公开(公告)号：US08699767B1

公开(公告)日：2014-04-15

申请号：US12975352

申请日：2010-12-21

申请人： Deepak Khosla ,  David J. Huber ,  Rajan Bhattacharyya

发明人： Deepak Khosla ,  David J. Huber ,  Rajan Bhattacharyya

IPC分类号： G06K9/00

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。

公开(公告)号：US08396282B1

公开(公告)日：2013-03-12

申请号：US12262548

申请日：2008-10-31

申请人： David J Huber ,  Deepak Khosla ,  Paul Alex Dow

发明人： David J Huber ,  Deepak Khosla ,  Paul Alex Dow

IPC分类号： G06K9/00 ,  G05B15/00

CPC分类号： G06K9/4623 ,  G06K9/629

摘要： The present disclosure describes a fused saliency map from visual and auditory saliency maps. The saliency maps are in azimuth and elevation coordinates. The auditory saliency map is based on intensity, frequency and temporal conspicuity maps. Once the auditory saliency map is determined, the map is converted into azimuth and elevation coordinates by processing selected snippets of sound from each of four microphones arranged on a robot head to detect the location of the sound source generating the saliencies.

摘要翻译： 本公开描述了来自视觉和听觉显着图的融合显着图。 显着图是方位角和高程坐标。 听觉显着图基于强度，频率和时间显着性图。 一旦听觉显着性图被确定，通过处理布置在机器人头上的四个麦克风中的每一个的选定的声音片段来检测产生出色的声源的位置，将地图转换成方位角和仰角坐标。

公开(公告)号：US08369652B1

公开(公告)日：2013-02-05

申请号：US12584744

申请日：2009-09-11

申请人： Deepak Khosla ,  David J. Huber

发明人： Deepak Khosla ,  David J. Huber

IPC分类号： G06K9/36

CPC分类号： G06K9/4623 ,  G06K9/3241

摘要： Described is a system for finding salient regions in imagery. The system improves upon the prior art by receiving an input image of a scene and dividing the image into a plurality of image sub-regions. Each sub-region is assigned a coordinate position within the image such that the sub-regions collectively form the input image. A plurality of local saliency maps are generated, where each local saliency map is based on a corresponding sub-region and a coordinate position representative of the corresponding sub-region. Finally, the plurality of local saliency maps is combined according to their coordinate positions to generate a single global saliency map of the input image of the scene.

摘要翻译： 描述了一种用于在图像中查找显着区域的系统。 该系统通过接收场景的输入图像并将图像划分为多个图像子区域来改进现有技术。 每个子区域被分配在图像内的坐标位置，使得子区域共同形成输入图像。 生成多个局部显着图，其中每个局部显着图基于对应的子区域和表示相应子区域的坐标位置。 最后，根据它们的坐标位置组合多个局部显着图，以生成场景的输入图像的单个全局显着图。

公开(公告)号：US08837839B1

公开(公告)日：2014-09-16

申请号：US12938722

申请日：2010-11-03

申请人： David J. Huber ,  Deepak Khosla

发明人： David J. Huber ,  Deepak Khosla

IPC分类号： G06K9/62 ,  G06K9/36 ,  G06K9/64 ,  G06K9/32

CPC分类号： G06K9/6218 ,  G06K9/4671

摘要： Described is a system for multiple-object recognition in visual images. The system is configured to receive an input test image comprising at least one object. Keypoints representing the object are extracted using a local feature algorithm. The keypoints from the input test image are matched with keypoints from at least one training image stored in a training database, resulting in a set of matching keypoints. A clustering algorithm is applied to the set of matching keypoints to detect inliers among the set of matching keypoints. The inliers and neighboring keypoints in a vicinity of the inliers are removed from the input test image. An object label and an object boundary for the object are generated, and the object in the input test image is identified and segmented. Also described is a method and computer program product for multiple-object recognition in visual images.

摘要翻译： 描述了一种在视觉图像中进行多目标识别的系统。 该系统被配置为接收包括至少一个对象的输入测试图像。 使用局部特征算法提取表示对象的关键点。 来自输入测试图像的关键点与来自存储在训练数据库中的至少一个训练图像的关键点匹配，从而产生一组匹配的关键点。 将聚类算法应用于匹配关键点集合，以检测匹配关键点集合之间的内联。 从入口测试图像中删除内部值附近的内联和相邻关键点。 生成对象标签和对象边界，并且识别和分割输入测试图像中的对象。 还描述了一种用于视觉图像中的多目标识别的方法和计算机程序产品。

公开(公告)号：US08774517B1

公开(公告)日：2014-07-08

申请号：US12982713

申请日：2010-12-30

申请人： Deepak Khosla ,  Rajan Bhattacharyya ,  Terrell N. Mundhenk ,  David J. Huber

发明人： Deepak Khosla ,  Rajan Bhattacharyya ,  Terrell N. Mundhenk ,  David J. Huber

IPC分类号： G06K9/00

CPC分类号： G06K9/00771 ,  G06K9/00993 ,  G06K9/4623 ,  G06K9/629

摘要： The present invention relates to a system for identifying regions of interest in visual imagery. The system is configured to receive a series of consecutive frames representing a scene as captured from N sensors. The frames include at least a current frame and a previous frame. A surprise map can be generated based on features found in the current frame and the previous frame. The surprise map having a plurality of values corresponding to spatial locations within the scene. Based on the values, a surprise in the scene can be identified if a value in the surprise map exceeds a predetermined threshold.

摘要翻译： 本发明涉及用于识别视觉图像中的感兴趣区域的系统。 该系统被配置为从N个传感器接收一系列表示场景的连续帧。 帧至少包括当前帧和前一帧。 可以基于当前帧和前一帧中发现的特征生成惊喜图。 惊奇地图具有对应于场景内的空间位置的多个值。 基于这些值，如果惊奇图中的值超过预定阈值，则可以识别场景中的惊喜。

公开(公告)号：US08285052B1

公开(公告)日：2012-10-09

申请号：US12653561

申请日：2009-12-15

申请人： Rajan Bhattacharyya ,  Deepak Khosla ,  David J. Huber ,  Penn Tasinga

发明人： Rajan Bhattacharyya ,  Deepak Khosla ,  David J. Huber ,  Penn Tasinga

IPC分类号： G06K9/46 ,  G06K9/68

CPC分类号： G06F17/30247

摘要： Described is a system for ordering images. The system receives a plurality of images. Image features are extracted from each image. A set of all possible image pairs are generated for all images. A similarity metric with weights is generated between the images in each image pair in the set, with a net similarity metric thereafter generated by combining the similarity metrics. The images are then ordered according to the net similarity metrics to generate a computer-ordered set of images. The computer-ordered set of images is then displayed to the user, which allows the user to re-order the images to generate a user-ordered set of images. The weights are then optimized to minimize the distance between the computer-ordered set of images and the user-ordered set of images. The similarity metrics are then re-weighted, with the images thereafter being re-ordered according to the new metrics.

摘要翻译： 描述了一种用于排序图像的系统。 系统接收多个图像。 从每个图像中提取图像特征。 为所有图像生成一组所有可能的图像对。 在集合中的每个图像对中的图像之间生成具有权重的相似性度量，其后通过组合相似度度量而生成净相似度度量。 然后根据网络相似度度量对图像进行排序以生成计算机有序的图像集合。 然后将计算机有序的图像集合显示给用户，这允许用户重新排序图像以生成用户有序的图像集合。 然后优化权重以最小化计算机有序的图像集与用户有序的图像集之间的距离。 然后相似性度量被重新加权，其后的图像根据新的度量重新排序。

公开(公告)号：US08693729B1

公开(公告)日：2014-04-08

申请号：US13585356

申请日：2012-08-14

申请人： Paul Alex Dow ,  Deepak Khosla ,  David J Huber

发明人： Paul Alex Dow ,  Deepak Khosla ,  David J Huber

IPC分类号： G06T7/00 ,  B25J9/16

CPC分类号： G06K9/00 ,  G06T7/74 ,  G06T2207/20081

摘要： The present invention creates and stores target representations in several coordinate representations based on biologically inspired models of the human vision system. By using biologically inspired target representations a computer can be programmed for robot control without using kinematics to relate a target position in camera eyes to a target position in body or head coordinates. The robot sensors and appendages are open loop controlled to focus on the target. In addition, the invention herein teaches a scenario and method to learn the mappings between coordinate representations using existing machine learning techniques such as Locally Weighted Projection Regression.

摘要翻译： 本发明基于人类视觉系统的生物学启发模型创建并存储几个坐标表示中的目标表示。 通过使用生物启发的目标表示，计算机可以被编程用于机器人控制，而不使用运动学来将相机眼睛中的目标位置与身体或头部坐标中的目标位置相关联。 机器人传感器和附件是开环控制的，专注于目标。 另外，本发明教导了使用现有机器学习技术（例如局部加权投影回归）来学习坐标表示之间的映射的情景和方法。

公开(公告)号：US08311317B1

公开(公告)日：2012-11-13

申请号：US12192918

申请日：2008-08-15

申请人： Paul Alex Dow ,  Deepak Khosla ,  David J Huber

发明人： Paul Alex Dow ,  Deepak Khosla ,  David J Huber

IPC分类号： G06K9/00

CPC分类号： G06K9/00 ,  G06T7/74 ,  G06T2207/20081

摘要： The present invention creates and stores target representations in several coordinate representations based on biologically inspired models of the human vision system. By using biologically inspired target representations a computer can be programmed for robot control without using kinematics to relate a target position in camera eyes to a target position in body or head coordinates. The robot sensors and appendages are open loop controlled to focus on the target. In addition, the invention herein teaches a scenario and method to learn the mappings between coordinate representations using existing machine learning techniques such as Locally Weighted Projection Regression.

摘要翻译： 本发明基于人类视觉系统的生物学启发模型创建并存储几个坐标表示中的目标表示。 通过使用生物启发的目标表示，计算机可以被编程用于机器人控制，而不使用运动学来将相机眼睛中的目标位置与身体或头部坐标中的目标位置相关联。 机器人传感器和附件是开环控制的，专注于目标。 另外，本发明教导了使用现有机器学习技术（例如局部加权投影回归）来学习坐标表示之间的映射的情景和方法。

公开(公告)号：US08634982B2

公开(公告)日：2014-01-21

申请号：US12543665

申请日：2009-08-19

申请人： Deepak Khosla ,  David Fuciarelli ,  David L Ii

发明人： Deepak Khosla ,  David Fuciarelli ,  David L Ii

IPC分类号： G05D1/00

CPC分类号： G05D1/104

摘要： To improve the scheduling and tasking of sensors, the present disclosure describes an improved planning system and method for the allocation and management of sensors. In one embodiment, the planning system uses a branch and bound approach of tasking sensors using a heuristic to expedite arrival at a deterministic solution. In another embodiment, a progressive lower bound is applied to the branch and bound approach. Also, in another embodiment, a hybrid branch and bound approach is used where both local and global planning are employed in a tiered fashion.

摘要翻译： 为了改进传感器的调度和任务，本公开描述了用于分配和管理传感器的改进的规划系统和方法。 在一个实施例中，规划系统使用启发式方法使用任务传感器的分支和绑定方法来加速到达确定性解决方案。 在另一个实施例中，渐进下界应用于分支和绑定方法。 此外，在另一个实施例中，使用混合分支和绑定方法，其中以分层方式采用本地和全局规划。

公开(公告)号：US20120209652A1

公开(公告)日：2012-08-16

申请号：US13069638

申请日：2011-03-23

申请人： Deepak Khosla ,  Eric Huang ,  David L. Ii

发明人： Deepak Khosla ,  Eric Huang ,  David L. Ii

IPC分类号： G06Q10/00

CPC分类号： G06Q10/0631

摘要： To improve the scheduling and tasking of resources, the present disclosure describes an improved planning system and method for the allocation and management of resources. The planning system uses a branch and bound approach of tasking resources using a heuristic to expedite arrival at a deterministic solution. For each possible functional mode of the resources, an upper bound is determined. The upper bounds are employed in an objective function for the branch and bound process to determine the functional mode in which to place the resources and to determine movement paths for the resources, all in an environment where a hostile force may attempt to destroy the resources.

摘要翻译： 为了改进资源的调度和任务，本公开描述了一种用于资源分配和管理的改进的规划系统和方法。 规划系统使用启发式方法使用分支和绑定的任务资源方法来加速到达确定性解决方案。 对于资源的每个可能的功能模式，确定上限。 上限用于分支和绑定过程的目标函数，以确定放置资源的功能模式，并确定资源的移动路径，所有这些都在敌对力量可能试图破坏资源的环境中。