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公开(公告)号：US20240242361A1
公开(公告)日：2024-07-18
申请号：US18156061
申请日：2023-01-18
申请人： INUITIVE LTD.
发明人： Edward ELIKHIS , Dor PERETZ
摘要： A computational platform and a method for use in a process of matching pairs of pixels, wherein each of the members of a pixel's pair belong to another image captured by a different image capturing sensor, and wherein the computational platform comprises at least one processor configured to carry out a process of matching pairs of pixels based on selecting a pixel mask to be used by selecting neighboring pixels of a given pixel from among all of its neighboring pixels, will be used for the matching process of said given pixel with the other member of its pixel's pair.
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公开(公告)号：US20240242363A1
公开(公告)日：2024-07-18
申请号：US18156145
申请日：2023-01-18
申请人： INUITIVE LTD.
发明人： Edward ELIKHIS , Dor PERETZ
CPC分类号： G06T7/50 , G06V10/25 , G06V10/761
摘要： A computational platform and a method for use in a depth calculation process based on information comprised in an image captured by one or more image capturing sensors, wherein the computational platform enables distinguishing between areas included in the captured image that comprise details that are implementable by a matching algorithm and areas that do not have such details, wherein the computational platform comprises at least one processor, configured to select at least one matching window comprised in the captured image for matching a corresponding part included in each image captured by the one or more image capturing devices; calculate a metric based on a respective selected matching window; and calculate a depth map based on the calculated metric associated with the at least one matching window.
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3.发明公开公开(公告)号：US20230401746A1
公开(公告)日：2023-12-14
申请号：US17835399
申请日：2022-06-08
申请人： INUITIVE LTD.
发明人： Dor PERETZ , Edward ELIKHIS
CPC分类号： G06T7/80 , G06T5/009 , G06T2207/20084
摘要： A method and a computational module are provided for carrying out a quantization process of a plurality of channels carrying data received from an image capturing sensor. The computational module comprises: at least one array of processors, configured to a) retrieve data from: a1) a neural network graph, a2) a dataset associated with a data and a3) parameters' values of a neural network model; b) carry out a dynamic range calibration process for the channels received and using the neural network graph for deriving grouping constrains associated with respective channels; c) carry out a grouping optimization based on results obtained for each channel from its respective dynamic range calibration and its grouping constrains; d) arrange the channels so that channels having similar grouping constrains are grouped together into one output channel; and e) calculate required quantization parameters for carrying out a quantization process of the output channels.
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