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公开(公告)号：US20150036936A1
公开(公告)日：2015-02-05
申请号：US14370108
申请日：2012-12-20
Applicant: TELECOM ITALIA S.p.A.
Inventor： Massimo Balestri , Gianluca Francini , Skjalg Lepsoy
CPC classification number: G06K9/4671 , G06K9/4642 , G06K9/6202 , G06K9/6255 , G06K9/6256
Abstract: A method for processing an image including: identifying a first group of keypoints in the image; for each keypoint of the first group, identifying at least one corresponding keypoint local feature related to the each keypoint; for the at least one keypoint local feature, calculating a corresponding local feature relevance probability; calculating a keypoint relevance probability based on the local feature relevance probabilities of the at least one local feature; selecting keypoints, among the keypoints of the first group, having the highest keypoint relevance probabilities to form a second group of keypoints, and exploiting the keypoints of the second group for analyzing the image. The local feature relevance probability calculated for a local feature of a keypoint is obtained by comparing the value assumed by the local feature with a corresponding reference statistical distribution of values of the local feature.
Abstract translation: 一种用于处理图像的方法，包括：识别图像中的第一组关键点; 对于第一组的每个关键点，识别与每个关键点相关的至少一个相应的关键点局部特征; 对于所述至少一个关键点局部特征，计算相应的局部特征相关概率; 基于所述至少一个局部特征的所述局部特征相关性概率来计算关键点相关概率; 在第一组的关键点中选择具有最高关键点相关性概率的关键点，以形成第二组关键点，以及利用第二组的关键点来分析图像。 通过将局部特征假定的值与本地特征值的相应参考统计分布进行比较，获得关键点的局部特征所计算的局部特征相关概率。
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公开(公告)号：US12271812B2
公开(公告)日：2025-04-08
申请号：US17251508
申请日：2019-07-18
Applicant: TELECOM ITALIA S.p.A.
Inventor： Attilio Fiandrotti , Gianluca Francini , Skjalg Lepsoy , Enzo Tartaglione
Abstract: A method includes providing a neural network having a set of weights. The neural network receives an input data structure for generating a corresponding output array according to values of the set of weights. The neural network is trained to obtain a trained neural network. The training includes setting values of the set of weights with a gradient descent algorithm which exploits a cost function including a loss term and a regularization term. The trained neural network is deployed on a device through a communication network, and used by the device. The regularization term is based on a rate of change of elements of the output array caused by variations of the set of weights values.
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公开(公告)号：US10152646B2
公开(公告)日：2018-12-11
申请号：US14906218
申请日：2014-07-23
Applicant: TELECOM ITALIA S.p.A.
Inventor： Massimo Balestri , Gianluca Francini , Skjalg Lepsoy
Abstract: A method for identifying keypoints in a digital image including a set of pixels. Each pixel has associated thereto a respective value of an image representative parameter. The method includes approximating a filtered image. The filtered image depends on a filtering parameter and includes for each pixel of the image a filtering function that depends on the filtering parameter to calculate a filtered value of the value of the representative parameter of the pixel. The approximating includes: a) generating a set of base filtered images; each base filtered image is the image filtered with a respective value of the filtering parameter; b) for each pixel of at least a subset of the set of pixels, approximating the filtering function by a respective approximation function based on the base filtered images; the approximation function is a function of the filtering parameter within a predefined range of the filtering parameter.
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4.发明授权Method and system for image analysis based upon correlation relationships of sub-arrays of a descriptor array 有权基于描述符数组的子阵列的相关关系的图像分析方法和系统公开(公告)号：US09412037B2
公开(公告)日：2016-08-09
申请号：US14370133
申请日：2012-10-12
Applicant: TELECOM ITALIA S.p.A.
Inventor： Massimo Balestri , Gianluca Francini , Skjalg Lepsoy
CPC classification number: G06K9/4652 , G06F17/30247 , G06K9/00536 , G06K9/4642 , G06K9/6256
Abstract: A method for processing an image, including: identifying a group of keypoints in the image; for each keypoint, calculating a corresponding descriptor array including plural array elements, each array element storing values taken by a corresponding color gradient histogram of a respective sub-region of the image in the neighborhood of the keypoint; for each keypoint, subdividing the descriptor array in at least two sub-arrays each including a respective number of elements of the descriptor array, and generating a compressed descriptor array including a corresponding compressed sub-array for each of the at least two sub-arrays, each compressed sub-array obtained by compressing the corresponding sub-array by vector quantization using a respective codebook; exploiting the compressed descriptor arrays of the keypoints for image analysis. For each keypoint of the group, the subdividing is based on correlation relationships among color gradient histograms with values stored in the elements of the descriptor array of each keypoint.
Abstract translation: 一种用于处理图像的方法，包括：识别图像中的一组关键点; 对于每个关键点，计算包括多个数组元素的相应描述符阵列，每个数组元素存储由关键点附近的图像的相应子区域的对应颜色梯度直方图获取的值; 对于每个关键点，在至少两个子阵列中分割描述符阵列，每个子阵列包括描述符阵列的相应数量的元素，并且生成包括用于至少两个子阵列中的每一个的对应压缩子阵列的压缩描述符阵列 通过使用相应的码本通过矢量量化压缩相应子阵列而获得的每个压缩子阵列; 利用关键点的压缩描述符数组进行图像分析。 对于组的每个关键点，细分是基于存储在每个关键点的描述符阵列的元素中的值的色彩梯度直方图之间的相关关系。
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公开(公告)号：US09319690B2
公开(公告)日：2016-04-19
申请号：US14396525
申请日：2013-04-19
Applicant: TELECOM ITALIA S.p.A.
Inventor： Massimo Balestri , Gianluca Francini , Skjalg Lepsoy
IPC: G06K9/36 , H04N19/124 , G06T9/00
CPC classification number: H04N19/124 , G06T9/00
Abstract: A method for processing an image includes: identifying a group of keypoints in the image; for each keypoint of the group; a) calculating a corresponding descriptor array including a plurality of array elements, each array element storing values taken by a corresponding color gradient histogram of a respective sub-region of the image in the neighborhood of the keypoint; b) generating at least one compressed descriptor array by compressing at least one portion of the descriptor array by vector quantization using a codebook including a plurality of codewords.
Abstract translation: 一种用于处理图像的方法包括：识别图像中的一组关键点; 对于组的每个关键点; a）计算包括多个阵列元素的对应描述符阵列，每个阵列元素存储由所述关键点附近的图像的相应子区域的对应颜色梯度直方图获取的值; b）通过使用包括多个码字的码本的矢量量化来压缩描述符阵列的至少一部分来生成至少一个压缩描述符阵列。
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Patent Agency Ranking
公开(公告)号：US09269020B2
公开(公告)日：2016-02-23
申请号：US14370108
申请日：2012-12-20
Applicant: TELECOM ITALIA S.p.A.
Inventor： Massimo Balestri , Gianluca Francini , Skjalg Lepsoy
CPC classification number: G06K9/4671 , G06K9/4642 , G06K9/6202 , G06K9/6255 , G06K9/6256
Abstract: A method for processing an image including: identifying a first group of keypoints in the image; for each keypoint of the first group, identifying at least one corresponding keypoint local feature related to the each keypoint; for the at least one keypoint local feature, calculating a corresponding local feature relevance probability; calculating a keypoint relevance probability based on the local feature relevance probabilities of the at least one local feature; selecting keypoints, among the keypoints of the first group, having the highest keypoint relevance probabilities to form a second group of keypoints, and exploiting the keypoints of the second group for analyzing the image. The local feature relevance probability calculated for a local feature of a keypoint is obtained by comparing the value assumed by the local feature with a corresponding reference statistical distribution of values of the local feature.
Abstract translation: 一种用于处理图像的方法，包括：识别图像中的第一组关键点; 对于第一组的每个关键点，识别与每个关键点相关的至少一个相应的关键点局部特征; 对于所述至少一个关键点局部特征，计算相应的局部特征相关概率; 基于所述至少一个局部特征的所述局部特征相关性概率来计算关键点相关概率; 在第一组的关键点中选择具有最高关键点相关性概率的关键点，以形成第二组关键点，以及利用第二组的关键点来分析图像。 通过将局部特征假定的值与本地特征值的相应参考统计分布进行比较，获得关键点的局部特征所计算的局部特征相关概率。
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公开(公告)号：US20140363078A1
公开(公告)日：2014-12-11
申请号：US14370133
申请日：2012-10-12
Applicant: TELECOM ITALIA S.p.A.
Inventor： Massimo Balestri , Gianluca Francini , Skjalg Lepsoy
IPC: G06K9/46
CPC classification number: G06K9/4652 , G06F17/30247 , G06K9/00536 , G06K9/4642 , G06K9/6256
Abstract: A method for processing an image, including: identifying a group of keypoints in the image; for each keypoint, calculating a corresponding descriptor array including plural array elements, each array element storing values taken by a corresponding color gradient histogram of a respective sub-region of the image in the neighborhood of the keypoint; for each keypoint, subdividing the descriptor array in at least two sub-arrays each including a respective number of elements of the descriptor array, and generating a compressed descriptor array including a corresponding compressed sub-array for each of the at least two sub-arrays, each compressed sub-array obtained by compressing the corresponding sub-array by vector quantization using a respective codebook; exploiting the compressed descriptor arrays of the keypoints for image analysis. For each keypoint of the group, the subdividing is based on correlation relationships among color gradient histograms with values stored in the elements of the descriptor array of each keypoint.
Abstract translation: 一种用于处理图像的方法，包括：识别图像中的一组关键点; 对于每个关键点，计算包括多个数组元素的相应描述符阵列，每个数组元素存储由关键点附近的图像的相应子区域的对应颜色梯度直方图获取的值; 对于每个关键点，在至少两个子阵列中分割描述符阵列，每个子阵列包括描述符阵列的相应数量的元素，并且生成包括用于至少两个子阵列中的每一个的对应压缩子阵列的压缩描述符阵列 通过使用相应的码本通过矢量量化压缩相应子阵列而获得的每个压缩子阵列; 利用关键点的压缩描述符数组进行图像分析。 对于组的每个关键点，细分是基于存储在每个关键点的描述符阵列的元素中的值的色彩梯度直方图之间的相关关系。
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公开(公告)号：US11755913B2
公开(公告)日：2023-09-12
申请号：US16081693
申请日：2016-03-11
Applicant: TELECOM ITALIA S.p.A
Inventor： Gianluca Francini , Skjalg Lepsoy , Pedro Porto Buarque De Gusmao
IPC: G06N3/04 , G06N3/08 , G06V10/764 , G06V10/82 , G06K9/00 , G06N3/084 , G06F18/214 , G06F18/21 , G06F18/2413 , G06V10/44
CPC classification number: G06N3/084 , G06F18/214 , G06F18/217 , G06F18/2413 , G06N3/04 , G06N3/08 , G06V10/454 , G06V10/764 , G06V10/82
Abstract: A method in which a convolutional neural network is configured to receive an input data structure including a group of values corresponding to signal samples and to generate a corresponding classification output indicative of a selected one among plural predefined classes. The convolutional neural network includes an ordered sequence of layers, each configured to receive a corresponding layer input data structure including a group of input values, and generate a corresponding layer output data structure including a group of output values by convolving the layer input data structure with at least one corresponding filter including a corresponding group of weights. The layer input data structure of the first layer of the sequence corresponds to the input data structure. The layer input data structure of a generic layer of the sequence different from the first layer corresponds to the layer output data structure generated by a previous layer in the sequence.
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公开(公告)号：US09373056B2
公开(公告)日：2016-06-21
申请号：US14991556
申请日：2016-01-08
Applicant: TELECOM ITALIA S.p.A.
Inventor： Massimo Balestri , Gianluca Francini , Skjalg Lepsoy
CPC classification number: G06K9/4671 , G06K9/4642 , G06K9/6202 , G06K9/6255 , G06K9/6256
Abstract: A method for processing an image is proposed. The method comprises identifying a first group of keypoints in the image. For each keypoint of the first group, the method provides for identifying at least one corresponding keypoint local feature related to said each keypoint; for said at least one keypoint local feature, calculating a corresponding local feature relevance probability; calculating a keypoint relevance probability based on the local feature relevance probabilities of said at least one local feature. The method further comprises selecting keypoints, among the keypoints of the first group, having the highest keypoint relevance probabilities to form a second group of keypoints, and exploiting the keypoints of the second group for analyzing the image. The local feature relevance probability calculated for a local feature of a keypoint is obtained by comparing the value assumed by said local feature with a corresponding reference statistical distribution of values of said local feature.
Abstract translation: 提出了一种处理图像的方法。 该方法包括识别图像中的第一组关键点。 对于第一组的每个关键点，该方法提供用于识别与所述每个关键点相关的至少一个对应的关键点局部特征; 对于所述至少一个关键点局部特征，计算相应的局部特征相关概率; 基于所述至少一个局部特征的局部特征相关性概率来计算关键点相关概率。 该方法还包括：选择具有最高关键点相关性概率的第一组的关键点中的关键点，以形成第二组关键点，以及利用第二组的关键点来分析图像。 通过将所述局部特征所假设的值与所述局部特征的值的相应参考统计分布进行比较来获得针对关键点的局部特征计算的局部特征相关概率。
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公开(公告)号：US10354143B2
公开(公告)日：2019-07-16
申请号：US15516965
申请日：2014-10-13
Applicant: TELECOM ITALIA S.p.A.
Inventor： Skjalg Lepsoy , Massimo Balestri , Gianluca Francini
Abstract: A method (100) for comparing a first video shot (Vs1) comprising a first set of first images (I1(s)) with a second video shot (Vs2) comprising a second set of second images (I2(t)), at least one between the first and the second set comprising at least two images. The method comprises pairing (110) each first image of the first set with each second image of the second set to form a plurality of images pairs (IP(m)), and, for each image pair, carrying out the operations a)-g): a) identifying (120) first interest points in the first image and second interest points in the second image; b) associating (120) first interest points with corresponding second interest points in order to form corresponding interest point matches; c) for each pair of first interest points, calculating (130) the distance therebetween for obtaining a corresponding first length; d) for each pair of second interest points, calculating (130) the distance therebetween for obtaining a corresponding second length; e) calculating a plurality of distance ratios (130), each distance ratio corresponding to a selected pair of interest point matches and being based on a ratio of a first term and a second term or on a ratio of the second term and the first term, said first term corresponding to the distance between the first interest points of said pair of interest point matches and said second term corresponding to the distance between the second interest points of said pair of interest point matches; f) computing (140) a first representation of the statistical distribution of the plurality of calculated distance ratios; g) computing (150) a second representation of the statistical distribution of distance ratios obtained under the hypothesis that all the interest point matches in the image pair are outliers. The method further comprises generating (160) a first global representation of the statistical distribution of the plurality of calculated distance ratios computed for all the image pairs based on the first representations of all the image pairs; generating (170) a second global representation of the statistical distribution of distance ratios obtained under the hypothesis that all the interest point matches in all the image pairs are outliers based on the second representations of all the image pairs; comparing (180) said first global representation with said second global representation, and assessing (190) whether the first video shot contains a view of an object depicted in the second video shot based on said comparison.
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