公开(公告)号：US20160012316A1

公开(公告)日：2016-01-14

申请号：US14853863

申请日：2015-09-14

Applicant: King Fahd University of Petroleum and Minerals ,  King Abdulaziz City for Science and Technology

Inventor： Lahouari GHOUTI

IPC: G06K9/62 ,  G06K9/46 ,  G06T11/60 ,  G06T5/00 ,  G06K9/52 ,  G06T7/00 ,  G06F17/30

CPC classification number: G06K9/6267 ,  A61B5/0013 ,  A61B5/002 ,  A61B5/4312 ,  A61B5/7203 ,  A61B5/7225 ,  A61B5/7267 ,  A61B2560/0475 ,  A61B2576/02 ,  G06F17/30247 ,  G06F19/00 ,  G06K9/46 ,  G06K9/52 ,  G06K2009/4666 ,  G06T5/00 ,  G06T7/0012 ,  G06T11/60 ,  G06T2207/10116 ,  G06T2207/20076 ,  G06T2207/30068 ,  G16H30/40

Abstract: A method for content-based image retrieval for the classification of breast density from mammographic imagery is described. The breast density is characterized through the Fisher linear discriminants (FLD) extracted from the Principal Component Analysis (PCA). Unlike PCA, the FLD provides a very discriminative representation of the mammographic images in terms of the breast density. Various exemplary methods, systems and computer program products are also disclosed.

Abstract translation: 描述了一种基于内容的图像检索方法，用于从乳房造影图像分类乳房密度。 通过从主成分分析（PCA）提取的Fisher线性鉴别（FLD）表征乳房密度。 与PCA不同，FLD在乳房密度方面提供了对乳房X线照相图像的非常歧视性的表示。 还公开了各种示例性方法，系统和计算机程序产品。

公开(公告)号：US20170199525A1

公开(公告)日：2017-07-13

申请号：US14990840

申请日：2016-01-08

Applicant: KING FAHD UNIVERSITY OF PETROLEUM AND MINERALS

Inventor： Anas Mohammed ALBAGHAJATI ,  Mohammad Tariq NASIR ,  Lahouari GHOUTI ,  Sami EL FERIK

IPC: G05D1/02 ,  G06T11/20 ,  H04W4/02 ,  H04N5/225 ,  H04B7/185

CPC classification number: G05D1/0214 ,  G01V1/003 ,  G01V1/166 ,  G01V1/168 ,  G05D1/0238 ,  G05D1/0246 ,  G05D1/0274 ,  G05D2201/0207 ,  G06T11/206 ,  H04B7/18523 ,  H04N5/225 ,  H04W4/02

Abstract: Described herein a robot assisted method of deploying sensors in a geographic region. The method of deploying sensors is posed as a Markovian decision process. The robot assigns each grid cell in a map of the geographic region a reward value based on a surface elevation of the geographic region and a soil hardness factor. Further, the robot determines an action for each grid cell of the plurality of grid cells, wherein the action corresponds to an expected direction of movement of the robot in the grid cell. The robot computes a global path as a concatenation of actions starting from a first grid cell and terminating at a second grid cell. The method monitors the movement of the robot on the computed global path and computes a second path based on a deviation of the robot from the global path.

公开(公告)号：US20160314579A1

公开(公告)日：2016-10-27

申请号：US14797977

申请日：2015-07-13

Applicant: King Fahd University of Petroleum and Minerals

Inventor： Lahouari GHOUTI ,  Abdullah Hussein OWAIDH

IPC: G06T7/00 ,  G06K9/52 ,  G06K9/62

CPC classification number: G06T7/0012 ,  G06K9/3241 ,  G06K9/52 ,  G06K9/6239 ,  G06K9/6267 ,  G06K9/6269 ,  G06K2209/05 ,  G06T2207/10116 ,  G06T2207/20172 ,  G06T2207/30068

Abstract: An automated content-based image retrieval method, a system and a computer program product for the classification of breast density from mammographic imagery. Raw digital mammogram images taken of patients are initially pre-processed to remove noise and enhance contrast, then subjected to pectoral muscle segmentation to produce region of interest (ROI) images. The ROI images are then decomposed using non-negative matrix factorization (NMF), where a non-negative sparsity constraint and reconstruction quality measures are imposed on the extracted and retained first few NMF factors. Based on the retained NMF factors, kernel matrix-based support vector machines classify the mammogram images binomially or multinomially to breast density categories. Methods of assessing and comparing the NMF-based breast classification method to principal component analysis or PCA-based methods are also described, and the NMF-based method is found to achieve higher classification accuracy and better handling of invariance in the digital mammogram images because of its parts-based factorization.

Abstract translation: 一种基于内容的自动图像检索方法，一种用于从乳房X线照相图像分类乳腺密度的系统和计算机程序产品。 最初对病人拍摄的原始数字乳房X线照片图像进行预处理，以消除噪音并增强对比度，然后进行胸肌分割以产生感兴趣区域（ROI）图像。 然后使用非负矩阵分解（NMF）分解ROI图像，其中对提取和保留的最初几个NMF因子施加非负稀疏约束和重建质量测量。 基于保留的NMF因子，基于内核矩阵的支持向量机将乳房X线照片图像二进制或多次分类为乳腺密度类别。 还描述了将基于NMF的乳房分类方法与主成分分析或基于PCA的方法进行评估和比较的方法，并且发现基于NMF的方法能够实现更高的分类精度和更好地处理数字乳房X线照片图像中的不变性，因为 其基于零件的分解。

公开(公告)号：US20160086087A1

公开(公告)日：2016-03-24

申请号：US14491373

申请日：2014-09-19

Applicant: King Fahd University of Petroleum and Minerals

Inventor： Lahouari GHOUTI

IPC: G06N5/04 ,  G06N99/00

CPC classification number: C10G7/12 ,  C10G2300/1033 ,  G06N3/02

Abstract: A method and device for predicting a gas composition, including pre-processing, by non-negative matrix factorization, a set of input parameters related to a fluid mixture of hydrocarbons and non-hydrocarbons fed into a multistage separator, and training an extreme learning machine model to predict the composition of non-hydrocarbons in the fluid mixture.

Abstract translation: 一种预测气体组成的方法和装置，包括通过非负矩阵分解进行预处理，一组与烃类和非烃类的流体混合物相关的输入参数，其被馈送到多级分离器中，以及训练极端学习机 模型来预测流体混合物中非碳氢化合物的组成。