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    • 2. 发明授权
    • Audio test apparatus and test method thereof
    • 音频测试装置及其测试方法
    • US08073146B2
    • 2011-12-06
    • US12168916
    • 2008-07-08
    • Hua-Dong ChengWen-Chuan LianYao ZhaoJian SunPeng ChenWen-Sheng Tsai
    • Hua-Dong ChengWen-Chuan LianYao ZhaoJian SunPeng ChenWen-Sheng Tsai
    • H04R29/00
    • H04R29/001
    • An audio test apparatus, and an exemplary audio test method that includes: processing an audio file through two independent channels; outputting no signals from a left channel and from a right channel in a first time period; receiving noise signals from the left and right channels; outputting single-frequency signals from the left channel only in a second time period; receiving the single-frequency signals from the left channel and crosstalk signals from the right channel; outputting multi-frequency signals from the left and right channels in a third time period; receiving the multi-frequency signals from the left and right channels; outputting single-frequency signals from the right channel only in a fourth time period; receiving the crosstalk signals from the left channel and the single-frequency signals from the right channel; and testing parameters according to the signals received during the four time periods.
    • 音频测试装置和示例性音频测试方法,其包括:通过两个独立信道处理音频文件; 在第一时间段内不从左声道和右声道输出信号; 从左右声道接收噪声信号; 仅在第二时间段内从左声道输出单频信号; 从左声道接收单频信号和来自右声道的串扰信号; 在第三时间段内从左右声道输出多频信号; 从左右声道接收多频信号; 仅在第四时间段内从右声道输出单频信号; 接收来自左声道的串扰信号和来自右声道的单频信号; 并根据在四个时间段期间接收的信号测试参数。
    • 6. 发明授权
    • Visual-cue refinement of user query results
    • 用户查询结果的视觉提示细化
    • US09355179B2
    • 2016-05-31
    • US12890097
    • 2010-09-24
    • Yu-Ting KuoYi LiFang WenQifa KeJian Sun
    • Yu-Ting KuoYi LiFang WenQifa KeJian Sun
    • G06F17/30
    • G06F17/30867G06F17/30651G06F17/30696
    • Methods and computer-storage media having computer-executable instructions embodied thereon that facilitate refining query results using visual cues are provided. Query results are determined in response to an indication of a user query. One or more groups of query results are generated from the query results based on categories of query results that share similar features. Visual cues are associated with each of the query result groups. Visual cues, in association with query result groups, are presented to a user. Query results associated with a selected visual cue may be presented to a user. A refined user query may be generated based on a selected visual cue.
    • 提供了具有实现在其上的计算机可执行指令的方法和计算机存储介质,其使用视觉提示便于精简查询结果。 响应于用户查询的指示来确定查询结果。 基于共享类似特征的查询结果的类别,从查询结果生成一组或多组查询结果。 视觉线索与每个查询结果组相关联。 与查询结果组相关联的视觉提示被呈现给用户。 与所选择的视觉提示相关联的查询结果可以被呈现给用户。 可以基于所选择的视觉提示来生成精细的用户查询。
    • 8. 发明授权
    • Image super-resolution using gradient profile prior
    • 使用梯度轮廓的图像超分辨率
    • US09064476B2
    • 2015-06-23
    • US12245712
    • 2008-10-04
    • Jian SunHeung-Yeung Shum
    • Jian SunHeung-Yeung Shum
    • G06K9/32G09G5/391G06T3/40
    • G09G5/391G06T3/4053G09G2340/0407
    • Described is a technology by which a low-resolution image is processed into a high-resolution image, including by performing processing in the gradient domain. A gradient profile corresponding to the lower-resolution image is transform into a sharpened image gradient. A high-resolution gradient profile is estimated from a low-resolution gradient profile, e.g., by multiplying the low-resolution gradient profile by a transform ratio that is based upon learned shape parameters, learned sharpness values and a curve distance to an edge pixel along the gradient profile. The transform ratio is used to transform a low-resolution gradient field to a high-resolution gradient field. Reconstructing the higher-resolution image is performed by using the high-resolution gradient field as a gradient domain constraint, e.g., in along with a reconstruction constraint obtained from image domain data. An energy function is minimized by enforcing the gradient domain constraint and the reconstruction constraint, e.g., by performing a gradient descent algorithm.
    • 描述了将低分辨率图像处理成高分辨率图像的技术,包括通过在梯度域中执行处理。 对应于较低分辨率图像的渐变曲线被转换成锐化的图像梯度。 从低分辨率梯度轮廓估计高分辨率梯度轮廓,例如,通过将低分辨率梯度轮廓乘以基于学习的形状参数,学习的锐度值和到边缘像素的曲线距离的变换比, 梯度轮廓。 变换比用于将低分辨率梯度场转换为高分辨率梯度场。 通过使用高分辨率梯度场作为梯度域约束,例如与从图像域数据获得的重建约束一起,进行重建高分辨率图像。 通过执行梯度域约束和重构约束,例如通过执行梯度下降算法来最小化能量函数。