ICIP.2016.7532986","url":null,"abstract":"We present a histogram-based real-time solution to detecting directly irradiated regions in digital fluoroscopic images. Our method leverages the power of model matching, machine learning and domain knowledge to characterize and segment images using histograms. The input image is automatically identified as containing partial, all, or null direct radiation. The regions with direct radiation are segmented out via global thresholding according to image characterizations. The algorithm involves only one-dimensional processing. The test results achieved 99.82% accurate detection rate on a dataset of 9256 clinical images.","PeriodicalId":6521,"journal":{"name":"2016 IEEE International Conference on Image Processing (ICIP)","volume":"1 1","pages":"3379-3383"},"PeriodicalIF":0.0,"publicationDate":"2016-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89232991","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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