公开(公告)号：US20180225833A1

公开(公告)日：2018-08-09

申请号：US15585032

申请日：2017-05-02

Applicant: HRL Laboratories, LLC

Inventor： Yongqiang Cao ,  Narayan Srinivasa ,  Shankar R. Rao

IPC: G06T7/246 ,  G06T7/73 ,  G06K9/32

CPC classification number: G06T7/20 ,  G06K9/32 ,  G06T2207/20068

Abstract: Described is a system for compensating for ego-motion during video processing. The system generates an initial estimate of camera ego-motion of a moving camera for consecutive image frame pairs of a video of a scene using a projected correlation method, the camera configured to capture the video from a moving platform. An optimal estimation of camera ego-motion is generated using the initial estimate as an input to a valley search method or an alternate line search method. All independent moving objects are detected in the scene using the described hybrid method at superior performance compared to existing methods while saving computational cost.

公开(公告)号：US09749007B1

公开(公告)日：2017-08-29

申请号：US15073626

申请日：2016-03-17

Applicant: HRL Laboratories, LLC

Inventor： Charles E Martin ,  Shankar R. Rao ,  Peter Petre

IPC: H04B1/00 ,  H04B1/719 ,  H04B1/7163

CPC classification number: H04B1/719 ,  G06K9/0057 ,  G06K9/624 ,  G06N3/0445 ,  H04B1/71632

Abstract: Described is a cognitive blind source separator (CBSS). The CBSS includes a delay embedding module that receives a mixture signal (the mixture signal being a time-series of data points from one or more mixtures of source signals) and time-lags the signal to generate a delay embedded mixture signal. The delay embedded mixture signal is then linearly mapped into a reservoir to create a high-dimensional state-space representation of the mixture signal. The state-space representations are then linearly mapped to one or more output nodes in an output layer to generate pre-filtered signals. The pre-filtered signals are passed through a bank of adaptable finite impulse response (FIR) filters to generate separate source signals that collectively formed the mixture signal.

公开(公告)号：US10712425B1

公开(公告)日：2020-07-14

申请号：US16112071

申请日：2018-08-24

Applicant: HRL Laboratories, LLC

Inventor： Shankar R. Rao ,  Peter Petre ,  Bryan H. Fong

IPC: H04B1/719 ,  G01S7/28 ,  G01S13/88 ,  G06N3/04 ,  G06N3/08

Abstract: Described is a system for signal denoising using a cognitive signal processor having a time-varying reservoir. The system receives a noisy input signal of a time-series of data points from a mixture of waveform signals. The noisy input signal is linearly mapped into the time-varying reservoir. A high-dimensional state-space representation of the mixture of waveform signals is generated by combining the noisy input signal with a plurality of reservoir states. The system then generates a denoised signal corresponding to the noisy input signal.

公开(公告)号：US10310074B1

公开(公告)日：2019-06-04

申请号：US14670199

申请日：2015-03-26

Applicant: HRL Laboratories, LLC

Inventor： Kang-Yu Ni ,  Shankar R. Rao

IPC: G01S13/90 ,  G06T11/00 ,  G06T7/20 ,  G06T5/00 ,  G06T7/194

Abstract: Described is a system for synthetic aperture radar (SAR) imaging. The system is adapted to reconstruct a set of images to generate a set of reconstructed SAR images, wherein at least some of the reconstructed SIR images have noise and contain glint. The reconstructed SAR images are then stacked into a matrix D, in which each column of the matrix is a reconstructed SAR image. Using sparse and low-rank decomposition on the matrix D, the system then extracts a clean background from the reconstructed SAR images and separates the noise and glint. Based on that, the system is operable to detect moving targets in sparse part S and issuing a notification of such a moving target.

公开(公告)号：US10153806B1

公开(公告)日：2018-12-11

申请号：US15452412

申请日：2017-03-07

Applicant: HRL Laboratories, LLC

Inventor： Peter Petre ,  Shankar R. Rao

IPC: H04B1/00 ,  H04B1/719 ,  H04B1/7163

Abstract: Described is a cognitive signal processor that can denoise an input signal that contains a mixture of waveforms over a large bandwidth. Delay-embedded mixture signals are generated from a mixture of input signals. The delay-embedded mixture signals are mapped with a reservoir computer to reservoir states of a dynamical reservoir having output layer weights. The output layer weights are adapted based on short-time linear prediction. Finally, a denoised output of the mixture of input signals is generated.

公开(公告)号：US20180322373A1

公开(公告)日：2018-11-08

申请号：US15949896

申请日：2018-04-10

Applicant: HRL Laboratories, LLC

Inventor： Soheil Kolouri ,  Shankar R. Rao ,  Kyungnam Kim

IPC: G06K9/72 ,  G06K9/62 ,  G06N3/08 ,  G06N3/04

Abstract: Described is a system that can recognize novel objects that the system has never before seen. The system uses a training image set to learn a model that maps visual features from known images to semantic attributes. The learned model is used to map visual features of an unseen input image to semantic attributes. The unseen input image is classified as belonging to an image class with a class label. A device is controlled based on the class label.

公开(公告)号：US09544550B1

公开(公告)日：2017-01-10

申请号：US14209136

申请日：2014-03-13

Applicant: HRL Laboratories, LLC

Inventor： Kang-Yu Ni ,  Shankar R. Rao ,  Yuri Owechko

IPC: H04N7/18

CPC classification number: G06K9/6249 ,  G06K9/00771 ,  G08B13/19604

Abstract: 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.

Abstract translation: 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。

公开(公告)号：US09450597B1

公开(公告)日：2016-09-20

申请号：US14702294

申请日：2015-05-01

Applicant: HRL LABORATORIES, LLC

Inventor： Mohiuddin Ahmed ,  Shankar R. Rao ,  Willie W. Ng

IPC: H03M1/12 ,  G02F7/00 ,  H03M1/06

CPC classification number: G02F7/00 ,  G02F1/065 ,  H03M1/0629 ,  H03M1/123 ,  H03M1/1245 ,  H03M1/128

Abstract: A back end-circuit for randomized non uniform and alias-free subsampling, comprising: an analog-to-digital converter (ADC) configured for sampling an input signal at random non uniform times; a compressive sensing processor, coupled to the ADC, to recover a sparse spectral representation of the input signal; and a Fourier transformer for converting the sparse spectral representation to a time sampled representation of the input signal.

Abstract translation: 一种用于随机非均匀和无别名的次采样的后端电路，包括：模数转换器（ADC），被配置为在随机的非均匀时间对输入信号进行采样; 耦合到ADC的压缩感测处理器，以恢复输入信号的稀疏频谱表示; 以及用于将稀疏频谱表示转换为输入信号的时间采样表示的傅里叶变换器。

公开(公告)号：US10720949B1

公开(公告)日：2020-07-21

申请号：US16295917

申请日：2019-03-07

Applicant: HRL Laboratories, LLC

Inventor： Shankar R. Rao ,  Peter Petre

IPC: H04B1/12 ,  G06N3/08 ,  G06N3/04

Abstract: Described is a multi-input cognitive signal processor (CSP) for estimating time-difference-of-arrival (TDOA) of incoming signals. The multi-input CSP receives a mixture of input signals from an antenna a and an antenna b. The multi-input CSP predicts and temporally de-noises input signals a and b received from antennas a and b, respectively, using an input corresponding to each input signal, resulting in de-noised state vectors for input signals a and b. Using the de-noised state vectors for input signals a and b, cross-predicting and spatially de-noising the other of the de-noised state vectors for input signals a and b. TDOA values of signal pulses to each of antennas a and b are estimated and converted into estimated angles of arrival for each signal pulse.

公开(公告)号：US10528818B1

公开(公告)日：2020-01-07

申请号：US15143471

申请日：2016-04-29

Applicant: HRL Laboratories, LLC

Inventor： Shankar R. Rao ,  Kang-Yu Ni ,  Yuri Owechko

IPC: G06K9/00 ,  G06K9/46 ,  G06T7/20 ,  H04N7/18

Abstract: Described is a video scene analysis system. The system includes a salience module that receives a video stream having one more pairs of frames (each frame having a background and a foreground) and detects salient regions in the video stream to generate salient motion estimates. The salient regions are regions that move differently than dominant motion in the pairs of video frames. A scene modeling module generates a sparse foreground model based on salient motion estimates from a plurality of consecutive frames. A foreground refinement module then generates a Task-Aware Foreground by refining the sparse foreground model based on task knowledge. The Task-Aware Foreground can then be used for further processing such as object detection, tracking or recognition.