An apparatus includes an interface and a processor. The interface may be configured to receive pixel data representing respective fields of view of two or more cameras arranged to obtain a predetermined field of view, where the respective fields of view of each adjacent pair of the two or more cameras overlap. The processor may be configured to process the pixel data arranged as video frames and perform a fixed pattern calibration for facilitating multi-view stitching. The fixed pattern calibration may comprise applying a pose calibration process to the video frames. The pose calibration process generally uses (i) intrinsic parameters, a respective translate vector, a respective rotation matrix, and distortion parameters for each lens of the two or more cameras and (ii) a calibration board to obtain configuration parameters for the respective fields of view of the two or more cameras. The pose calibration process may comprise changing a z value of the respective translate vector for each lens of the two or more cameras to at least one of a middle distance value and a long distance value while maintaining the respective rotation matrix for each lens of the two or more cameras unchanged.

An apparatus includes an interface and a processor. The interface may be configured to receive pixel data representing respective fields of view of two or more cameras arranged to obtain a predetermined field of view, where the respective fields of view of each adjacent pair of the two or more cameras overlap. The processor may be configured to process the pixel data arranged as video frames and perform a fixed pattern calibration for facilitating multi-view stitching. The fixed pattern calibration may comprise applying a pose calibration process to the video frames. The pose calibration process generally uses (i) intrinsic parameters, a respective translate vector, a respective rotation matrix, and distortion parameters for each lens of the two or more cameras and (ii) a calibration board to obtain configuration parameters for the respective fields of view of the two or more cameras. The pose calibration process may comprise changing a z value of the respective translate vector for each lens of the two or more cameras to at least one of a middle distance value and a long distance value while maintaining the respective rotation matrix for each lens of the two or more cameras unchanged.

A system for determining a distance to a region of interest. The system may be used to adjust focus of a motion picture camera. The system may include a first camera configured to have a first field of view, and a second camera configured to have a second field of view that overlaps at least a portion of the first field of view. The system may include a processor configured to calculate a distance of the selected region of interest relative to a location by comparing a position of the selected region of interest in the first field of view with a position of the selected region of interest in the second field of view.

A system for generating three-dimensional (3D) images from captured images of a target when executing digital magnification. A controller executes a digital magnification on the first image of the target captured by the first image sensor and on the second image captured by the second image sensor of the target. The controller crops the first image and the second image to overlap a first portion of the target captured by the first image sensor with a second portion of the target captured by the second image sensor. The controller adjusts the cropping of the first image and the second image to provide binocular overlap of the first portion of the target with the second portion of target. The displayed cropped first image and the cropped second image display the 3D image at the digital magnification to the user.

Three-dimensional image calibration and presentation for eyewear including a pair of image capture devices is described. Calibration and presentation includes obtaining a calibration offset to accommodate flexure in the support structure for the eyewear, adjusting a three-dimensional rendering offset by the obtained calibration offset, and presenting the stereoscopic images using the three-dimension rendering offset.

Three-dimensional image calibration and presentation for eyewear including a pair of image capture devices is described. Calibration and presentation includes obtaining a calibration offset to accommodate flexure in the support structure for the eyewear, adjusting a three-dimensional rendering offset by the obtained calibration offset, and presenting the stereoscopic images using the three-dimension rendering offset.

A stereoscopic camera with fluorescence visualization is disclosed. An example stereoscopic camera includes a visible light source, a near-infrared light source, and a near-ultraviolet light source. The stereoscopic camera also includes a light filter assembly having left and right filter magazines positioned respectively along left and right optical paths and configured to selectively enable certain wavelengths of light to pass through. Each of the left and right filter magazines includes an infrared cut filter, a near-ultraviolent cut filter, and a near-infrared bandpass filter. A controller of the camera is configured to provide for a visible light mode, an indocyanine green (“ICG”) fluorescence mode, and a 5-aminolevulinic acid (“ALA”) fluorescence mode by synchronizing the activation of the light sources with the selection of the filters. A processor of the camera combines image data from the different modes to enable fluorescence emission light to be superimposed on visible light stereoscopic images.

A stereoscopic camera with fluorescence visualization is disclosed. An example stereoscopic camera includes a visible light source, a near-infrared light source, and a near-ultraviolet light source. The stereoscopic camera also includes a light filter assembly having left and right filter magazines positioned respectively along left and right optical paths and configured to selectively enable certain wavelengths of light to pass through. Each of the left and right filter magazines includes an infrared cut filter, a near-ultraviolent cut filter, and a near-infrared bandpass filter. A controller of the camera is configured to provide for a visible light mode, an indocyanine green (“ICG”) fluorescence mode, and a 5-aminolevulinic acid (“ALA”) fluorescence mode by synchronizing the activation of the light sources with the selection of the filters. A processor of the camera combines image data from the different modes to enable fluorescence emission light to be superimposed on visible light stereoscopic images.

Systems and methods for implementing one or more autonomous features for autonomous and semi-autonomous control of one or more vehicles are provided. More specifically, image data may be obtained from an image acquisition device and processed utilizing one or more machine learning models to identify, track, and extract one or more features of the image utilized in decision making processes for providing steering angle and/or acceleration/deceleration input to one or more vehicle controllers. In some instances, techniques may be employed such that the autonomous and semi-autonomous control of a vehicle may change between vehicle follow and lane follow modes. In some instances, at least a portion of the machine learning model may be updated based on one or more conditions.

Systems and methods for implementing one or more autonomous features for autonomous and semi-autonomous control of one or more vehicles are provided. More specifically, image data may be obtained from an image acquisition device and processed utilizing one or more machine learning models to identify, track, and extract one or more features of the image utilized in decision making processes for providing steering angle and/or acceleration/deceleration input to one or more vehicle controllers. In some instances, techniques may be employed such that the autonomous and semi-autonomous control of a vehicle may change between vehicle follow and lane follow modes. In some instances, at least a portion of the machine learning model may be updated based on one or more conditions.