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1. 发明授权

US11317870B1 System and method for health assessment on smartphones 有权
公开(公告)号：US11317870B1
公开(公告)日：2022-05-03
申请号：US16267376
申请日：2019-02-04
申请人： HRL Laboratories, LLC
发明人： Vincent De Sapio , Jaehoon Choe , Iman Mohammadrezazadeh , Kang-Yu Ni , Heiko Hoffmann , Charles E. Martin , Yuri Owechko
IPC分类号： A61B5/00 , A61B5/11
摘要： Described is a system for health assessment. The system is implemented on a mobile device having at least one of an accelerometer, a geographic location sensor, and a camera. In operation, the system obtains sensor data related to an operator of the mobile device from one of the sensors. A network of networks (NoN) is generated based on the sensor data, the NoN having a plurality of layers with linked nodes. Tuples are thereafter generated. Each tuple contains a node from each layer that optimizes importance, diversity, and coherence. Storylines are created based on the tuples that solves a longest path problem for each tuple. The storylines track multiple symptom progressions of the operator. Finally, a disease prediction of the operator is provided based on the storylines.

2. 发明授权

US10198790B1 Multi-domain foveated compressive sensing system for adaptive imaging 有权
公开(公告)号：US10198790B1
公开(公告)日：2019-02-05
申请号：US15213228
申请日：2016-07-18
申请人： HRL Laboratories, LLC
发明人： Yuri Owechko , Daniel Yap , Oleg M. Efimov
IPC分类号： G06T3/40 , G02B17/00 , G02B26/08 , G02B27/10 , G06T1/00 , H04N5/262 , G06T7/00
摘要： Described in this disclosure is a space-variant Multi-domain Foveated Compressive Sensing (MFCS) system for adaptive imaging with variable resolution in spatial, polarization, and spectral domains simultaneously and with very low latency between multiple adaptable regions of interest (ROIs) across the field of view (FOV). The MFCS system combines space-variant foveated compressive sensing (FCS) imaging covered by a previous disclosure with a unique dual-path high efficiency optical architecture for parallel multi-domain compressive sensing (CS) processing. A single programmable Digital Micromirror Device (DMD) micro-mirror array is used at the input aperture to adaptively define and vary the resolution of multiple variable-sized ROIs across the FOV, encode the light for CS reconstruction, and adaptively divide the input light among multiple optical paths using complementary measurement codes, which can then be reconstructed as desired.

3. 发明授权

US09544550B1 Low power surveillance camera system for intruder detection 有权
标题翻译：用于入侵者检测的低功率监控摄像系统
公开(公告)号：US09544550B1
公开(公告)日：2017-01-10
申请号：US14209136
申请日：2014-03-13
申请人： HRL Laboratories, LLC
发明人： Kang-Yu Ni , Shankar R. Rao , Yuri Owechko
IPC分类号： H04N7/18
CPC分类号： G06K9/6249 , G06K9/00771 , G08B13/19604
摘要： Described is a low power surveillance camera system for intruder detection. The system observes a scene with a known camera motion to generate images with various viewing angles. Next, a background learning mode is employed to generate a low rank matrix for the background in the images. Background null space projections are then learned, which provide a foreground detection kernel. A new scene with known viewing angles is then obtained. Based on the foreground detection kernel and the new input image frame, low power foreground detection is performed to detect foreground potential regions of interest (ROIs), such as intruders. To filter out minimal foreground activity, the system identifies contiguous ROIs to generate the foreground ROI. Focus measures are then employed on the ROIs using foveated compressed sensing to generate foveated measurements. Based on the foveated measurements, the foreground is reconstructed for presentation to a user.
摘要翻译：描述了一种用于入侵者检测的低功率监控摄像机系统。该系统观察具有已知摄像机运动的场景以产生具有各种视角的图像。接下来，使用背景学习模式来生成图像中的背景的低秩矩阵。然后学习背景零空间投影，其提供前景检测内核。然后获得具有已知视角的新场景。基于前景检测核心和新的输入图像帧，执行低功率前景检测，以检测诸如入侵者的感兴趣的前景潜在区域（ROI）。为了过滤掉最小的前景活动，系统识别连续的ROI以生成前景ROI。然后，利用移动压缩感测对ROI进行聚焦，以产生移动测量。基于移动测量，重建前景以呈现给用户。

4. 发明授权

US09286538B1 Adaptive 3D to 2D projection for different height slices and extraction of robust morphological features for 3D object recognition 有权
标题翻译：针对不同高度切片的自适应3D到2D投影，并提取用于3D对象识别的鲁棒形态特征
公开(公告)号：US09286538B1
公开(公告)日：2016-03-15
申请号：US14522180
申请日：2014-10-23
申请人： HRL Laboratories, LLC
发明人： Hai-Wen Chen , Yuri Owechko
IPC分类号： G06K9/46 , G06T11/00 , G06T7/00
CPC分类号： G06T11/003 , G06K9/00208
摘要： Described is a system for adaptive three-dimensional (3D) to two-dimensional (2D) projection for different height slices and extraction of morphological features. Initially, the system receives 3D point cloud data. Next, an image pixel number to point cloud number ratio is selected. Thereafter, an image row and image column are selected to identify desired height slices. The 3D point cloud is then accumulated on the desired height slices to generate a plurality of 2D height slices.
摘要翻译：描述了用于不同高度切片的自适应三维（3D）到二维（2D）投影的系统，并提取形态特征。最初，系统接收3D点云数据。接下来，选择图像像素数到点云数比。此后，选择图像行和图像列以识别期望的高度切片。然后将3D点云积累在期望的高度切片上以产生多个2D高度切片。

5. 发明授权

US10592788B2 Zero-shot learning using multi-scale manifold alignment 有权
公开(公告)号：US10592788B2
公开(公告)日：2020-03-17
申请号：US15847895
申请日：2017-12-19
申请人： HRL Laboratories, LLC
发明人： Shay Deutsch , Kyungnam Kim , Yuri Owechko
IPC分类号： G06K9/62 , G06K9/72
摘要： Described is a system for recognition of unseen and untrained patterns. A graph is generated based on visual features from input data, the input data including labeled instances and unseen instances. Semantic representations of the input data are assigned as graph signals based on the visual features. The semantic representations are aligned with visual representations of the input data using a regularization method applied directly in a spectral graph wavelets (SGW) domain. The semantic representations are then used to generate labels for the unseen instances. The unseen instances may represent unknown conditions for an autonomous vehicle.

6. 发明授权

US10268914B1 Blind sensing for hyperspectral surveillance 有权
公开(公告)号：US10268914B1
公开(公告)日：2019-04-23
申请号：US15402159
申请日：2017-01-09
申请人： HRL Laboratories, LLC
发明人： Yuri Owechko
IPC分类号： G06K9/00 , G06K9/20 , G01J3/28 , H04N5/232 , G06K9/32 , G06K9/46 , G06K9/62
摘要： Described is a blind sensing system for hyperspectral surveillance. During operation, hyperspectral data is captured using a hyperspectral camera as mounted on a mobile platform. The system then forms a signal mixture of a plurality of multi-dimensional signals. The multi-dimensional signals being the captured hyperspectral data of a wide area having a background and an object. The plurality of multi-dimensional signals are then demixed using blind source separation, resulting in separated spectra. Finally, the system detects and recognizes a spectral signature of the object in the separated spectra.

7. 发明申请

US20190080210A1 INDEPENDENT COMPONENT ANALYSIS OF TENSORS FOR SENSOR DATA FUSION AND RECONSTRUCTION 审中-公开
公开(公告)号：US20190080210A1
公开(公告)日：2019-03-14
申请号：US16034780
申请日：2018-07-13
申请人： HRL Laboratories, LLC
发明人： Yuri Owechko
IPC分类号： G06K9/62 , G06F17/16 , G06F17/12
摘要： Described is a system for sensor data fusion and reconstruction. The system extracts slices from a tensor having multiple tensor modes. Each tensor mode represents a different sensor data stream of incomplete sensor signals. The tensor slices are processed into demixed outputs. The demixed outputs are converted back into tensor slices, and the tensor slices are decomposed into mode factors using matrix decomposition. Mode factors are determined for all of the tensor modes, and the mode factors are assigned to tensor factors by matching mode factors common to two or more demixings. Tensor weight factors are determined and used for fusing the sensor data streams for sensor data reconstruction. Based on the sensor data reconstruction, hidden sensor data is extracted.

8. 发明申请

US20180197050A1 ZERO-SHOT LEARNING USING MULTI-SCALE MANIFOLD ALIGNMENT 审中-公开
公开(公告)号：US20180197050A1
公开(公告)日：2018-07-12
申请号：US15847895
申请日：2017-12-19
申请人： HRL Laboratories, LLC
发明人： Shay Deutsch , Kyungnam Kim , Yuri Owechko
IPC分类号： G06K9/72 , G06K9/62
摘要： Described is a system for recognition of unseen and untrained patterns. A graph is generated based on visual features from input data, the input data including labeled instances and unseen instances. Semantic representations of the input data are assigned as graph signals based on the visual features. The semantic representations are aligned with visual representations of the input data using a regularization method applied directly in a spectral graph wavelets (SGW) domain. The semantic representations are then used to generate labels for the unseen instances. The unseen instances may represent unknown conditions for an autonomous vehicle.

9. 发明授权

US09633483B1 System for filtering, segmenting and recognizing objects in unconstrained environments 有权
公开(公告)号：US09633483B1
公开(公告)日：2017-04-25
申请号：US14669833
申请日：2015-03-26
申请人： HRL Laboratories, LLC
发明人： Jiejun Xu , Kyungnam Kim , Yuri Owechko
IPC分类号： G06T19/20 , G06K9/46 , G06K9/62
CPC分类号： G06T19/20 , G06K9/00214 , G06K9/4604 , G06K9/6267 , G06T17/10 , G06T2210/56
摘要： Described is a system for filtering, segmenting and recognizing objects. The system receives a three-dimensional (3D) point cloud having a plurality of data points in 3D space and down-samples the 3D point cloud to generate a down-sampled 3D point cloud with reduced data points in the 3D space. A ground plane is then identified and removed, leaving above-ground data points in the down-sampled 3D point cloud. The above-ground data points are clustered to generate a plurality of 3D blobs, each of the 3D blobs having a cluster size. The 3D blobs are filtered based on cluster size to generate a set of 3D candidate blobs. Features are extracted from each 3D candidate blob. Finally, at least one of the 3D candidate blobs is classified as a pre-defined object class based on the extracted features.

10. 发明授权

US09230302B1 Foveated compressive sensing system 有权
标题翻译：移动压缩感应系统
公开(公告)号：US09230302B1
公开(公告)日：2016-01-05
申请号：US14204028
申请日：2014-03-11
申请人： HRL Laboratories, LLC
发明人： Yuri Owechko , Kang-Yu Ni , Shankar R. Rao
IPC分类号： G06K9/32 , G06T3/40 , G06T7/00
CPC分类号： G06T7/0022 , G06T1/0007 , G06T3/0012 , H03M7/3062
摘要： Described is a system for foveated compressive sensing. The system is configured to receive an input image f of a scene and initialize a measurement matrix. Global measurements are then performed, with a lower resolution image of the scene thereafter reconstructed. Task salient regions are extracted from the low resolution image. Thereafter, the system estimates a task-specific operator and detects regions-of-interest (ROI) based on the task salient regions. An ROI-adapted and foveated measurement matrix is then generated. Local measurements are then performed on task-relevant ROIs. A higher resolution image can then be reconstructed of the scene to allow for identification of objects in the ROI.
摘要翻译：描述了一种用于移动压缩感测的系统。该系统被配置为接收场景的输入图像f并初始化测量矩阵。然后执行全局测量，然后重建该场景的较低分辨率图像。从低分辨率图像中提取任务显着区域。此后，系统估计特定于任务的运营商并且基于任务突出区域检测感兴趣区域（ROI）。然后生成ROI适应和移动的测量矩阵。然后在任务相关的ROI上执行本地测量。然后可以对场景重建更高分辨率的图像，以允许识别ROI中的对象。

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