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1. 发明授权

US10699139B2 System for real-time object detection and recognition using both image and size features 有权
公开(公告)号：US10699139B2
公开(公告)日：2020-06-30
申请号：US16276513
申请日：2019-02-14
申请人： HRL Laboratories, LLC
发明人： Yang Chen , Deepak Khosla , Ryan M. Uhlenbrock
IPC分类号： G06K9/00 , G06K9/32 , G06K9/46 , G06K9/62 , G06N3/08
摘要： Described is an object recognition system. Using an integral channel features (ICF) detector, the system extracts a candidate target region (having an associated original confidence score representing a candidate object) from an input image of a scene surrounding a platform. A modified confidence score is generated based on a location and height of detection of the candidate object. The candidate target regions are classified based on the modified confidence score using a trained convolutional neural network (CNN) classifier, resulting in classified objects. The classified objects are tracked using a multi-target tracker for final classification of each classified object as a target or non-target. If the classified object is a target, a device can be controlled based on the target.

2. 发明授权

US10410092B1 Automated classification of rock types and analyst-centric visualizations—front end 有权
公开(公告)号：US10410092B1
公开(公告)日：2019-09-10
申请号：US15380039
申请日：2016-12-15
申请人： HRL Laboratories, LLC
发明人： Yang Chen , Deepak Khosla , Fredy Monterroza , Ryan M. Uhlenbrock
IPC分类号： G06K9/62 , G06K9/46 , G01N33/24
摘要： A system and method of automated classification of rock types includes: partitioning, by a processing device, an image into partitions; extracting, by the processing device, sub-images from each of the partitions; first-level classifying, by an automated classifier, the sub-images into corresponding first classes; and second-level classifying, by the processing device, the partitions into corresponding second classes by, for each partition of the partitions, selecting a most numerous one of the corresponding first classes of the sub-images extracted from the partition. A method of displaying automated classification results on a display device is provided. The method includes: receiving, by a processing device, an image partitioned into partitions and classified into corresponding classes; and manipulating, by the processing device, the display device to display the image together with visual identification of the partitions and their corresponding classes. The method may include generating a depth-aligned log file of the classifications.

3. 发明授权

US09978149B1 System and method for door detection for corridor exploration 有权
公开(公告)号：US09978149B1
公开(公告)日：2018-05-22
申请号：US14981131
申请日：2015-12-28
申请人： HRL Laboratories, LLC
发明人： Lei Zhang , Kyungnam Kim , Deepak Khosla
IPC分类号： G06T7/00
CPC分类号： G06T7/0044 , G06T7/0061 , G06T7/0085 , G06T2207/10016 , G06T2207/20021 , G06T2207/20024 , G06T2207/30252
摘要： Described is a system for door detection for use with an unmanned aerial vehicle (UAV). The system receive a video input image from a single monocular camera. Edge points are detected in the video, with the edge points connected to form long edge lines. Orientations of the long edge lines are determined, such that long edge lines having a substantially vertical orientation are designated as initial door line candidates and long edge lines having a non-vertical, non-horizontal orientation are designated for use in detecting a vanishing point. A vanishing point is then detected in the video frame. Thereafter, intensity profile and line properties of the door line candidates are calculated. Finally, it is verified if the door line candidates are real world door lines and, if so, an area between the real world door lines is designated as an open door.

4. 发明授权

US09934437B1 System and method for real-time collision detection 有权
公开(公告)号：US09934437B1
公开(公告)日：2018-04-03
申请号：US14795884
申请日：2015-07-09
申请人： HRL Laboratories, LLC
发明人： Yongqiang Cao , Deepak Khosla , Yang Chen , David J. Huber
IPC分类号： G06K9/00 , G06T7/20
CPC分类号： G06K9/00624 , G06K9/0063 , G06K9/00805 , G06T7/2006 , G06T2207/10004 , G06T2207/30252
摘要： 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.

5. 发明授权

US09489596B1 Optimal rapid serial visual presentation (RSVP) spacing and fusion for electroencephalography (EEG)-based brain computer interface (BCI) 有权
标题翻译：脑电脑界面（BCI）的最佳快速连续视觉呈现（RSVP）间隔和融合，用于脑电图（EEG）
公开(公告)号：US09489596B1
公开(公告)日：2016-11-08
申请号：US14565094
申请日：2014-12-09
申请人： HRL Laboratories, LLC
发明人： Deepak Khosla , Matthew S. Keegan , Kevin R. Martin
IPC分类号： G06K9/62
CPC分类号： G06K9/6263 , A61B5/04001 , A61B5/0476 , A61B5/04842 , A61B5/7267 , G06F3/015 , G06K9/00496 , G06K9/68
摘要： Described is system for optimizing rapid serial visual presentation (RSVP) spacing and fusion. The system receives a sequence of a plurality of rapid serial visual presentation (RSVP) image chips. The plurality of RSVP image chips are generated from an image via a pre-processing step and have a high probability of containing a target of interest. The system alters the order of the sequence of the plurality of RSVP image chips to increase the probability of detection of a true target of interest when presented to a human subject.
摘要翻译：描述了用于优化快速串行视觉呈现（RSVP）间隔和融合的系统。系统接收多个快速串行视觉呈现（RSVP）图像码片的序列。通过预处理步骤从图像生成多个RSVP图像芯片，并且具有包含感兴趣目标的高概率。该系统改变多个RSVP图像码片的序列的顺序，以便在呈现给人类对象时增加感兴趣的真实目标的检测概率。

6. 发明授权

US09269027B1 System for optimal rapid serial visual presentation (RSVP) from user-specific neural brain signals 有权
标题翻译：用于用户特定神经脑信号的最佳快速连续视觉呈现（RSVP）系统
公开(公告)号：US09269027B1
公开(公告)日：2016-02-23
申请号：US14185908
申请日：2014-02-20
申请人： HRL Laboratories, LLC
发明人： Deepak Khosla , David J. Huber , Rajan Bhattacharyya
IPC分类号： G06K9/62 , G06T7/20
CPC分类号： G06K9/6267 , G06K9/00496 , G06K9/4609 , G06K9/6212 , G06K9/626 , G06T7/215
摘要： Described is a system for optimizing rapid serial visual presentation (RSVP). A similarity metric is computed for RSVP images, and the images are sequenced according to the similarity metrics. The sequenced images are presented to a user, and neural signals are received to detect a P300 signal. A neural score for each image is computed, and the system is optimized to model the neural scores. The images are resequenced according a predictive model to output a sequence prediction which does not cause a false P300 signal. Additionally, the present invention describes computing a set of motion surprise maps from image chips. The image chips are labeled as static or moving and prepared into RSVP datasets. Neural signals are recorded in response to the RSVP datasets, and an EEG score is computed from the neural signals. Each image chip is then classified as containing or not containing an item of interest.
摘要翻译：描述了一种用于优化快速串行视觉呈现（RSVP）的系统。为RSVP图像计算相似性度量，并且根据相似性度量对图像进行排序。将序列图像呈现给用户，并且接收神经信号以检测P300信号。计算每个图像的神经分数，并优化系统以对神经分数进行建模。根据预测模型对图像进行重新排序，以输出不引起假P300信号的序列预测。另外，本发明描述了从图像芯片计算一组运动惊喜图。图像芯片被标记为静态或移动并准备成RSVP数据集。根据RSVP数据集记录神经信号，并根据神经信号计算EEG得分。然后将每个图像芯片分类为包含或不包含感兴趣的项目。

7. 发明授权

US09165369B1 Multi-object detection and recognition using exclusive non-maximum suppression (eNMS) and classification in cluttered scenes 有权
标题翻译：使用独占非最大抑制（eNMS）和杂乱场景中的分类进行多对象检测和识别
公开(公告)号：US09165369B1
公开(公告)日：2015-10-20
申请号：US14207391
申请日：2014-03-12
申请人： HRL Laboratories, LLC
发明人： Lei Zhang , Kyungnam Kim , Yang Chen , Deepak Khosla , Shinko Y. Cheng , Alexander L. Honda , Changsoo S. Jeong
IPC分类号： G06T7/00 , G06K9/62
CPC分类号： G06T7/0081 , G06K9/00369 , G06K9/3241 , G06K9/628
摘要： Described is a system for multi-object detection and recognition in cluttered scenes. The system receives an image patch containing multiple objects of interest as input. The system evaluates a likelihood of existence of an object of interest in each sub-window of a set of overlapping sub-windows. A confidence map having confidence values corresponding to the sub-windows is generated. A non-maxima suppression technique is applied to the confidence map to eliminate sub-windows having confidence values below a local maximum confidence value. A global maximum confidence value is determined for a sub-window corresponding to a location of an instance of an object of interest in the image patch. The sub-window corresponding to the location of the instance of the object of interest is removed from the confidence map. The system iterates until a predetermined stopping criteria is met. Finally, detection information related to multiple instances of the object of interest is output.
摘要翻译：描述了一种用于在混乱场景中进行多物体检测和识别的系统。系统接收包含多个感兴趣对象的图像补丁作为输入。系统评估在一组重叠子窗口的每个子窗口中存在感兴趣对象的可能性。产生具有与子窗口对应的置信度值的置信度图。将非最大抑制技术应用于置信图以消除具有低于局部最大置信度值的置信度值的子窗口。对于与图像补丁中的感兴趣对象的实例的位置相对应的子窗口确定全局最大置信度值。对应于感兴趣对象的实例的位置的子窗口从置信度图中移除。系统迭代直到达到预定的停止标准。最后，输出与感兴趣对象的多个实例有关的检测信息。

8. 发明授权

US09165208B1 Robust ground-plane homography estimation using adaptive feature selection 有权
标题翻译：使用自适应特征选择的强大的地平面单应性估计
公开(公告)号：US09165208B1
公开(公告)日：2015-10-20
申请号：US14203470
申请日：2014-03-10
申请人： HRL Laboratories, LLC
发明人： Changsoo S. Jeong , Kyungnam Kim , Yang Chen , Deepak Khosla , Shinko Y. Cheng , Lei Zhang , Alexander L. Honda
IPC分类号： G06K9/46 , G06K9/66
CPC分类号： G06T7/215 , G06K9/0063 , G06T7/194 , G06T7/337 , G06T2207/10016 , G06T2207/20021
摘要： Described is system and method for robust ground-plane homography estimation using adaptive feature selection. The system determines feature correspondences of an image that correspond with at least one moving object in each image in a set of images. Additionally, feature correspondences of the image that correspond with at least one above-ground object are determined in each image. Feature correspondences that correspond with each moving object in each image are excluded, and feature correspondences that correspond with each above-ground object in each image are excluded. Each image is divided into a plurality of sub-regions comprising features correspondences. The number of feature correspondences in each sub-region is limited to a predetermined threshold to ensure that feature correspondences are evenly distributed over each image. Finally, a ground-plane homography estimation between the set of images is generated.
摘要翻译：描述了使用自适应特征选择的鲁棒接地平面单应性估计的系统和方法。系统确定与一组图像中的每个图像中的至少一个移动对象相对应的图像的特征对应。此外，在每个图像中确定与至少一个地面物体对应的图像的特征对应。排除与每个图像中的每个移动物体相对应的特征对应，并排除与每个图像中的每个地面物体相对应的特征对应。每个图像被分成包括特征对应的多个子区域。每个子区域中的特征对应的数量被限制到预定阈值，以确保特征对应均匀分布在每个图像上。最后，生成图像集合之间的地平面单应性估计。

9. 发明申请

US20220375222A1 SYSTEM AND METHOD FOR THE FUSION OF BOTTOM-UP WHOLE-IMAGE FEATURES AND TOP-DOWN ENTTIY CLASSIFICATION FOR ACCURATE IMAGE/VIDEO SCENE CLASSIFICATION 有权
公开(公告)号：US20220375222A1
公开(公告)日：2022-11-24
申请号：US17865337
申请日：2022-07-14
申请人： HRL Laboratories, LLC
发明人： Ryan M. Uhlenbrock , Deepak Khosla , Yang Chen , Fredy Monterroza
IPC分类号： G06V20/40 , G06K9/62 , G06N3/04 , G06V10/40 , G06V10/46 , G06V20/00 , G06N3/08
摘要： Described is a system and method for accurate image and/or video scene classification. More specifically, described is a system that makes use of a specialized convolutional-neural network (hereafter CNN) based technique for the fusion of bottom-up whole-image features and top-down entity classification. When the two parallel and independent processing paths are fused, the system provides an accurate classification of the scene as depicted in the image or video.

10. 发明授权

US11423651B2 System and method for the fusion of bottom-up whole-image features and top-down enttiy classification for accurate image/video scene classification 有权
公开(公告)号：US11423651B2
公开(公告)日：2022-08-23
申请号：US15427597
申请日：2017-02-08
申请人： HRL Laboratories, LLC
发明人： Ryan M. Uhlenbrock , Deepak Khosla , Yang Chen , Fredy Monterroza
IPC分类号： G06K9/62 , G06N3/04 , G06V10/40 , G06V10/46 , G06V20/00 , G06N3/08 , G06V10/82 , G06V20/40 , G06T7/10 , G06T1/20 , G06T1/60 , G06N20/10
摘要： Described is a system and method for accurate image and/or video scene classification. More specifically, described is a system that makes use of a specialized convolutional-neural network (hereafter CNN) based technique for the fusion of bottom-up whole-image features and top-down entity classification. When the two parallel and independent processing paths are fused, the system provides an accurate classification of the scene as depicted in the image or video.

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