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    APPARATUS AND METHODS FOR SALIENCY DETECTION BASED ON COLOR OCCURRENCE ANALYSIS
    11.
    发明申请
    APPARATUS AND METHODS FOR SALIENCY DETECTION BASED ON COLOR OCCURRENCE ANALYSIS 审中-公开

    公开(公告)号：US20180293742A1

    公开(公告)日：2018-10-11

    申请号：US15871862

    申请日：2018-01-15

    Applicant: Brain Corporation

    Inventor： FILIP Piekniewski ,  Micah Richert ,  Dimitry Fisher

    IPC: G06T7/292

    Abstract: Apparatus and methods for detecting and utilizing saliency in digital images. In one implementation, salient objects may be detected based on analysis of pixel characteristics. Least frequently occurring pixel values may be deemed as salient. Pixel values in an image may be compared to a reference. Color distance may be determined based on a difference between reference color and pixel color. Individual image channels may be scaled when determining saliency in a multi-channel image. Areas of high saliency may be analyzed to determine object position, shape, and/or color. Multiple saliency maps may be additively or multiplicative combined in order to improve detection performance (e.g., reduce number of false positives). Methodologies described herein may enable robust tracking of objects utilizing fewer determination resources. Efficient implementation of the methods described below may allow them to be used for example on board a robot (or autonomous vehicle) or a mobile determining platform.

    TRAINABLE MODULAR ROBOTIC APPARATUS AND METHODS
    12.
    发明申请
    TRAINABLE MODULAR ROBOTIC APPARATUS AND METHODS 有权

    公开(公告)号：US20150258682A1

    公开(公告)日：2015-09-17

    申请号：US14208709

    申请日：2014-03-13

    Applicant: Brain Corporation

    Inventor： Eugene Izikevich ,  Dimitry Fisher ,  Jean-Baptiste Passot

    IPC: B25J9/16 ,  B25J9/00

    CPC classification number: B25J9/0009 ,  B25J9/08 ,  B25J9/104 ,  Y10T74/20305 ,  Y10T74/20311

    Abstract: Apparatus and methods for a modular robotic device with artificial intelligence that is receptive to training controls. In one implementation, modular robotic device architecture may be used to provide all or most high cost components in an autonomy module that is separate from the robotic body. The autonomy module may comprise controller, power, actuators that may be connected to controllable elements of the robotic body. The controller may position limbs of the toy in a target position. A user may utilize haptic training approach in order to enable the robotic toy to perform target action(s). Modular configuration of the disclosure enables users to replace one toy body (e.g., the bear) with another (e.g., a giraffe) while using hardware provided by the autonomy module. Modular architecture may enable users to purchase a single AM for use with multiple robotic bodies, thereby reducing the overall cost of ownership.

    COMPUTERIZED LEARNING LANDSCAPING APPARATUS AND METHODS
    16.
    发明申请
    COMPUTERIZED LEARNING LANDSCAPING APPARATUS AND METHODS 有权
    计算机学习景观设计与方法

    公开(公告)号：US20160150739A1

    公开(公告)日：2016-06-02

    申请号：US14558403

    申请日：2014-12-02

    Applicant: Brain Corporation

    Inventor： Dimitry Fisher

    IPC: A01G3/04 ,  G05D1/00 ,  A01D34/00 ,  G06Q30/02

    CPC classification number: A01G3/0435 ,  A01D34/008 ,  G05D1/0221 ,  G05D2201/0208 ,  G06Q30/0241

    Abstract: A method and an apparatus for shaping of lawns and hedges into desired 3D patterns or shapes. The apparatus consists of a bStem and/or other computational device comprising storage, a motorized platform, and trimmer end effectors. The computational device instructs the end effectors to extend or retract as the platform moves along at a steady pace, thus producing a target pattern (e.g., a company logo) in a hedge, lawn, a wall or a ground-cover of any material suitable for such shaping. The apparatus may be configured to operate autonomously based on a pre-loaded pattern file. Software (e.g., such as BrainOS) may be used to provide real-time feedback to trimmers regarding the process and the results, and possibly to train the inverse model accordingly. The apparatus may learn to minimize predicted or current mismatches between the desired pattern and the one being produced. Users compete for the best designs.

    Abstract translation: 一种用于将草坪和树篱整形成所需3D图案或形状的方法和装置。 该装置由包括存储器，电动平台和修剪器末端执行器的bStem和/或其他计算设备组成。 当平台以稳定的速度移动时，计算设备指示终端执行器延伸或缩回，从而在适合的任何材料的树篱，草坪，墙壁或地面覆盖物中产生目标图案（例如，公司标志） 用于这种塑形。 该装置可以被配置为基于预加载模式文件自主地操作。 可以使用软件（例如，诸如BrainOS）来提供关于过程和结果的修剪器的实时反馈，并且可能相应地训练逆模型。 该装置可以学习最小化期望图案与正在生产的图案之间的预测或当前不匹配。 用户竞争最好的设计。

    Spiking neuron network apparatus and methods for encoding of sensory data
    17.
    发明授权
    Spiking neuron network apparatus and methods for encoding of sensory data 有权
    Spiking神经元网络设备和感觉数据编码方法

    公开(公告)号：US09311594B1

    公开(公告)日：2016-04-12

    申请号：US13623838

    申请日：2012-09-20

    Applicant: BRAIN CORPORATION

    Inventor： Dimitry Fisher ,  Eugene Izhikevich ,  Vadim Polonichko

    IPC: G06N5/00 ,  G06F1/00 ,  G06N3/04

    CPC classification number: G06N3/049

    Abstract: Sensory encoder may be implemented. Visual encoder apparatus may comprise spiking neuron network configured to receive photodetector input. Excitability of neurons may be adjusted and output spike may be generated based on the input. When neurons generate spiking response, spiking threshold may be dynamically adapted to produce desired output rate. The encoder may dynamically adapt its input range to match statistics of the input and to produce output spikes at an appropriate rate and/or latency. Adaptive input range adjustment and/or spiking threshold adjustment collaborate to enable recognition of features in sensory input of varying dynamic range.

    Abstract translation: 可以实现感觉编码器。 可视编码器装置可以包括配置成接收光电检测器输入的加标神经元网络。 可以调整神经元的兴奋性，并且可以基于输入产生输出尖峰。 当神经元产生尖峰响应时，尖峰阈值可以动态地适应以产生期望的输出速率。 编码器可以动态地调整其输入范围以匹配输入的统计量并以适当的速率和/或等待时间产生输出尖峰。 自适应输入范围调整和/或加标阈值调整协作可以识别不同动态范围的感官输入中的特征。

    APPARATUS AND METHODS FOR TRACKING SALIENT FEATURES
    18.
    发明申请
    APPARATUS AND METHODS FOR TRACKING SALIENT FEATURES 审中-公开
    跟踪特征的装置和方法

    公开(公告)号：US20160086051A1

    公开(公告)日：2016-03-24

    申请号：US14637164

    申请日：2015-03-03

    Applicant: Brain Corporation

    Inventor： Filip Piekniewski ,  Micah Richert ,  Dimitry Fisher

    IPC: G06K9/46 ,  G06T3/00 ,  H04N9/797 ,  G06T7/40 ,  G06T7/00 ,  G06T3/40 ,  G06T7/20

    CPC classification number: G06T7/292 ,  G06K9/3241 ,  G06K9/4623 ,  G06K9/4642 ,  G06K9/4652 ,  G06K9/4671 ,  G06K9/48 ,  G06T7/20 ,  G06T7/246 ,  G06T7/90 ,  G06T2207/10024 ,  G06T2207/20164 ,  G06T2207/30241 ,  G06T2215/16 ,  H04N9/7973

    Abstract: Apparatus and methods for detecting and utilizing saliency in digital images. In one implementation, salient objects may be detected based on analysis of pixel characteristics. Least frequently occurring pixel values may be deemed as salient. Pixel values in an image may be compared to a reference. Color distance may be determined based on a difference between reference color and pixel color. Individual image channels may be scaled when determining saliency in a multi-channel image. Areas of high saliency may be analyzed to determine object position, shape, and/or color. Multiple saliency maps may be additively or multiplicative combined in order to improve detection performance (e.g., reduce number of false positives). Methodologies described herein may enable robust tracking of objects utilizing fewer determination resources. Efficient implementation of the methods described below may allow them to be used for example on board a robot (or autonomous vehicle) or a mobile determining platform.

    Abstract translation: 用于检测和利用数字图像显着性的装置和方法。 在一个实现中，可以基于像素特性的分析来检测显着对象。 最常出现的像素值可能被认为是显着的。 图像中的像素值可以与参考进行比较。 可以基于参考颜色和像素颜色之间的差来确定颜色距离。 当确定多通道图像中的显着性时，可以缩放各个图像通道。 可以分析高显着性的区域以确定对象位置，形状和/或颜色。 为了提高检测性能（例如，减少误报数量），多重显着图可以被相加或乘法组合。 本文描述的方法可以利用较少的确定资源来实现对物体的鲁棒跟踪。 下面描述的方法的有效实现可以允许它们例如在机器人（或自主车辆）或移动确定平台上使用。

    Rate stabilization through plasticity in spiking neuron network
    19.
    发明授权
    Rate stabilization through plasticity in spiking neuron network 有权
    通过刺激神经元网络中的可塑性来稳定速率

    公开(公告)号：US09275326B2

    公开(公告)日：2016-03-01

    申请号：US13691554

    申请日：2012-11-30

    Applicant: Brain Corporation

    Inventor： Filip Piekniewski ,  Micah Richert ,  Dimitry Fisher ,  Eugene Izhikevich

    IPC: G06F15/18 ,  G06N3/02 ,  G06N3/04

    CPC classification number: G06N3/02 ,  G06N3/049 ,  G06N3/088

    Abstract: Apparatus and methods for activity based plasticity in a spiking neuron network adapted to process sensory input. In one embodiment, the plasticity mechanism may be configured for example based on activity of one or more neurons providing feed-forward stimulus and activity of one or more neurons providing inhibitory feedback. When an inhibitory neuron generates an output, inhibitory connections may be potentiated. When an inhibitory neuron receives inhibitory input, the inhibitory connection may be depressed. When the inhibitory input arrives subsequent to the neuron response, the inhibitory connection may be depressed. When input features are unevenly distributed in occurrence, the plasticity mechanism is capable of reducing response rate of neurons that develop receptive fields to more prevalent features. Such functionality may provide network output such that rarely occurring features are not drowned out by more widespread stimulus.

    Abstract translation: 适用于处理感觉输入的加标神经元网络中基于活动的可塑性的装置和方法。 在一个实施方案中，可塑性机制可以例如基于提供前馈刺激的一个或多个神经元的活性和提供抑制反馈的一个或多个神经元的活性来配置。 当抑制性神经元产生输出时，可以增强抑制性连接。 当抑制性神经元接受抑制性输入时，可能抑制抑制性连接。 当抑制输入在神经元响应之后到达时，可能抑制抑制性连接。 当输入特征在发生时不均匀分布时，可塑性机制能够将发展接受场的神经元的反应率降低到更普遍的特征。 这样的功能可以提供网络输出，使得很少发生的特征不被更广泛的刺激所淹没。

    APPARATUS AND METHODS FOR TRAINING ROBOTS UTILIZING GAZE-BASED SALIENCY MAPS
    20.
    发明申请
    APPARATUS AND METHODS FOR TRAINING ROBOTS UTILIZING GAZE-BASED SALIENCY MAPS 审中-公开
    使用基于GAZE的消费者培训机器人的装置和方法

    公开(公告)号：US20150339589A1

    公开(公告)日：2015-11-26

    申请号：US14284120

    申请日：2014-05-21

    Applicant: BRAIN CORPORATION

    Inventor： Dimitry Fisher

    IPC: G06N99/00 ,  B25J9/16

    CPC classification number: G06N20/00 ,  B25J9/16 ,  G05B2219/33034 ,  G05B2219/36039 ,  G06K9/00597 ,  G06K9/00771 ,  G06K9/00805 ,  G06K9/00845 ,  G06K9/4628 ,  G06N3/008 ,  G06N3/049 ,  G06N99/00

    Abstract: Robotic devices may be trained using saliency maps derived from gaze of a trainer. In navigation applications, the saliency map may correspond to portions of the environment being observed by a driving instructor during training using a gaze detector. During an operation, a driver assist robot may utilize the saliency map in order to assess attention of the driver, detect potential hazards, and issue alerts. Responsive to a detection of a mismatch between the driver current attention and the target attention derived from the saliency map, the robot may issue a warning, and/or prompt the driver of an upcoming hazard. A data processing apparatus may employ gaze based saliency maps in order to analyze, e.g., surveillance camera feeds for intruders, open doors, hazards, policy violations (e.g., open doors).

    Abstract translation: 机器人设备可以使用从教练的凝视得出的显着图来训练。 在导航应用中，显着图可以对应于使用注视检测器的训练期间由驾驶教练观察到的环境的部分。 在操作期间，驾驶员辅助机器人可以利用显着性图来评估驾驶员的注意力，检测潜在危险并发出警报。 响应于检测驾驶员目前的关注与从显着性图导出的目标注意之间的不匹配，机器人可能发出警告，和/或提示驾驶员即将到来的危险。 数据处理装置可以使用基于凝视的显着图，以分析例如入侵者的监视摄像机馈送，敞开门，危险，违反政策（例如，开门）。

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