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公开(公告)号：US20240020951A1
公开(公告)日：2024-01-18
申请号：US18352168
申请日：2023-07-13
IPC分类号： G06V10/764 , G06T7/00 , A01M7/00 , G06T7/73 , G06V20/10 , G06V10/20 , G06V10/44 , G06V20/20 , G06F18/2413
CPC分类号： G06V10/764 , G06T7/0002 , A01M7/0089 , G06T7/75 , G06V20/188 , G06V10/255 , G06V10/451 , G06V20/20 , G06F18/2414 , G06T2207/20081 , G06T2207/20084 , G06T2207/30188 , G06T2210/12 , G06T2207/20132 , G06T2207/10024
摘要： A plant treatment platform uses a plant detection model to detect plants as the plant treatment platform travels through a field. The plant treatment platform receives image data from a camera that captures images of plants (e.g., crops or weeds) growing in the field. The plant treatment platform applies pre-processing functions to the image data to prepare the image data for processing by the plant detection model. For example, the plant treatment platform may reformat the image data, adjust the resolution or aspect ratio, or crop the image data. The plant treatment platform applies the plant detection model to the pre-processed image data to generate bounding boxes for the plants. The plant treatment platform then can apply treatment to the plants based on the output of the machine-learned model.
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公开(公告)号：US11734573B2
公开(公告)日：2023-08-22
申请号：US17134924
申请日：2020-12-28
发明人： Tengfei Wu , Miaomiao Cui , Xuansong Xie
IPC分类号： G06N3/08 , G06N3/084 , G06F18/22 , G06F18/21 , G06V10/75 , G06V10/764 , G06V10/82 , G06V10/44 , G06N20/00
CPC分类号： G06N3/084 , G06F18/217 , G06F18/22 , G06V10/451 , G06V10/751 , G06V10/764 , G06V10/82 , G06N20/00
摘要： The present application discloses an image element matching method and apparatus, a model training method and apparatus, and a data processing method. The issue of finding image information matching a given image element is converted into the issue of predicting, from a matching knowledge graph, whether or not an edge is present between a node corresponding to the given image element and another node in the matching knowledge graph. Therefore, matching between image elements is flexibly implemented, matching performance is improved, and labor costs are reduced.
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公开(公告)号：US11893497B2
公开(公告)日：2024-02-06
申请号：US18127891
申请日：2023-03-29
发明人： Chang Kyu Choi , Youngjun Kwak , Seohyung Lee
IPC分类号： G06N3/00 , G06N3/084 , G06T5/00 , G06T5/20 , G06N3/08 , G06N20/00 , G06V10/764 , G06V10/82 , G06V10/44
CPC分类号： G06N3/084 , G06N3/08 , G06N20/00 , G06T5/001 , G06T5/20 , G06V10/451 , G06V10/764 , G06V10/82 , G06F2218/00 , G06T2207/10024 , G06T2207/20084
摘要： A processor-implemented method of generating feature data includes: receiving an input image; generating, based on a pixel value of the input image, at least one low-bit image having a number of bits per pixel lower than a number of bits per pixel of the input image; and generating, using at least one neural network, feature data corresponding to the input image from the at least one low-bit image.
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公开(公告)号：US11755709B2
公开(公告)日：2023-09-12
申请号：US17676797
申请日：2022-02-21
申请人： SHARECARE AI, INC.
发明人： Axel Sly , Srivatsa Akshay Sharma , Brett Robert Redinger , Devin Daniel Reich , Geert Trooskens , Meelis Lootus , Young Jin Lee , Ricardo Lopez Arredondo , Frederick Franklin Kautz, IV , Satish Srinivasan Bhat , Scott Michael Kirk , Walter Adolf De Brouwer , Kartik Thakore
IPC分类号： G06F21/32 , G06F21/45 , G06N20/00 , H04L9/32 , G16H10/60 , G06K7/14 , G06N5/04 , H04L9/08 , G06V40/70 , G06K19/06 , G06F18/214 , G06V10/74 , G06V10/77 , G06V10/80 , G06V10/44 , G06V40/16 , G06V10/774 , H04L9/40 , G06N3/08 , G06N3/04
CPC分类号： G06F21/32 , G06F18/214 , G06F21/45 , G06K7/1417 , G06K19/06037 , G06N5/04 , G06N20/00 , G06V10/451 , G06V10/761 , G06V10/774 , G06V10/7715 , G06V10/803 , G06V40/161 , G06V40/168 , G06V40/70 , G16H10/60 , H04L9/085 , H04L9/0841 , H04L9/0866 , H04L9/0894 , H04L9/3228 , H04L9/3231 , H04L9/3236 , H04L9/3239 , H04L9/3242 , H04L9/3247 , H04L9/3297 , G06N3/04 , G06N3/08 , H04L63/0861
摘要： The technology disclosed relates to authenticating users using a plurality of non-deterministic registration biometric inputs. During registration, a plurality of non-deterministic biometric inputs are given as input to a trained machine learning model to generate sets of feature vectors. The non-deterministic biometric inputs can include a plurality of face images and a plurality of voice samples of a user. A characteristic identity vector for the user can be determined by averaging feature vectors. During authentication, a plurality of non-deterministic biometric inputs are given as input to a trained machine learning model to generate a set of authentication feature vectors. The sets of feature vectors are projected onto a surface of a hyper-sphere. The system can authenticate the user when a cosine distance between the authentication feature vector and a characteristic identity vector for the user is less than a pre-determined threshold.
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5.发明授权公开(公告)号：US11741693B2
公开(公告)日：2023-08-29
申请号：US15826613
申请日：2017-11-29
IPC分类号： G06V10/82 , G06N3/08 , G06F18/2413 , G06V10/764 , G06V10/44
CPC分类号： G06V10/82 , G06F18/2413 , G06N3/08 , G06V10/451 , G06V10/764
摘要： One embodiment facilitates generating synthetic data objects using a semi-supervised GAN. During operation, a generator module synthesizes a data object derived from a noise vector and an attribute label. The system passes, to an unsupervised discriminator module, the data object and a set of training objects which are obtained from a training data set. The unsupervised discriminator module calculates: a value indicating a probability that the data object is real; and a latent feature representation of the data object. The system passes the latent feature representation and the attribute label to a supervised discriminator module. The supervised discriminator module calculates a value indicating a probability that the attribute label given the data object is real. The system performs the aforementioned steps iteratively until the generator module produces data objects with a given attribute label which the unsupervised and supervised discriminator modules can no longer identify as fake.
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公开(公告)号：US11748976B2
公开(公告)日：2023-09-05
申请号：US17378658
申请日：2021-07-17
IPC分类号： G06T7/00 , G06V10/764 , A01M7/00 , G06T7/73 , G06V20/10 , G06V10/20 , G06V10/44 , G06V20/20 , G06F18/2413
CPC分类号： G06V10/764 , A01M7/0089 , G06F18/2414 , G06T7/0002 , G06T7/75 , G06V10/255 , G06V10/451 , G06V20/188 , G06V20/20 , G06T2207/10024 , G06T2207/20081 , G06T2207/20084 , G06T2207/20132 , G06T2207/30188 , G06T2210/12
摘要： A plant treatment platform uses a plant detection model to detect plants as the plant treatment platform travels through a field. The plant treatment platform receives image data from a camera that captures images of plants (e.g., crops or weeds) growing in the field. The plant treatment platform applies pre-processing functions to the image data to prepare the image data for processing by the plant detection model. For example, the plant treatment platform may reformat the image data, adjust the resolution or aspect ratio, or crop the image data. The plant treatment platform applies the plant detection model to the pre-processed image data to generate bounding boxes for the plants. The plant treatment platform then can apply treatment to the plants based on the output of the machine-learned model.
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公开(公告)号：US20230252296A1
公开(公告)日：2023-08-10
申请号：US18300851
申请日：2023-04-14
申请人： Numenta,Inc.
CPC分类号： G06N3/08 , G06N5/04 , G06V20/80 , G06V10/95 , G06V10/255 , G06V10/451 , G06V10/454 , G06F18/22 , G06N3/045
摘要： An inference system performs inference, such as object recognition, based on sensory inputs generated by sensors and control information associated with the sensory inputs. The sensory inputs describe one or more features of the objects. The control information describes movement of the sensors or known locations of the sensors relative to a reference point. For a particular object, an inference system learns a set of object-location representations of the object. An object-location representation is a unique characterization of an object-centric location relative to the particular object. The inference system also learns a set of feature-location representations associated with the object-location representation that indicate presence of features at the corresponding object-location pair. The inference system can perform inference on an unknown object by identifying candidate object-location representations consistent with feature-location representations observed from the sensory input data and control information.
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公开(公告)号：US20230154181A1
公开(公告)日：2023-05-18
申请号：US18092689
申请日：2023-01-03
申请人： Cape Analytics, Inc.
发明人： Peter Lorenzen
IPC分类号： G06V20/10 , G06V10/44 , G06F18/2413 , G06V10/764 , G06V10/82
CPC分类号： G06V20/176 , G06V10/451 , G06F18/2413 , G06V10/764 , G06V10/82 , H04N23/11
摘要： Disclosed systems and methods relate to remote sensing, deep learning, and object detection. Some embodiments relate to machine learning for object detection, which includes, for example, identifying a class of pixel in a target image and generating a label image based on a parameter set. Other embodiments relate to machine learning for geometry extraction, which includes, for example, determining heights of one or more regions in a target image and determining a geometric object property in a target image. Yet other embodiments relate to machine learning for alignment, which includes, for example, aligning images via direct or indirect estimation of transformation parameters.
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公开(公告)号：US20240249518A1
公开(公告)日：2024-07-25
申请号：US18628845
申请日：2024-04-08
申请人： Cape Analytics, Inc.
发明人： Peter Lorenzen
IPC分类号： G06V20/10 , G06F18/2413 , G06V10/44 , G06V10/764 , G06V10/82 , H04N23/11
CPC分类号： G06V20/176 , G06F18/2413 , G06V10/451 , G06V10/764 , G06V10/82 , H04N23/11
摘要： Disclosed systems and methods relate to remote sensing, deep learning, and object detection. Some embodiments relate to machine learning for object detection, which includes, for example, identifying a class of pixel in a target image and generating a label image based on a parameter set. Other embodiments relate to machine learning for geometry extraction, which includes, for example, determining heights of one or more regions in a target image and determining a geometric object property in a target image. Yet other embodiments relate to machine learning for alignment, which includes, for example, aligning images via direct or indirect estimation of transformation parameters.
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10.发明授权公开(公告)号：US11893514B2
公开(公告)日：2024-02-06
申请号：US17857921
申请日：2022-07-05
申请人： Revealit Corporation
发明人： Garry Anthony Smith , Naomi Felina Moneypenny , Zachary Oakes , Steven Dennis Flinn , Michael George Renie
IPC分类号： G06F40/211 , G06F40/30 , G06V20/40 , G06N5/048 , G06F18/21 , G06V10/764 , G06V10/82 , G06V10/44 , G06V40/20
CPC分类号： G06N5/048 , G06F18/2178 , G06F40/211 , G06F40/30 , G06V10/451 , G06V10/764 , G06V10/82 , G06V20/40 , G06V40/20
摘要： A contextual-based method and system for identifying and revealing objects from video directs a focus of attention to images or sequences of images responsive to a command, interrogative, or inferred preference of a user. A probability is assigned to an object that is inferred to be represented in the images or sequence of images. The probability is generated by application of a computer-implemented neural network. The probability is then updated based upon a context within which the representation of the object is inferred to be situated. The context may be inferred from one or more inferences related to one or more archetypical objects that are associated with the context. In accordance with the updated probability, a communication may be delivered that references attributes of the object and/or the user may direct a command to the representation of the object.
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