Brain Corporation

Gopro, Inc.

Dimitry Fisher

Eugene Izhikevich

Filip Lukasz Piekniewski

Micah Richert

Jean-Baptiste Passot

Vadim Polonichko

Heathcliff Hatcher

Philip Meier

John Black

Jayram Moorkanikara Nageswaran

Cody Griffin

Chance Roth

Steven Hypnarowski

Botond Szatmary

Csaba Petre

Marius Buibas

Patryk Laurent

Michah Richert

Apparatus and methods for identification of a coded pattern visible to a computerized imaging apparatus while invisible or inconspicuous to human eyes. A pattern and/or marking may serve to indicate identity of an object, and/or the relative position of the pattern to a viewer. While some solutions exist for identifying patterns (for example, QR codes), they may be visually obtrusive to a human observer due to visual clutter. In exemplary implementations, apparatus and methods are capable of generating patterns with sufficient structure to be used for either discrimination or some aspect of localization, while incorporating spectral properties that are more aesthetically acceptable such as being: a) imperceptible or subtle to the human observer and/or b) aligned to an existing acceptable visual form, such as a logo. In one variant, a viewer comprises an imaging system comprised as a processor and laser scanner, or camera, or moving photodiode.

Autonomous vehicle control method, system, and medium

Systems and methods for automatic detection of spills are disclosed. In some exemplary implementations, a robot can have a spill detector comprising at least one optical imaging device configured to capture at least one image of a scene containing a spill while the robot moves between locations. The robot can process the at least one image by segmentation. Once the spill has been identified, the robot can then generate an alert indicative at least in part of a recognition of the spill.

Systems and methods for automatic detection of spills

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.

Apparatus and methods for tracking salient features

Systems and methods for predictive/reconstructive visual object tracking are disclosed. The visual object tracking has advanced abilities to track objects in scenes, which can have a variety of applications as discussed in this disclosure. In some exemplary implementations, a visual system can comprise a plurality of associative memory units, wherein each associative memory unit has a plurality of layers. The associative memory units can be communicatively coupled to each other in a hierarchical structure, wherein data in associative memory units in higher levels of the hierarchical structure are more abstract than lower associative memory units. The associative memory units can communicate to one another supplying contextual data.

Systems and methods for predictive/reconstructive visual object tracker

Apparatus and methods for saliency detection based on color occurrence analysis

Apparatus and methods for computerized object identification

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.

Trainable modular robotic apparatus and methods

Trainable modular robotic apparatus

Salient features tracking apparatus and methods using visual initialization

Growing community of inventors

San Diego, CA, United States of America

Dimitry Fisher