Intuitive Surgical Operations, Inc.

Xilinx, Inc.

Geoffrey A Richmond

Brian David Hoffman

Siddarth Sen

Rafael C Camarota

Khaldoon S Abugharbieh

David L Ferguson

Ying Cao

A system may access an image that is captured by an imaging device and that depicts an operational scene illuminated by close-range light. The system may also access a depth map of the operational scene. Based on the image and the depth map, the system may generate a processed image depicting the operational scene as being illuminated by a virtual light source that is to be simulated to be illuminating the operational scene and may provide the processed image for presentation on a display screen. Corresponding systems and methods are also disclosed.

Systems and methods for emulating far-range lighting for an operational scene illuminated by close-range light

An exemplary surgical instrument tracking system includes at least one physical computing device that determines, based on endoscopic imagery of a surgical area and using a trained neural network, an observation for an object of interest depicted in the endoscopic imagery, associates, based on a probabilistic framework and kinematics of a robotically-manipulated surgical instrument located at the surgical area, the observation for the object of interest to the robotically-manipulated surgical instrument, and determines a physical position of the robotically-manipulated surgical instrument at the surgical area based on the kinematics of the robotically-manipulated surgical instrument and the observation associated with the robotically-manipulated surgical instrument.

Systems and methods for tracking a position of a robotically-manipulated surgical instrument

An imaging system is provided for pixel-level segmentation of images comprising: a camera to capture images of an anatomical object and to represent the images in two-dimensional (2D) arrangements of pixels; one or more processors and a non-transitory computer readable medium with information including: CNN instructions to cause the one or more processors to implement a CNN configured to associate anatomical object classifications with pixels of the 2D arrangements of pixels; and multiple sets of weights, to differently configure the CNN based upon different camera image training data; and a display screen configured to display the two-dimensional (2D) arrangements of classified pixels and the anatomical object classifications.

Convolutional neural networks for efficient tissue segmentation

A system may access an image that is captured by an imaging device and that depicts an operational scene illuminated by close-range light. The system may also access a depth map of the operational scene that includes depth data corresponding to each pixel in the image. Based on the depth map, the system may determine a far-range lighting coefficient for each pixel in the image based on a relationship between the corresponding depth data included in the depth map for that respective pixel and a target distance to a virtual light source that is to be simulated to be illuminating the operational scene. Based on the image and these far-range lighting coefficients, the system may generate a processed image depicting the operational scene as being illuminated by the virtual light source and may provide the processed image for presentation on a display screen. Corresponding systems and methods are also disclosed.

A lighting emulation system accesses an image that is captured by an imaging device and that depicts an operational scene illuminated by close-range light. The lighting emulation system accesses a depth map of the operational scene that includes depth data corresponding to each pixel in the image. Based on the depth map, the lighting emulation system determines a far-range lighting coefficient for each pixel in the image. Specifically, the far-range lighting coefficient for each respective pixel is determined based on the corresponding depth data included in the depth map for that respective pixel. Based on the image and the far-range lighting coefficient for each pixel in the image, the lighting emulation system generates a processed image depicting the operational scene as illuminated by far-range lighting, and provides the processed image for presentation on a display screen.

A method of characterizing a die can include correlating, using a processor, a static voltage profile of a die under test in wafer form with a plurality of test static voltage profiles. The plurality of test static voltage profiles can be associated with dynamic performance profiles. The method further can include predicting dynamic performance of the die under test according to the dynamic performance profile associated with a test static voltage profile that is correlated with the static voltage profile.

Predicting performance of an integrated circuit

An embodiment of a method for providing a bandgap voltage is described. In such an embodiment, current density of a current in a bandgap circuit is shifted into a current density range having at least a substantially stable scaling factor to enhance temperature stability of the bandgap voltage, and the bandgap voltage output is moved to a target voltage.

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San Jose, CA, United States of America

Geoffrey A Richmond