公开(公告)号：US20190228300A1

公开(公告)日：2019-07-25

申请号：US16199054

申请日：2018-11-23

Applicant: HRL Laboratories, LLC

Inventor： Yongqiang Cao ,  Praveen K. Pilly

IPC: G06N3/08 ,  G06K9/00 ,  G06K9/62 ,  G06N20/00 ,  G06N3/063

Abstract: Described is a system for pattern recognition designed for neuromorphic hardware. The system generates a spike train of neuron spikes for training patterns with each excitatory neuron in an excitatory layer, where each training pattern belongs to a pattern class. A spiking rate distribution of excitatory neurons is generated for each pattern class. Each spiking rate distribution of excitatory neurons is normalized, and a class template is generated for each pattern class from the normalized spiking rate distributions. An unlabeled input pattern is classified using the class templates. A mechanical component of an autonomous device can be controlled based on classification of the unlabeled input pattern.

公开(公告)号：US09984326B1

公开(公告)日：2018-05-29

申请号：US14680057

申请日：2015-04-06

Applicant: HRL Laboratories, LLC

Inventor： Yang Chen ,  Yongqiang Cao ,  Deepak Khosla

IPC: G06K9/62 ,  G06N3/10 ,  G06N3/08

CPC classification number: G06K9/62 ,  G06N3/049 ,  G06N3/088

Abstract: Described is system for simulating spiking neural networks for image and video processing. The system processes an image with a spiking neural network simulator having a plurality of inter-connected modules. Each module comprises a plurality of neuron elements. Processing the image further comprises performing a neuron state update for each module, that includes aggregating input spikes and updating neuron membrane potentials, and performing spike propagation for each module, which includes transferring spikes generated in a current time step. Finally, an analysis result is output.

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：US09934437B1

公开(公告)日：2018-04-03

申请号：US14795884

申请日：2015-07-09

Applicant: HRL Laboratories, LLC

Inventor： Yongqiang Cao ,  Deepak Khosla ,  Yang Chen ,  David J. Huber

IPC: G06K9/00 ,  G06T7/20

CPC classification number: G06K9/00624 ,  G06K9/0063 ,  G06K9/00805 ,  G06T7/2006 ,  G06T2207/10004 ,  G06T2207/30252

Abstract: Described is a system for collision detection. The system divides an image in a sequence of images into multiple sub-fields comprising complementary visual sub-fields. For each visual sub-field, motion is detected in a direction corresponding to the visual sub-field using a spiking Reichardt detector with a spiking neural network. Motion in a direction complementary to the visual sub-field is also detected using the spiking Reichardt detector. Outputs of the spiking Reichardt detector, comprising data corresponding to one direction of movement from two complementary visual sub-fields, are processed using a movement detector. Based on the output of the movement detector, an impending collision is signaled.

公开(公告)号：US20170316555A1

公开(公告)日：2017-11-02

申请号：US15481220

申请日：2017-04-06

Applicant: HRL Laboratories, LLC

Inventor： Yongqiang Cao ,  Narayan Srinivasa

IPC: G06T5/50

CPC classification number: G06T5/50 ,  G06T7/194 ,  G06T7/254 ,  G06T2207/10016 ,  G06T2207/20021 ,  G06T2207/20224

Abstract: Described is a system for ghost removal in video footage. During operation, the system generates a background subtraction map and an original bounding box that surrounds a detected foreground object through background subtraction. A detected foreground map is then generated. The detected foreground map includes at least two detected foreground (DF) bounding boxes of detected foregrounds obtained by a difference of two consecutive frames in video footage. Further, the original bounding box is then trimmed into a trimmed box, the trimmed box being a smallest box that contains the at least two DF bounding boxes. The trimmed box is designated as containing a real-world object, which can then be used for object tracking.

公开(公告)号：US10198689B2

公开(公告)日：2019-02-05

申请号：US15269777

申请日：2016-09-19

Applicant: HRL Laboratories, LLC

Inventor： Yongqiang Cao ,  Qin Jiang ,  Yang Chen ,  Deepak Khosla

IPC: G06K9/62 ,  G06N3/04 ,  G06K9/46 ,  G06K9/42 ,  G06K9/44

Abstract: Described is a system for object detection in images or videos using spiking neural networks. An intensity saliency map is generated from an intensity of an input image having color components using a spiking neural network. Additionally, a color saliency map is generated from a plurality of colors in the input image using a spiking neural network. An object detection model is generated by combining the intensity saliency map and multiple color saliency maps. The object detection model is used to detect multiple objects of interest in the input image.

公开(公告)号：US10121234B2

公开(公告)日：2018-11-06

申请号：US15481220

申请日：2017-04-06

Applicant: HRL Laboratories, LLC

Inventor： Yongqiang Cao ,  Narayan Srinivasa

IPC: G06K9/00 ,  G06T5/50

Abstract: Described is a system for ghost removal in video footage. During operation, the system generates a background subtraction map and an original bounding box that surrounds a detected foreground object through background subtraction. A detected foreground map is then generated. The detected foreground map includes at least two detected foreground (DF) bounding boxes of detected foregrounds obtained by a difference of two consecutive frames in video footage. Further, the original bounding box is then trimmed into a trimmed box, the trimmed box being a smallest box that contains the at least two DF bounding boxes. The trimmed box is designated as containing a real-world object, which can then be used for object tracking.

公开(公告)号：US10387774B1

公开(公告)日：2019-08-20

申请号：US14609775

申请日：2015-01-30

Applicant: HRL Laboratories, LLC

Inventor： Yongqiang Cao ,  Yang Chen ,  Deepak Khosla

IPC: G06N3/08

Abstract: Described is a system for converting convolutional neural networks to spiking neural networks. A convolutional neural network (CNN) is adapted to fit a set of requirements of a spiking neural network (SNN), resulting in an adapted CNN. The adapted CNN is trained to obtain a set of learned weights, and the set of learned weights is then applied to a converted SNN having an architecture similar to the adapted CNN. The converted SNN is then implemented on neuromorphic hardware, resulting in reduced power consumption.

公开(公告)号：US10311341B1

公开(公告)日：2019-06-04

申请号：US15249849

申请日：2016-08-29

Applicant: HRL Laboratories, LLC

Inventor： Yongqiang Cao ,  Praveen K. Pilly ,  Narayan Srinivasa

IPC: H03L7/26 ,  G06K9/66 ,  G06K9/62 ,  G06K9/46 ,  G06T7/00 ,  G06N3/04 ,  G06N3/08 ,  G06T15/20 ,  H03K4/50 ,  G06T5/20 ,  H04L9/12 ,  H04L9/00 ,  H04N19/156 ,  G09G3/20 ,  H03K3/03 ,  G09G5/00

Abstract: Described is a system and method for ultra-low power consumption state deep online learning. The system operates by filtering an input image to generate one or more feature maps. The one or more feature maps are divided into non-overlapping small regions with feature values in each small region pooled to generate decreased size feature maps. The decreased size feature maps are divided into overlapping patches which are joined together to form a collection of cell maps having connections to the decreased sized feature maps. The collection of cell maps are then divided into non-overlapping small regions, with feature values in each small region pooled to generate a decreased sized collection of cell maps. The decreased sized collection of cell maps are then mapped to a single cell, which results in a class label being generated as related to the input image based on the single cell.

公开(公告)号：US10089549B1

公开(公告)日：2018-10-02

申请号：US15584986

申请日：2017-05-02

Applicant: HRL Laboratories, LLC

Inventor： Yongqiang Cao ,  Narayan Srinivasa

IPC: G06T7/20 ,  G06K9/32 ,  G06K9/20 ,  G06F17/11 ,  G06T7/215 ,  G06K9/48

Abstract: Described is a system for estimating ego-motion of a moving camera for detection of independent moving objects in a scene. For consecutive frames in a video captured by a moving camera, a first ego-translation estimate is determined between the consecutive frames from a first local minimum. From a second local minimum, a second ego-translation estimate is determined. If the first ego-translation estimate is equivalent to the second ego-translation estimate, the second ego-translation estimate is output as the optimal solution. Otherwise, a cost function is minimized to determine an optimal translation until the first ego-translation estimate is equivalent to the second ego-translation estimate, and an optimal solution is output. Ego-motion of the camera is estimated using the optimal solution, and independent moving objects are detected in the scene.