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Patent Agency Ranking
公开(公告)号：US20180260688A1
公开(公告)日：2018-09-13
申请号：US15976983
申请日：2018-05-11
Applicant: Deep Learning Analytics, LLC
Inventor： John Patrick KAUFHOLD
CPC classification number: G06N3/0454 , G01S7/417 , G01S13/90 , G01S13/904
Abstract: The present invention is directed to systems and methods for detecting objects in a radar image stream. Embodiments of the invention can receive a data stream from radar sensors and use a deep neural network to convert the received data stream into a set of semantic labels, where each semantic label corresponds to an object in the radar data stream that the deep neural network has identified. Processing units running the deep neural network may be collocated onboard an airborne vehicle along with the radar sensor(s). The processing units can be configured with powerful, high-speed graphics processing units or field-programmable gate arrays that are low in size, weight, and power requirements. Embodiments of the invention are also directed to providing innovative advances to object recognition training systems that utilize a detector and an object recognition cascade to analyze radar image streams in real time. The object recognition cascade can comprise at least one recognizer that receives a non-background stream of image patches from a detector and automatically assigns one or more semantic labels to each non-background image patch. In some embodiments, a separate recognizer for the background analysis of patches may also be incorporated. There may be multiple detectors and multiple recognizers, depending on the design of the cascade. Embodiments of the invention also include novel methods to tailor deep neural network algorithms to successfully process radar imagery, utilizing techniques such as normalization, sampling, data augmentation, foveation, cascade architectures, and label harmonization.
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公开(公告)号：US09978013B2
公开(公告)日：2018-05-22
申请号：US14794376
申请日：2015-07-08
Applicant: Deep Learning Analytics, LLC
Inventor： John Patrick Kaufhold
CPC classification number: G06N3/0454 , G01S7/417 , G01S13/90 , G01S13/9035
Abstract: The present invention is directed to systems and methods for detecting objects in a radar image stream. Embodiments of the invention can receive a data stream from radar sensors and use a deep neural network to convert the received data stream into a set of semantic labels, where each semantic label corresponds to an object in the radar data stream that the deep neural network has identified. Processing units running the deep neural network may be collocated onboard an airborne vehicle along with the radar sensor(s). The processing units can be configured with powerful, high-speed graphics processing units or field-programmable gate arrays that are low in size, weight, and power requirements. Embodiments of the invention are also directed to providing innovative advances to object recognition training systems that utilize a detector and an object recognition cascade to analyze radar image streams in real time. The object recognition cascade can comprise at least one recognizer that receives a non-background stream of image patches from a detector and automatically assigns one or more semantic labels to each non-background image patch. In some embodiments, a separate recognizer for the background analysis of patches may also be incorporated. There may be multiple detectors and multiple recognizers, depending on the design of the cascade. Embodiments of the invention also include novel methods to tailor deep neural network algorithms to successfully process radar imagery, utilizing techniques such as normalization, sampling, data augmentation, foveation, cascade architectures, and label harmonization.
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公开(公告)号：US20190080205A1
公开(公告)日：2019-03-14
申请号：US15705504
申请日：2017-09-15
Applicant: Deep Learning Analytics, LLC
Inventor： John Patrick KAUFHOLD , Jennifer Sleeman
Abstract: Embodiments of the present invention relate to systems and methods for improving the training of machine learning systems to recognize certain objects within a given image by supplementing an existing sparse set of real-world training images with a comparatively dense set of realistic training images. Embodiments may create such a dense set of realistic training images by training a machine learning translator with a convolutional autoencoder to translate a dense set of synthetic images of an object into more realistic training images. Embodiments may also create a dense set of realistic training images by training a generative adversarial network (“GAN”) to create realistic training images from a combination of the existing sparse set of real-world training images and either Gaussian noise, translated images, or synthetic images. The created dense set of realistic training images may then be used to more effectively train a machine learning object recognizer to recognize a target object in a newly presented digital image.
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公开(公告)号：US20160019458A1
公开(公告)日：2016-01-21
申请号：US14794376
申请日：2015-07-08
Applicant: Deep Learning Analytics, LLC
Inventor： John Patrick KAUFHOLD
CPC classification number: G06N3/0454 , G01S7/417 , G01S13/90 , G01S13/9035
Abstract: The present invention is directed to systems and methods for detecting objects in a radar image stream. Embodiments of the invention can receive a data stream from radar sensors and use a deep neural network to convert the received data stream into a set of semantic labels, where each semantic label corresponds to an object in the radar data stream that the deep neural network has identified. Processing units running the deep neural network may be collocated onboard an airborne vehicle along with the radar sensor(s). The processing units can be configured with powerful, high-speed graphics processing units or field-programmable gate arrays that are low in size, weight, and power requirements. Embodiments of the invention are also directed to providing innovative advances to object recognition training systems that utilize a detector and an object recognition cascade to analyze radar image streams in real time. The object recognition cascade can comprise at least one recognizer that receives a non-background stream of image patches from a detector and automatically assigns one or more semantic labels to each non-background image patch. In some embodiments, a separate recognizer for the background analysis of patches may also be incorporated. There may be multiple detectors and multiple recognizers, depending on the design of the cascade. Embodiments of the invention also include novel methods to tailor deep neural network algorithms to successfully process radar imagery, utilizing techniques such as normalization, sampling, data augmentation, foveation, cascade architectures, and label harmonization.
Abstract translation: 本发明涉及用于检测雷达图像流中物体的系统和方法。 本发明的实施例可以从雷达传感器接收数据流，并使用深层神经网络将接收的数据流转换成一组语义标签，其中每个语义标签对应于雷达数据流中的一个对象，深层神经网络具有 确定。 运行深层神经网络的处理单元可以与雷达传感器一起配置在机载车辆上。 处理单元可配置强大的高速图形处理单元或现场可编程门阵列，其尺寸，重量和功率要求较低。 本发明的实施例还涉及为利用检测器和物体识别级联实时分析雷达图像流的对象识别训练系统提供创新的进步。 对象识别级联可以包括至少一个识别器，其从检测器接收非背景图像块流，并且自动地将一个或多个语义标签分配给每个非背景图像块。 在一些实施例中，还可以并入用于斑块背景分析的单独识别器。 根据级联的设计，可能有多个检测器和多个识别器。 本发明的实施例还包括定制深层神经网络算法以成功处理雷达图像的新方法，利用诸如归一化，采样，数据增加，移动，级联架构和标签协调之类的技术。
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公开(公告)号：US09990687B1
公开(公告)日：2018-06-05
申请号：US15454845
申请日：2017-03-09
Applicant: Deep Learning Analytics, LLC
Inventor： John Patrick Kaufhold , Michael Jeremy Trammell
CPC classification number: G06T1/005 , G06K9/00 , G06K9/4628 , G06K9/6247 , G06K9/6256 , G06K9/627 , G06N3/0454 , G06N3/082 , G06N3/084 , H04N1/32154 , H04N1/32267
Abstract: Embodiments of the present invention are directed to providing new systems and methods for using deep learning techniques to generate embeddings for high dimensional data objects that can both simulate prior art embedding algorithms and also provide superior performance compared to the prior art methods. Deep learning techniques used by embodiments of the present invention to embed high dimensional data objects may comprise the following steps: (1) generating an initial formal embedding of selected high-dimensional data objects using any of the traditional formal embedding techniques; (2a) designing a deep embedding architecture, which includes choosing the types and numbers of inputs and outputs, types and number of layers, types of units/nonlinearities, and types of pooling, for example, among other design choices, typically in a convolutional neural network; (2b) designing a training strategy; (2c) tuning the parameters of a deep embedding architecture to reproduce, as reliably as possible, the generated embedding for each training sample; (3) optionally deploying the trained deep embedding architecture to convert new high dimensional data objects into approximately the same embedded space as found in step (1); and optionally (4) feeding the computed embeddings of high dimensional objects to an application in a deployed embodiment.
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