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1. 发明授权

US08452109B2 Image segregation system with method for handling textures 有权
标题翻译：图像分离系统与处理纹理的方法
公开(公告)号：US08452109B2
公开(公告)日：2013-05-28
申请号：US12655930
申请日：2010-01-11
申请人： Neil Alldrin , Kshitiz Garg , Andrew Neil Stein , Kristin Jean Dana , Bruce Allen Maxwell , Casey Arthur Smith , Youngrock Yoon , Besma Roui Abidi
发明人： Neil Alldrin , Kshitiz Garg , Andrew Neil Stein , Kristin Jean Dana , Bruce Allen Maxwell , Casey Arthur Smith , Youngrock Yoon , Besma Roui Abidi
IPC分类号： G06K9/62
CPC分类号： G06K9/4661 , G06T7/11 , G06T7/162 , G06T7/41 , G06T7/49 , G06T7/90 , G06T2200/24 , G06T2207/10024
摘要： In a first exemplary embodiment of the present invention, an automated, computerized method is provided for processing an image. According to a feature of the present invention, the method comprises the steps of providing an image file depicting an image, in a computer memory, assembling a feature vector for the image file, the feature vector containing information regarding a likelihood that a selected pair of regions of the image file are of a same intrinsic characteristic, for example, a same texture, providing a classifier derived from a computer learning technique, computing a classification score for the selected pair of regions of the image file, as a function of the feature vector and the classifier and classifying the regions as being of the same intrinsic characteristic, as a function of the classification score.
摘要翻译：在本发明的第一示例性实施例中，提供了一种用于处理图像的自动计算机化方法。根据本发明的特征，该方法包括以下步骤：提供描绘图像的图像文件，在计算机存储器中组装图像文件的特征向量，所述特征向量包含关于所选择的一对图像文件的区域具有相同的固有特性，例如相同的纹理，提供从计算机学习技术导出的分类器，根据特征的函数计算所选择的图像文件区域的分类分数矢量和分类器，并将区域分类为具有相同固有特征的区域，作为分类分数的函数。

2. 发明申请

US20110170768A1 Image segregation system with method for handling textures 有权
标题翻译：图像分离系统与处理纹理的方法
公开(公告)号：US20110170768A1
公开(公告)日：2011-07-14
申请号：US12655930
申请日：2010-01-11
申请人： Neil Alldrin , Kshitiz Garg , Andrew Neil Stein , Kristin Jean Dana , Bruce Allen Maxwell , Casey Arthur Smith , Youngrock Yoon , Besma Roui Abidi
发明人： Neil Alldrin , Kshitiz Garg , Andrew Neil Stein , Kristin Jean Dana , Bruce Allen Maxwell , Casey Arthur Smith , Youngrock Yoon , Besma Roui Abidi
IPC分类号： G06K9/62
CPC分类号： G06K9/4661 , G06T7/11 , G06T7/162 , G06T7/41 , G06T7/49 , G06T7/90 , G06T2200/24 , G06T2207/10024
摘要： In a first exemplary embodiment of the present invention, an automated, computerized method is provided for processing an image. According to a feature of the present invention, the method comprises the steps of providing an image file depicting an image, in a computer memory, assembling a feature vector for the image file, the feature vector containing information regarding a likelihood that a selected pair of regions of the image file are of a same intrinsic characteristic, for example, a same texture, providing a classifier derived from a computer learning technique, computing a classification score for the selected pair of regions of the image file, as a function of the feature vector and the classifier and classifying the regions as being of the same intrinsic characteristic, as a function of the classification score.
摘要翻译：在本发明的第一示例性实施例中，提供了一种用于处理图像的自动计算机化方法。根据本发明的特征，该方法包括以下步骤：提供描绘图像的图像文件，在计算机存储器中组装图像文件的特征向量，所述特征向量包含关于所选择的一对图像文件的区域具有相同的固有特性，例如相同的纹理，提供从计算机学习技术导出的分类器，根据特征的函数计算所选择的图像文件区域的分类分数矢量和分类器，并将区域分类为具有相同固有特征的区域，作为分类分数的函数。

3. 发明授权

US07840066B1 Method of enhancing a digital image by gray-level grouping 失效
标题翻译：通过灰度级分组增强数字图像的方法
公开(公告)号：US07840066B1
公开(公告)日：2010-11-23
申请号：US11598943
申请日：2006-11-14
申请人： Zhiyu Chen , Besma Roui Abidi , Mongi Al Abidi
发明人： Zhiyu Chen , Besma Roui Abidi , Mongi Al Abidi
IPC分类号： G06K9/00 , G06K9/40 , G06K9/38
CPC分类号： G06T5/007 , G06T5/002 , G06T5/40 , G06T2207/10024 , G06T2207/10032 , G06T2207/10116 , G06T2207/20012 , G06T2207/20021 , G06T2207/20032 , G06T2207/20064 , G06T2207/20221 , G06T2207/20224 , G06T2207/30112 , G06T2207/30201
摘要： Methods for enhancing the quality of an electronic image, including automated methods for contrast enhancement and image noise reduction. In many embodiments the method provides for grouping the histogram components of a low-contrast image into the proper number of bins according to a criterion, then redistributing these bins uniformly over the grayscale, and finally ungrouping the previously grouped gray-levels. The technique is named gray-level grouping (GLG). An extension of GLG called Selective Gray-Level Grouping (SGLG), and various variations thereof are also provided. SGLG selectively groups and ungroups histogram components to achieve specific application purposes, such as eliminating background noise, enhancing a specific segment of the histogram, and so on. GLG techniques may be applied to both monochromatic grayscale images and to color images. Several preprocessing or postprocessing methods for image noise reduction are provided for use independently or for use with GLG techniques to both reduce or eliminate background noise and enhance the image contrast in noisy low-contrast images.
摘要翻译：用于提高电子图像质量的方法，包括用于对比度增强和图像噪声降低的自动化方法。在许多实施例中，该方法提供了根据标准将低对比度图像的直方图分量分组成适当数量的分组，然后将灰度均匀地重新分布在灰度上，最后取消分组先前分组的灰度级。该技术被命名为灰阶分组（GLG）。还提供了GLG的扩展名称选择性灰阶分组（SGLG）及其各种变体。 SGLG选择性地组合和取消分组直方图组件以实现特定的应用目的，例如消除背景噪声，增强直方图的特定段等。 GLG技术可以应用于单色灰度图像和彩色图像。提供用于图像噪声降低的几种预处理或后处理方法用于独立使用或与GLG技术一起使用以减少或消除背景噪声并增强嘈杂的低对比度图像中的图像对比度。

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