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    • 21. 发明申请
    • IMAGE RECOGNITION USING DESCRIPTOR PRUNING
    • 使用描述符预处理的图像识别
    • WO2016037848A1
    • 2016-03-17
    • PCT/EP2015/069452
    • 2015-08-25
    • THOMSON LICENSING
    • ZEPEDA SALVATIERRA, JoaquinPEREZ, PatrickRANA, Aakanksha
    • G06K9/46G06F17/30
    • G06T5/20G06K9/4676G06T2207/20081
    • The present disclosure relates to image recognition or image searching. More precisely, the present disclosure relates to pruning local descriptors extracted from an input image. The present disclosure proposes a system, method and device directed to the pruning of local descriptors extracted from image patches of an input image. The present disclosure prunes local descriptors assigned to a codebook cell, based on a relationship of the local descriptor and the assigned codebook cell. The present disclosure includes assigning a weight value for use in pruning based on the relationship of the local descriptor and the assigned codebook cell. This weight value is then used during the encoding of the local descriptors for use in image searching or image recognition.
    • 本公开涉及图像识别或图像搜索。 更准确地说,本公开涉及从输入图像中提取的修剪局部描述符。 本公开提出了一种针对从输入图像的图像块提取的局部描述符的修剪的系统,方法和装置。 本发明基于本地描述符和分配的码本单元的关系来修剪分配给码本单元的本地描述符。 本公开包括基于本地描述符和分配的码本小区的关系来分配用于修剪的权重值。 然后在本地描述符的编码期间使用该权重值用于图像搜索或图像识别。
    • 24. 发明申请
    • DEVICES AND METHODS FOR LEARNING AND APPLYING A DISTANCE METRIC
    • 用于学习和应用距离公制的装置和方法
    • WO2015132016A1
    • 2015-09-11
    • PCT/EP2015/051169
    • 2015-01-21
    • THOMSON LICENSING
    • SHARMA, GauravPEREZ, Patrick
    • G06K9/00G06K9/62
    • G06K9/00288G06K9/6256
    • A distance metric (63) is learned with a training set (61) including training images and pairwise information indicating whether subsets of those images are pairwise corresponding to same objects. The distance metric is associated (41) with adjustable projection spaces, adapted to projecting the training images into those projection spaces and to computing related local distances. The projection spaces are selected (42) in function of pairs of the subsets of training images, the distance metric corresponding to the selected spaces. Adjusted projection spaces are accordingly determined (43, 44) by reducing offsets between expected values based on the pairwise information and effective values, of the distance metric with respect to the objects for the training images. A distance metric is also applied to study images, thereby deciding whether those images correspond pairwise to same objects. Those are relevant to face verification and to identity based clustering of faces.
    • 使用包括训练图像的训练集(61)和指示这些图像的子集是否对应于相同对象的成对信息来学习距离度量(63)。 距离度量与可调投影空间相关联(41),适于将训练图像投影到这些投影空间中并计算相关的局部距离。 在训练图像的子集对中,对应于所选择的空间的距离度量,投影空间被选择(42)。 因此,通过基于相对于训练图像的对象的距离度量的成对信息和有效值来减小预期值之间的偏移,从而确定调整的投影空间(43,44)。 还应用距离度量来研究图像,从而决定这些图像是否与成对对象成对对应。 这些与面部验证和基于身份的面孔聚类相关。
    • 28. 发明申请
    • METHOD AND DEVICE FOR REFOCUSING AT LEAST ONE PLENOPTIC VIDEO
    • 至少一个普通视频重新聚焦的方法和设备
    • WO2017102549A1
    • 2017-06-22
    • PCT/EP2016/080285
    • 2016-12-08
    • THOMSON LICENSING
    • HELLIER, PierreALLIE, ValériePEREZ, Patrick
    • G06T5/00G06T5/50
    • G06T5/003G06T5/50G06T2200/21G06T2207/10052
    • A method for refocusing, on at least one common point of interest, the rendering of one set of plenoptic video data provided by one plenoptic device belonging to a set of plenoptic devices capturing simultaneously a same scene. According to the present disclosure, said method comprises: obtaining (21) a common 3D reference system used for spatially locating said plenoptic device that has provided said set of plenoptic video data and at least one other device of said set of plenoptic devices, from said at least one common point of interest, determining (22) common refocusing plane parameters in said common 3D reference system, refocusing (23) the rendering of said set of plenoptic video data by converting (231) said common refocusing plane parameters into a rendering refocusing plane of a 3D reference system associated with said plenoptic device.
    • 一种用于在至少一个公共兴趣点上重新聚焦由属于同时捕捉相同场景的一组全景设备的一个全光设备提供的一组全景视频数据的方法。 根据本公开内容,所述方法包括:从所述集合中获得(21)用于在空间上定位已经提供所述一组全光视频数据的所述全光设备和所述一组全域设备中的至少一个其他设备的公共3D参考系统 至少一个公共重点,在所述公共3D参考系统中确定(22)共同重聚焦平面参数,通过将所述共同重聚焦平面参数转换(231)为渲染重聚焦,重新聚焦(23)所述全光视频数据组的渲染 与所述全光设备相关联的3D参考系统的平面。
    • 29. 发明申请
    • METHOD AND APPARATUS FOR GENERATING CODEBOOKS FOR EFFICIENT SEARCH
    • 用于生成有效搜索的代码簿的方法和设备
    • WO2017077076A1
    • 2017-05-11
    • PCT/EP2016/076734
    • 2016-11-04
    • THOMSON LICENSING
    • JAIN, HimalayaBILEN, CagdasZEPEDA, Salvatierra JoaquinPEREZ, Patrick
    • G06F17/30
    • G06F17/30277G06F17/30247
    • In a particular implementation, a codebook C can be used for quantizing a feature vector of a database image into a quantization index, and then a different codebook ( B ) can be used to approximate the feature vector based on the quantization index. The codebooks B and C can have different sizes. Before performing image search, a lookup table can be built offline to include distances between the feature vector for a query image and codevectors in codebook B to speed up the image search. Using triplet constraints wherein a first image and a second image are indicated as a matching pair and the first image and a third image as non-matching, the codebooks B and C can be trained for the task of image search. The present principles can be applied to regular vector quantization, product quantization, and residual quantization.
    • 在特定的实现中,可以使用码本C来将数据库图像的特征向量量化为量化索引,然后使用不同的码本(B )可以用来近似基于量化索引的特征向量。 码本 可以有不同的大小。 在执行图像搜索之前,可以离线构建查找表以包括查询图像的特征向量与码本中的代码向量之间的距离,以加速图像搜索。 使用三重约束,其中第一图像和第二图像被指示为匹配对并且第一图像和第三图像不匹配,码本B和C可以 接受图像搜索任务的培训。 本原理可应用于常规矢量量化,产品量化和残余量化。