公开(公告)号：US07929411B2

公开(公告)日：2011-04-19

申请号：US11529084

申请日：2006-09-28

申请人： Anna Scaglione ,  Yao-Win Hong ,  Pramod K. Varshney

发明人： Anna Scaglione ,  Yao-Win Hong ,  Pramod K. Varshney

IPC分类号： H04J11/00

CPC分类号： G06F17/30545

摘要： Methods and systems for obtaining data from a number of distributed sources.

摘要翻译： 从多个分布式源获取数据的方法和系统。

公开(公告)号：US08214177B2

公开(公告)日：2012-07-03

申请号：US12710143

申请日：2010-02-22

申请人： Renbin Peng ,  Hao Chen ,  Pramod K. Varshney

发明人： Renbin Peng ,  Hao Chen ,  Pramod K. Varshney

IPC分类号： H04B15/00 ,  G06F19/00

CPC分类号： G06T5/002 ,  A61B6/502 ,  G06T5/20 ,  G06T2207/10116 ,  G06T2207/30068 ,  H04B17/26

摘要： Apparatus and method for detecting micro-calcifications in mammograms using novel algorithms and stochastic resonance noise is provided, where a suitable dose of noise is added to the abnormal mammograms such that the performance of a suboptimal lesion detector is improved without altering the detector's parameters. A stochastic resonance noise-based detection approach is presented to improve suboptimal detectors which suffer from model mismatch due to the Gaussian assumption. Furthermore, a stochastic resonance noise-based detection enhancement framework is presented to deal with more general model mismatch cases.

摘要翻译： 提供了使用新颖的算法和随机共振噪声来检测乳房X线照片中的微钙化的装置和方法，其中将适当剂量的噪声添加到异常乳房X线照片中，使得不优选检测器的参数改善了次最佳病变检测器的性能。 提出了一种基于随机共振噪声的检测方法，以改进由于高斯假设而遭受模型不匹配的次优检测器。 此外，提出了一种随机共振噪声检测增强框架来处理更一般的模型不匹配情况。

公开(公告)号：US09026404B2

公开(公告)日：2015-05-05

申请号：US13410949

申请日：2012-03-02

申请人： Renbin Peng ,  Pramod K. Varshney

发明人： Renbin Peng ,  Pramod K. Varshney

IPC分类号： H04B15/00 ,  A61B6/00 ,  G06T5/20 ,  H04B17/00 ,  G06T5/00 ,  G06F19/00

CPC分类号： A61B6/502 ,  G06T5/002 ,  G06T5/20 ,  G06T2207/10116 ,  G06T2207/30068 ,  H04B17/26

摘要： Apparatus and method for improving the performance of a threshold-based detector or classifier, or a generic detector or classifier and increasing the probability of detecting at least one object in an image using novel algorithms and stochastic resonance noise is provided, where a suitable dose of noise is introduced to the image data such that the performance of the above-referenced detectors or classifiers is improved without altering the detector's or classifier's parameters. Several stochastic resonance (SR) noise-based detection and classification enhancement schemes are presented. The SR noise-enhanced detection and classification schemes can improve any algorithms and systems. To implement these schemes, the only knowledge that is needed is the original input data (no matter 1D, 2D, 3D or others) and the output (detection results) of the existing algorithms and systems.

摘要翻译： 提供了一种用于改进基于阈值的检测器或分类器或通用检测器或分类器的性能并且增加使用新颖算法和随机共振噪声来检测图像中的至少一个对象的概率的装置和方法，其中适当剂量的 将噪声引入到图像数据中，使得上述参考的检测器或分类器的性能得到改善而不改变检测器或分类器的参数。 提出了几种随机共振（SR）噪声检测和分类增强方案。 SR噪声增强检测和分类方案可以改进任何算法和系统。 为了实现这些方案，唯一需要知道的是原始输入数据（无论是1D，2D，3D还是其他）和现有算法和系统的输出（检测结果）。

公开(公告)号：US20120278039A1

公开(公告)日：2012-11-01

申请号：US13410949

申请日：2012-03-02

申请人： Renbin Peng ,  Pramod K. Varshney

发明人： Renbin Peng ,  Pramod K. Varshney

IPC分类号： G06F17/18

CPC分类号： A61B6/502 ,  G06T5/002 ,  G06T5/20 ,  G06T2207/10116 ,  G06T2207/30068 ,  H04B17/26

摘要： Apparatus and method for improving the performance of a threshold-based detector or classifier, or a generic detector or classifier and increasing the probability of detecting at least one object in an image using novel algorithms and stochastic resonance noise is provided, where a suitable dose of noise is introduced to the image data such that the performance of the above-referenced detectors or classifiers is improved without altering the detector's or classifier's parameters. Several stochastic resonance (SR) noise-based detection and classification enhancement schemes are presented. The SR noise-enhanced detection and classification schemes can improve any algorithms and systems. To implement these schemes, the only knowledge that is needed is the original input data (no matter 1D, 2D, 3D or others) and the output (detection results) of the existing algorithms and systems.

摘要翻译： 提供了一种用于改进基于阈值的检测器或分类器或通用检测器或分类器的性能并且增加使用新颖算法和随机共振噪声来检测图像中的至少一个对象的概率的装置和方法，其中适当剂量的 将噪声引入到图像数据中，使得上述参考的检测器或分类器的性能得到改善而不改变检测器或分类器的参数。 提出了几种随机共振（SR）噪声检测和分类增强方案。 SR噪声增强检测和分类方案可以改进任何算法和系统。 为了实现这些方案，唯一需要知道的是原始输入数据（无论是1D，2D，3D还是其他）和现有算法和系统的输出（检测结果）。

公开(公告)号：US20100169051A1

公开(公告)日：2010-07-01

申请号：US12710143

申请日：2010-02-22

申请人： Renbin Peng ,  Hao Chen ,  Pramod K. Varshney

发明人： Renbin Peng ,  Hao Chen ,  Pramod K. Varshney

IPC分类号： G06F15/00

CPC分类号： G06T5/002 ,  A61B6/502 ,  G06T5/20 ,  G06T2207/10116 ,  G06T2207/30068 ,  H04B17/26

摘要： Apparatus and method for detecting micro-calcifications in mammograms using novel algorithms and stochastic resonance noise is provided, where a suitable dose of noise is added to the abnormal mammograms such that the performance of a suboptimal lesion detector is improved without altering the detector's parameters. A stochastic resonance noise-based detection approach is presented to improve suboptimal detectors which suffer from model mismatch due to the Gaussian assumption. Furthermore, a stochastic resonance noise-based detection enhancement framework is presented to deal with more general model mismatch cases.

摘要翻译： 提供了使用新颖的算法和随机共振噪声来检测乳房X线照片中的微钙化的装置和方法，其中将适当剂量的噪声添加到异常乳房X线照片中，使得不优选检测器的参数改善了次最佳病变检测器的性能。 提出了一种基于随机共振噪声的检测方法，以改进由于高斯假设而遭受模型不匹配的次优检测器。 此外，提出了一种随机共振噪声检测增强框架来处理更一般的模型不匹配情况。

公开(公告)号：US08131512B2

公开(公告)日：2012-03-06

申请号：US12710143

申请日：2010-02-22

申请人： Renbin Peng ,  Hao Chen ,  Pramod K. Varshney

发明人： Renbin Peng ,  Hao Chen ,  Pramod K. Varshney

IPC分类号： H04B15/00 ,  G06F19/00

摘要： Apparatus and method for detecting micro-calcifications in mammograms using novel algorithms and stochastic resonance noise is provided, where a suitable dose of noise is added to the abnormal mammograms such that the performance of a suboptimal lesion detector is improved without altering the detector's parameters. A stochastic resonance noise-based detection approach is presented to improve suboptimal detectors which suffer from model mismatch due to the Gaussian assumption. Furthermore, a stochastic resonance noise-based detection enhancement framework is presented to deal with more general model mismatch cases.