A fiducial for use in the manufacture of a fabric article includes a hole through a layer of fabric. Another layer of fabric of the fabric article overlays and obscures the fiducial. The fiducial is detected by transmitting electromagnetic radiation through the fabric article. The electromagnetic radiation may make a single pass directly through the fabric article to a sensor, or may pass through the fabric article, be reflected off a surface, and pass back through the fabric article to the sensor.

The present invention relates to an apparatus (10) for diagnostic image acquisition, comprising: an input unit (20); a processing unit (30); and an output unit (40). The input unit is configured to receive a data value relating to at least one biomarker in a measurement blood sample of a patient. The processing unit is configured to determine a time to acquire a diagnostic image of the patient, wherein the determination comprises utilization of the data value. The output unit is configured to output an indication of the time to acquire the diagnostic image of the patient.

A snapshot hyperspectral imaging method includes the steps of: S1, selecting a set of reference wavelengths for calibration, rectifying the shifted positions due to dispersion at each reference wavelength, and selecting a center wavelength; S2, estimating relative dispersion at each reconstructed wavelength with respect to the center wavelength; S3, generating a dispersion matrix describing the direction of dispersion, and generating a spectral response matrix using a spectral response curve of a sensor; S4, capturing images blurred with dispersion; S5, deblurring the dispersed images captured in S4 using the dispersion matrix and the spectral response matrix generated in S3 to obtain spectral data spatially aligned in all spectrums; and S6, projecting the aligned spectral data obtained in S5 into color space, extracting a foreground image by a threshold method, sampling the dispersed images obtained in S4 as strong prior constraints for the foreground image, and reconstructing accurate spatial hyperspectral data.

The disclosure provides a dynamic interaction-oriented subject's limb time-varying stiffness identification method and device. The method includes: the combination of subject's limb displacement and measured force data or the combination of angle and measured torque data is collected; based on the time-varying dynamic system constructed based on a second-order impedance model, the linear parameter varying method is utilized to substitute the time-varying impedance parameters and reconstruct the restoring force/torque expression; iterative identification is performed on variable weights, dynamic interaction force/torque, and restoring force/torque by using time-varying dynamic parameters based on the dynamic interaction force/torque expression expanded from basis function; the time-varying stiffness is solved by using variable weights and dynamic interaction force/torque according to expression with substituted the time-varying impedance parameters. The disclosure not only improves the accuracy of the time-varying stiffness identification technology but also expands the application scenarios of the time-varying stiffness identification technology.

A depth image of an object can be input to a deep neural network to determine a first four degree-of-freedom pose of the object. The first four degree-of-freedom pose and a three-dimensional model of the object can be input to a silhouette rendering program to determine a first two-dimensional silhouette of the object. A second two-dimensional silhouette of the object can be determined based on thresholding the depth image. A loss function can be determined based on comparing the first two-dimensional silhouette of the object to the second two-dimensional silhouette of the object. Deep neural network parameters can be optimized based on the loss function and the deep neural network can be output.

A device includes: an image sensor to capture images of an object within an FOV, a guide projector and a processor. The processor is to analyze some of the images to detect entry of an encoded data marking carried by the object into the FOV, and in response to detecting such entry into the FOV: operate the guide projector to project a visual guide onto the object to guide movement of the encoded data marking to a first location indicated by the visual guide within the FOV; analyze more of the images to detect such movement to the first location, and then to a second location within the FOV; and in response to the movement to the second location, interpret the movement to the second location as receipt of manual input; and in response to the manual input, transmit data decoded from the encoded data marking to another device.

Systems and methods for image space control of a medical instrument are provided. In one example, a system is configured to display a two-dimensional medical image including a view of at least a distal end of an instrument. The system can determine, based on one or more fiducials on the instrument, a roll estimate of the instrument. The system further can receive a user input comprising a heading command to change a heading of the instrument within a plane of the medical image, or an incline command to change an incline of the instrument into or out of the plane of the medical image. Based on the roll estimate and the user input, the system can generate one or more motor commands configured to cause a robotic system coupled to the medical instrument to move the robotic medical instrument.

The invention relates to a computer-implemented method of tracking animals is provided. Such a method typically includes recognizing, by using at least one data processor, individual animals in images of a plurality of the animals; and tracking the animals using a probabilistic tracking-by-detection process. In another aspect, a system for recognizing animals is provided. Such a system typically includes an instance detection and part localization module; a visual marker classification module; a fixed-cardinality track interpolation module; and a maximum a posteriori estimation of animal identity module.

A digital twin modeling method to assemble a robotic teleoperation environment, including: capturing images of the teleoperation environment; identifying a part being assembled; querying the assembly assembling order to obtain a list of assembled parts according to the part being assembled; generating a three-dimensional model of the current assembly from the list and calculating position pose information of the current assembly in an image acquisition device coordinate system; loading a three-dimensional model of the robot, determining a coordinate transformation relationship between a robot coordinate system and an image acquisition device coordinate system; determining position pose information of the robot in an image acquisition device coordinate system from the coordinate transformation relationship; determining a relative positional relationship between the current assembly and the robot from position pose information of the current assembly and the robot in an image acquisition device coordinate system; establishing a digital twin model of the teleoperation environment.

A system and a method of position and orientation determination use an image capturing device and position and orientation sensors in user equipment such as a head mounted device “HMD”. The method may comprise receiving position measurements from the position sensor and receiving a selection of one or more anchors based on said position measurements including the position of each anchor. The position and orientation measurements may be used to determine whether any selected anchor is visible to said image capturing device based on said position and orientation measurements. Then the image capturing device may be activated to capture an image including said one or more anchors when a selected anchor is visible. The image may be analyzed to determine the position and orientation of the image capturing device relative to the one or more anchors.