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1. 发明申请

US20130039566A1 CODING OF FEATURE LOCATION INFORMATION 有权
标题翻译：特征位置信息编码
公开(公告)号：US20130039566A1
公开(公告)日：2013-02-14
申请号：US13229654
申请日：2011-09-09
申请人： Yuriy Reznick , Onur C. Hamsici , Sundeep Vaddadi , John H. Hong , Chong U. Lee
发明人： Yuriy Reznick , Onur C. Hamsici , Sundeep Vaddadi , John H. Hong , Chong U. Lee
IPC分类号： G06K9/36 , G06K9/00
CPC分类号： G06T9/00 , G06F17/18 , G06F17/30247 , G06K9/4671 , G06K2009/226 , G06T9/005
摘要： Methods and devices for coding of feature locations are disclosed. In one embodiment, a method of coding feature location information of an image includes generating a hexagonal grid, where the hexagonal grid includes a plurality of hexagonal cells, quantizing feature locations of an image using the hexagonal grid, generating a histogram to record occurrences of feature locations in each hexagonal cell, and encoding the histogram in accordance with the occurrences of feature locations in each hexagonal cell. The method of encoding the histogram includes applying context information of neighboring hexagonal cells to encode information of a subsequent hexagonal cell to be encoded in the histogram, where the context information includes context information from first order neighbors and context information from second order neighbors of the subsequent hexagonal cell to be encoded.
摘要翻译：公开了用于编码特征位置的方法和装置。在一个实施例中，一种编码图像的特征位置信息的方法包括生成六边形网格，其中六边形网格包括多个六边形单元格，使用六边形网格量化图像的特征位置，生成直方图以记录特征的出现每个六边形单元格中的位置，并根据每个六边形单元格中特征位置的出现来对直方图进行编码。对直方图进行编码的方法包括应用相邻六边形单元格的上下文信息来编码在直方图中要编码的随后的六边形单元格的信息，其中上下文信息包括来自一阶邻居的上下文信息和来自第二阶邻居的上下文信息六角形单元格进行编码。

2. 发明申请

US20110170780A1 SCALE SPACE NORMALIZATION TECHNIQUE FOR IMPROVED FEATURE DETECTION IN UNIFORM AND NON-UNIFORM ILLUMINATION CHANGES 有权
标题翻译：用于改进特征检测的均匀空间正常化技术和非均匀照明变化
公开(公告)号：US20110170780A1
公开(公告)日：2011-07-14
申请号：US12986607
申请日：2011-01-07
申请人： Sundeep Vaddadi , John H. Hong , Onur C. Hamsici , Chong U. Lee
发明人： Sundeep Vaddadi , John H. Hong , Onur C. Hamsici , Chong U. Lee
IPC分类号： G06K9/46
CPC分类号： G06K9/4671
摘要： A normalization process is implemented at a difference of scale space to completely or substantially reduce the effect that illumination changes has on feature/keypoint detection in an image. An image may be processed by progressively blurring the image using a smoothening function to generate a smoothened scale space for the image. A difference of scale space may be generated by taking the difference between two different smoothened versions of the image. A normalized difference of scale space image may be generated by dividing the difference of scale space image by a third smoothened version of the image, where the third smoothened version of the image that is as smooth or smoother than the smoothest of the two different smoothened versions of the image. The normalized difference of scale space image may then be used to detect one or more features/keypoints for the image.
摘要翻译：在尺度空间的差异上实现归一化过程以完全或基本上减少照明变化对图像中的特征/关键点检测的影响。可以通过使用平滑函数逐渐模糊图像来生成图像来生成图像，以生成平滑的图像尺度空间。可以通过获取图像的两个不同平滑版本之间的差异来产生比例空间的差异。尺度空间图像的归一化差异可以通过将尺度空间图像的差除以图像的第三平滑版本而生成，其中图像的第三平滑版本比两个不同平滑版本中最平滑的平滑或平滑的图像。尺度空间图像的归一化差异可以用于检测图像的一个或多个特征/关键点。

3. 发明授权

US09036925B2 Robust feature matching for visual search 有权
标题翻译：强大的功能匹配视觉搜索
公开(公告)号：US09036925B2
公开(公告)日：2015-05-19
申请号：US13312335
申请日：2011-12-06
申请人： Sundeep Vaddadi , Onur C. Hamsici , Yuriy Reznik , John H. Hong , Chong U. Lee
发明人： Sundeep Vaddadi , Onur C. Hamsici , Yuriy Reznik , John H. Hong , Chong U. Lee
IPC分类号： G06K9/62 , G06K9/54 , G06K9/60 , G06F17/30
CPC分类号： G06F17/30247 , G06K9/62 , G06K9/627
摘要： Techniques are disclosed for performing robust feature matching for visual search. An apparatus comprising an interface and a feature matching unit may implement these techniques. The interface receives a query feature descriptor. The feature matching unit then computes a distance between a query feature descriptor and reference feature descriptors and determines a first group of the computed distances and a second group of the computed distances in accordance with a clustering algorithm, where this second group of computed distances comprises two or more of the computed distances. The feature matching unit then determines whether the query feature descriptor matches one of the reference feature descriptors associated with a smallest one of the computed distances based on the determined first group and second group of the computed distances.
摘要翻译：公开了用于执行用于视觉搜索的鲁棒特征匹配的技术。包括接口和特征匹配单元的装置可以实现这些技术。接口接收查询特征描述符。特征匹配单元然后计算查询特征描述符和参考特征描述符之间的距离，并且根据聚类算法确定计算出的距离的第一组和所计算的距离的第二组，其中该第二组计算距离包括两个或更多的计算距离。特征匹配单元然后基于所确定的计算出的距离的第一组和第二组来确定查询特征描述符是否与与计算出的距离中的最小一个相关联的参考特征描述符之一匹配。

4. 发明申请

US20120263388A1 ROBUST FEATURE MATCHING FOR VISUAL SEARCH 有权
标题翻译：强大的功能匹配视觉搜索
公开(公告)号：US20120263388A1
公开(公告)日：2012-10-18
申请号：US13312335
申请日：2011-12-06
申请人： Sundeep Vaddadi , Onur C. Hamsici , Yuriy Reznik , John H. Hong , Chong U. Lee
发明人： Sundeep Vaddadi , Onur C. Hamsici , Yuriy Reznik , John H. Hong , Chong U. Lee
IPC分类号： G06K9/62
CPC分类号： G06F17/30247 , G06K9/62 , G06K9/627
摘要： Techniques are disclosed for performing robust feature matching for visual search. An apparatus comprising an interface and a feature matching unit may implement these techniques. The interface receives a query feature descriptor. The feature matching unit then computes a distance between a query feature descriptor and reference feature descriptors and determines a first group of the computed distances and a second group of the computed distances in accordance with a clustering algorithm, where this second group of computed distances comprises two or more of the computed distances. The feature matching unit then determines whether the query feature descriptor matches one of the reference feature descriptors associated with a smallest one of the computed distances based on the determined first group and second group of the computed distances.
摘要翻译：公开了用于执行用于视觉搜索的鲁棒特征匹配的技术。包括接口和特征匹配单元的装置可以实现这些技术。接口接收查询特征描述符。特征匹配单元然后计算查询特征描述符和参考特征描述符之间的距离，并且根据聚类算法确定计算出的距离的第一组和所计算的距离的第二组，其中该第二组计算距离包括两个或更多的计算距离。特征匹配单元然后基于所确定的计算出的距离的第一组和第二组来确定查询特征描述符是否与与计算出的距离中的最小一个相关联的参考特征描述符之一匹配。

5. 发明申请

US20110299770A1 PERFORMANCE OF IMAGE RECOGNITION ALGORITHMS BY PRUNING FEATURES, IMAGE SCALING, AND SPATIALLY CONSTRAINED FEATURE MATCHING 有权
标题翻译：图像识别算法通过特征，图像调整和空间约束特征匹配的性能
公开(公告)号：US20110299770A1
公开(公告)日：2011-12-08
申请号：US12959056
申请日：2010-12-02
申请人： Sundeep Vaddadi , John H. Hong , Onur C. Hamsici , Yuriy Reznik , Chong U. Lee
发明人： Sundeep Vaddadi , John H. Hong , Onur C. Hamsici , Yuriy Reznik , Chong U. Lee
IPC分类号： G06K9/46
CPC分类号： G06K9/6211
摘要： A method for feature matching in image recognition is provided. First, image scaling may be based on a feature distribution across scale spaces for an image to estimate image size/resolution, where peak(s) in the keypoint distribution at different scales is used to track a dominant image scale and roughly track object sizes. Second, instead of using all detected features in an image for feature matching, keypoints may be pruned based on cluster density and/or the scale level in which the keypoints are detected. Keypoints falling within high-density clusters may be preferred over features falling within lower density clusters for purposes of feature matching. Third, inlier-to-outlier keypoint ratios are increased by spatially constraining keypoints into clusters in order to reduce or avoid geometric consistency checking for the image.
摘要翻译：提供了图像识别中特征匹配的方法。首先，图像缩放可以基于用于图像的尺度空间上的特征分布来估计图像尺寸/分辨率，其中使用不同尺度的关键点分布中的峰值来跟踪主要图像尺度并粗略跟踪对象尺寸。其次，代替使用图像中的所有检测到的特征进行特征匹配，可以基于检测到关键点的聚类密度和/或比例级别来修剪关键点。落入高密度群集中的关键点可能优于落入低密度群集中的特征，用于特征匹配。第三，通过将关键点空间约束成簇，从而增加关键点比率，以减少或避免图像的几何一致性检查。

6. 发明授权

US08706711B2 Descriptor storage and searches of k-dimensional trees 失效
标题翻译：描述符存储和搜索k维树
公开(公告)号：US08706711B2
公开(公告)日：2014-04-22
申请号：US13340024
申请日：2011-12-29
申请人： Sundeep Vaddadi , Onur C. Hamsici , John H. Hong , Yuriy Reznik , Chong U. Lee
发明人： Sundeep Vaddadi , Onur C. Hamsici , John H. Hong , Yuriy Reznik , Chong U. Lee
IPC分类号： G06F17/30
CPC分类号： G06K9/6282 , G06K9/6228
摘要： Various arrangements for using a k-dimensional tree for a search are presented. A plurality of descriptors may be stored. Each of the plurality of descriptors stored is linked with a first number of stored dimensions. The search may be performed using the k-dimensional tree for one or more query descriptors that at least approximately match one or more of the plurality of descriptors linked with the first number of stored dimensions. The k-dimensional tree may be built using the plurality of descriptors wherein each of the plurality of descriptors is linked with a second number of dimensions when the k-dimensional tree is built. The second number of dimensions may be a greater number of dimensions than the first number of stored dimensions.
摘要翻译：提出了使用k维树进行搜索的各种布置。可以存储多个描述符。所存储的多个描述符中的每一个与存储维度的第一数量相关联。可以使用k维树进行搜索，所述查询描述符至少近似地匹配与第一数量的存储维度相链接的多个描述符中的一个或多个。可以使用多个描述符构建k维树，其中当构建k维树时，多个描述符中的每个描述符与第二数量的维度相关联。第二数量的尺寸可以是比第一数量的存储尺寸更大的尺寸数量。

7. 发明授权

US08705876B2 Improving performance of image recognition algorithms by pruning features, image scaling, and spatially constrained feature matching 有权
标题翻译：通过修剪特征，图像缩放和空间约束的特征匹配来提高图像识别算法的性能
公开(公告)号：US08705876B2
公开(公告)日：2014-04-22
申请号：US12959056
申请日：2010-12-02
申请人： Sundeep Vaddadi , John H. Hong , Onur C. Hamsici , Yuriy Reznik , Chong U. Lee
发明人： Sundeep Vaddadi , John H. Hong , Onur C. Hamsici , Yuriy Reznik , Chong U. Lee
IPC分类号： G06K9/62
CPC分类号： G06K9/6211
摘要： A method for feature matching in image recognition is provided. First, image scaling may be based on a feature distribution across scale spaces for an image to estimate image size/resolution, where peak(s) in the keypoint distribution at different scales is used to track a dominant image scale and roughly track object sizes. Second, instead of using all detected features in an image for feature matching, keypoints may be pruned based on cluster density and/or the scale level in which the keypoints are detected. Keypoints falling within high-density clusters may be preferred over features falling within lower density clusters for purposes of feature matching. Third, inlier-to-outlier keypoint ratios are increased by spatially constraining keypoints into clusters in order to reduce or avoid geometric consistency checking for the image.
摘要翻译：提供了图像识别中特征匹配的方法。首先，图像缩放可以基于用于图像的尺度空间上的特征分布来估计图像尺寸/分辨率，其中使用不同尺度的关键点分布中的峰值来跟踪主要图像尺度并粗略跟踪对象尺寸。其次，代替使用图像中的所有检测到的特征进行特征匹配，可以基于检测到关键点的聚类密度和/或比例级别来修剪关键点。落入高密度群集中的关键点可能优于落入低密度群集中的特征，用于特征匹配。第三，通过将关键点空间约束成簇，从而增加关键点比率，以减少或避免图像的几何一致性检查。

8. 发明授权

US08582889B2 Scale space normalization technique for improved feature detection in uniform and non-uniform illumination changes 有权
标题翻译：尺度空间归一化技术，用于改进均匀和不均匀照明变化中的特征检测
公开(公告)号：US08582889B2
公开(公告)日：2013-11-12
申请号：US12986607
申请日：2011-01-07
申请人： Sundeep Vaddadi , John H. Hong , Onur C. Hamsici , Chong U. Lee
发明人： Sundeep Vaddadi , John H. Hong , Onur C. Hamsici , Chong U. Lee
IPC分类号： G06K9/00
CPC分类号： G06K9/4671
摘要： A normalization process is implemented at a difference of scale space to completely or substantially reduce the effect that illumination changes has on feature/keypoint detection in an image. An image may be processed by progressively blurring the image using a smoothening function to generate a smoothened scale space for the image. A difference of scale space may be generated by taking the difference between two different smoothened versions of the image. A normalized difference of scale space image may be generated by dividing the difference of scale space image by a third smoothened version of the image, where the third smoothened version of the image that is as smooth or smoother than the smoothest of the two different smoothened versions of the image. The normalized difference of scale space image may then be used to detect one or more features/keypoints for the image.
摘要翻译：在尺度空间的差异上实现归一化过程以完全或基本上减少照明变化对图像中的特征/关键点检测的影响。可以通过使用平滑函数逐渐模糊图像来生成图像来生成图像，以生成平滑的图像尺度空间。可以通过获取图像的两个不同平滑版本之间的差异来产生比例空间的差异。尺度空间图像的归一化差异可以通过将尺度空间图像的差除以图像的第三平滑版本而生成，其中图像的第三平滑版本比两个不同平滑版本中最平滑的平滑或平滑的图像。尺度空间图像的归一化差异可以用于检测图像的一个或多个特征/关键点。

9. 发明申请

US20120330967A1 Descriptor storage and searches of k-dimensional trees 失效
标题翻译：描述符存储和搜索k维树
公开(公告)号：US20120330967A1
公开(公告)日：2012-12-27
申请号：US13340024
申请日：2011-12-29
申请人： Sundeep Vaddadi , Onur C. Hamsici , John H. Hong , Yuriy Reznik , Chong U. Lee
发明人： Sundeep Vaddadi , Onur C. Hamsici , John H. Hong , Yuriy Reznik , Chong U. Lee
IPC分类号： G06F17/30
CPC分类号： G06K9/6282 , G06K9/6228
摘要： Various arrangements for using a k-dimensional tree for a search are presented. A plurality of descriptors may be stored. Each of the plurality of descriptors stored is linked with a first number of stored dimensions. The search may be performed using the k-dimensional tree for one or more query descriptors that at least approximately match one or more of the plurality of descriptors linked with the first number of stored dimensions. The k-dimensional tree may be built using the plurality of descriptors wherein each of the plurality of descriptors is linked with a second number of dimensions when the k-dimensional tree is built. The second number of dimensions may be a greater number of dimensions than the first number of stored dimensions.
摘要翻译：提出了使用k维树进行搜索的各种布置。可以存储多个描述符。所存储的多个描述符中的每一个与存储维度的第一数量相关联。可以使用k维树进行搜索，所述查询描述符至少近似地匹配与第一数量的存储维度相链接的多个描述符中的一个或多个。可以使用多个描述符构建k维树，其中当构建k维树时，多个描述符中的每个描述符与第二数量的维度相关联。第二数量的尺寸可以是比第一数量的存储尺寸更大的尺寸数量。

10. 发明授权

US08897572B2 Fast subspace projection of descriptor patches for image recognition 有权
标题翻译：用于图像识别的描述符片的快速子空间投影
公开(公告)号：US08897572B2
公开(公告)日：2014-11-25
申请号：US12959347
申请日：2010-12-02
申请人： Onur C. Hamsici , Yuriy Reznik , John Hong , Sundeep Vaddadi , Chong U. Lee
发明人： Onur C. Hamsici , Yuriy Reznik , John Hong , Sundeep Vaddadi , Chong U. Lee
IPC分类号： G06K9/46
CPC分类号： G06K9/4671
摘要： A method for generating a feature descriptor is provided. A set of pre-generated sparse projection vectors is obtained. A scale space for an image is also obtained, where the scale space having a plurality scale levels. A descriptor for a keypoint in the scale space is then generated based on a combination of the sparse projection vectors and sparsely sampled pixel information for a plurality of pixels across the plurality of scale levels.
摘要翻译：提供了一种生成特征描述符的方法。获得一组预先生成的稀疏投影向量。也可以获得图像的标度空间，其中刻度空间具有多个刻度级别。然后，基于稀疏投影矢量和跨越多个比例级别的多个像素的稀疏采样的像素信息的组合来生成缩放空间中的关键点的描述符。

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