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    • 1. 发明申请
    • CODING OF FEATURE LOCATION INFORMATION
    • 特征位置信息编码
    • WO2013022656A3
    • 2014-03-13
    • PCT/US2012049055
    • 2012-07-31
    • QUALCOMM INCREZNIK YURIYHAMSICI ONUR CVADDADI SUNDEEPHONG JOHN HLEE CHONG U
    • REZNIK YURIYHAMSICI ONUR CVADDADI SUNDEEPHONG JOHN HLEE CHONG U
    • G06K9/46G06F17/18G06T9/00
    • G06T9/00G06F17/18G06F17/30247G06K9/4671G06K2009/226G06T9/005
    • 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.
    • 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。
    • 3. 发明申请
    • FEATURE MATCHING BY CLUSTERING DETECTED KEPOINTS IN QUERY AND MODEL IMAGES
    • 通过在查询和模型图像中聚类检测的KEPOIN的特征匹配
    • WO2011069021A3
    • 2011-08-18
    • PCT/US2010058805
    • 2010-12-02
    • QUALCOMM INCVADDADI SUNDEEPHONG JOHN HHAMSICI ONUR CREZNIK YURIYLEE CHONG U
    • VADDADI SUNDEEPHONG JOHN HHAMSICI ONUR CREZNIK YURIYLEE CHONG U
    • G06K9/64
    • G06K9/6211
    • 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.
    • 提供了一种图像识别中的特征匹配方法。 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地跟踪对象尺寸。 第二,不是使用图像中的所有检测到的特征来进行特征匹配,而是可以基于簇密度和/或检测关键点的比例级别来修剪关键点。 落入高密度簇内的关键点可能优于落入低密度簇内的特征,用于特征匹配。 第三,通过空间约束关键点进入群集来增加从早到晚的关键点比例,以便减少或避免图像的几何一致性检查。