Abstract: An automatic guided vehicle (AGV) comprising a mobile base including a drive train and configured to drive the AGV in a self-navigation mode within a facility, a first camera configured to capture first image data of objects within the facility, a second camera configured to capture second image data of objects within the facility, the second camera including a content filter, and a main control module configured to receive the first and second image data from the first and second cameras. The main control module executes a recognition neural network program. The neural network program recognizes targets in the first image data. The main control module also executes a supervisor program under user control. The supervisor program is receives the second image data and recognizes markers attached to targets in the second image data. The supervisor program produces a supervised outcome in which targets to which markers are attached are associated with categories based on user commands.

Abstract: In a device including a processor and a memory in communication with the processor is disclosed. The memory includes executable instructions that, when executed by the processor, cause the processor to control the device to perform functions of receiving, via a communication network from a device, a message and a dynamic tag associated with the message, the dynamic tag characterizing a recipient group for the message; determining that the dynamic tag is relevant to a data set including an attribute of a plurality of members corresponding to the dynamic tag; based on the dynamic tag and the attribute, identifying, from the plurality of members, a member group relevant to the dynamic tag; and sending, via the communication network, the message to a device group associated with the member group.

Abstract: An automatic guided vehicle (AGV) comprising a mobile base including a drive train and configured to drive the AGV in a self-navigation mode within a facility, a first camera configured to capture first image data of objects within the facility, a second camera configured to capture second image data of objects within the facility, the second camera including a content filter, and a main control module configured to receive the first and second image data from the first and second cameras. The main control module executes a recognition neural network program. The neural network program recognizes targets in the first image data. The main control module also executes a supervisor program under user control. The supervisor program is receives the second image data and recognizes markers attached to targets in the second image data. The supervisor program produces a supervised outcome in which targets to which markers are attached are associated with categories based on user commands.

Abstract: A Method, apparatus, device, and storage media for image processing are provided. The method include: acquiring a set of image sequences, the set of image sequences including a plurality of image sequences; determining a first similarity measurement between image sequences in the set of image sequences; dividing the set of image sequences into one or more subset of image sequences based on a first similarity measurement; and determining, in each subset of image sequences, degrees of correlation between images in one image sequence of the subset of image sequences and images in other image sequences of the subset of image sequences.

Abstract: A method and an apparatus for processing an image are provided. The method may include: acquiring a set of image sequences, the set of image sequences including a plurality of image sequence subsets divided according to similarity measurements between image sequences, each image sequence subset including a basic image sequence and other image sequence, wherein a first similarity measurement corresponding to the basic image sequence is greater than or equal to a first similarity measurement corresponding to the other image sequence; creating an original three-dimensional model using the basic image sequence; and creating a final three-dimensional model using the other image sequence based on the original three-dimensional model.

Abstract: The embodiments of the present disclosure provide a processing method and apparatus for vehicle scene sequence tracking, and a vehicle. The method includes: obtaining a current vehicle speed of a vehicle driving in a first area and at least one frame of a current image taken; determining a vehicle speed ratio interval and a length of a sequence to be tracked according to the current vehicle speed; and performing the scene sequence tracking according to the vehicle speed ratio interval, the length of the sequence to be tracked, the at least one frame of the current image, and pre-stored multiple frames of historical reference images of the vehicle, to obtain position information of the vehicle. The method ensures that an accurate speed scanning range can still be quickly found when the vehicle speed changes greatly, and the speed scanning efficiency is greatly improved when the vehicle speed is relatively stable.

Abstract: An automatic guided vehicle (AGV) comprising a mobile base including a drive train and configured to drive the AGV in a self-navigation mode within a facility, a first camera configured to capture first image data of objects within the facility, a second camera configured to capture second image data of objects within the facility, the second camera including a content filter, and a main control module configured to receive the first and second image data from the first and second cameras. The main control module executes a recognition neural network program. The neural network program recognizes targets in the first image data. The main control module also executes a supervisor program under user control. The supervisor program is receives the second image data and recognizes markers attached to targets in the second image data. The supervisor program produces a supervised outcome in which targets to which markers are attached are associated with categories based on user commands.

Abstract: In a device including a processor and a memory, the memory includes executable instructions that, when executed by the processor, cause the processor to control the device to perform functions of receiving an access control setting for granting access to an access-controlled resource and a dynamic tag characterizing a member group subject to the access control setting; accessing a data source storing member data including an attribute associated with each member, the attribute including a parameter related to a time or time period. The dynamic tag is mapped to the member data based on (1) the parameter of the attribute and (2) a time or time period associated with the dynamic tag, to identify mapped members forming the member group, wherein the mapped members identified based on a same dynamic tag vary depending on the time or time period associated with the dynamic tag, to identify the member group.

Abstract: In a device including a processor and a memory in communication with the processor is disclosed. The memory includes executable instructions that, when executed by the processor, cause the processor to control the device to perform functions of receiving, via a communication network from a device, a message and a dynamic tag associated with the message, the dynamic tag characterizing a recipient group for the message; determining that the dynamic tag is relevant to a data set including an attribute of a plurality of members corresponding to the dynamic tag; based on the dynamic tag and the attribute, identifying, from the plurality of members, a member group relevant to the dynamic tag; and sending, via the communication network, the message to a device group associated with the member group.

Abstract: The embodiments of the present disclosure provide a processing method and apparatus for vehicle scene sequence tracking, and a vehicle. The method includes: obtaining a current vehicle speed of a vehicle driving in a first area and at least one frame of current image taken; determining a vehicle speed ratio interval and a length of a sequence to be tracked according to the current vehicle speed; and performing the scene sequence tracking according to the vehicle speed ratio interval, the length of the sequence to be tracked, the at least one frame of the current image, and pre-stored multiple frames of historical reference images of the vehicle, to obtain position information of the vehicle. The method ensures that an accurate speed scanning range can still be quickly found when the vehicle speed changes greatly, and the speed scanning efficiency is greatly improved when the vehicle speed is relatively stable.

Abstract: Examples of the disclosure accurately define a gray region for automatic white balancing. Ratios corresponding to possible gray areas are white balanced before color distance metrics are calculated with respect to white points under different light sources. The color distance metrics of each point in the image data are compared with a predetermined threshold value to identify a gray region. Gray points within a captured image are identified using the gray region and input to any AWB method to accurately white balance the captured image.

Abstract: Examples of the disclosure accurately define a gray region for automatic white balancing. Ratios corresponding to possible gray areas are white balanced before color distance metrics are calculated with respect to white points under different light sources. The color distance metrics of each point in the image data are compared with a predetermined threshold value to identify a gray region. Gray points within a captured image are identified using the gray region and input to any AWB method to accurately white balance the captured image.

Abstract: Examples of the disclosure accurately define a gray region for automatic white balancing. Ratios corresponding to possible gray areas are white balanced before color distance metrics are calculated with respect to white points under different light sources. The color distance metrics of each point in the image data are compared with a predetermined threshold value to identify a gray region. Gray points within a captured image are identified using the gray region and input to any AWB method to accurately white balance the captured image.

Abstract: The present invention provides a composite anode for a battery comprising a copper current collector working electrode, at least one anode material comprising at least one of a carbon, a silicon, a conductive agent, and combinations thereof, wherein at least one anode material is deposited on a surface of the copper current collector working electrode to form the composite anode for a battery. An electrophoretic method for making this anode is provided. A lithium-ion battery having the composite anode is disclosed.

Abstract: The present invention provides a composite anode for a battery comprising a copper current collector working electrode, at least one anode material comprising at least one of a carbon, a silicon, a conductive agent, and combinations thereof, wherein at least one anode material is deposited on a surface of the copper current collector working electrode to form the composite anode for a battery. An electrophoretic method for making this anode is provided. A lithium-ion battery having the composite anode is disclosed.

Abstract: Examples of the disclosure accurately define a gray region for automatic white balancing. Ratios corresponding to possible gray areas are white balanced before color distance metrics are calculated with respect to white points under different light sources. The color distance metrics of each point in the image data are compared with a predetermined threshold value to identify a gray region. Gray points within a captured image are identified using the gray region and input to any AWB method to accurately white balance the captured image.

Abstract: Examples of the disclosure accurately define a gray region for automatic white balancing. Ratios corresponding to possible gray areas are white balanced before color distance metrics are calculated with respect to white points under different light sources. The color distance metrics of each point in the image data are compared with a predetermined threshold value to identify a gray region. Gray points within a captured image are identified using the gray region and input to any AWB method to accurately white balance the captured image.

Abstract: Embodiments relate to systems and methods for a computation-efficient image processing system architecture. Image data can be transmitted from a computer, online service, and/or other image source to an output device having a set of image processing modules in two or more image paths, including an edge detection module and a video decoding module. The edge detection module can produce edge tag output, and the video decoding module, operating in parallel, can generate decoded video output. The edge tag output and decoded video output can be transmitted to a set of downstream image processing modules, including modules for color trapping, edge smoothing, and other operations. Because earlier processing stages share information with downstream modules which require the same or related data, redundant processing can be reduced or eliminated. Complex image operations can therefore be carried out, and high-quality output can be generated, without sacrificing responsiveness.

Abstract: Examples of the disclosure accurately define a gray region for automatic white balancing. Ratios corresponding to possible gray areas are white balanced before color distance metrics are calculated with respect to white points under different light sources. The color distance metrics of each point in the image data are compared with a predetermined threshold value to identify a gray region. Gray points within a captured image are identified using the gray region and input to any AWB method to accurately white balance the captured image.

Abstract: Various exemplary embodiments relate to a method and related network node including one or more of the following: receiving a first object to be translated, wherein the first object has a first type; obtaining a second object having a second type that is different from the first type; identifying a first property of the first object, wherein the first property is associated with a first location within a hierarchy of the first object; identifying a second property of the second object; setting a value of the second property based on a value of the first property when: a name of the first property matches a name of the second property, and the first location matches a second location of the second property within a hierarchy of the second object; and making the second object available to a receiving device.