Abstract: A computer-implemented method for detecting at least one feature of interest in images captured with an imaging device includes: receiving an ordered set of images and analyzing one or more subsets of the ordered set using a local spatio-temporal processing module. The local spatio-temporal processing module determines presence of characteristics related to the feature of interest in each image of each subset of images and annotates the subset of images. The method also includes processing a set of feature vectors of the ordered set of images using a global spatio-temporal processing module to refine the determined characteristics associated with each subset of images, and calculate one or more values for each image using a timeseries analysis module, the values being representative of the feature of interest and calculated using the refined characteristics associated with each subset of images and spatio-temporal information.

Abstract: A computer-implemented method for detecting at least one feature of interest in images captured with an imaging device includes: receiving an ordered set of images and analyzing one or more subsets of the ordered set using a local spatio-temporal processing module. The local spatio-temporal processing module determines presence of characteristics related to the feature of interest in each image of each subset of images and annotates the subset of images. The method also includes processing a set of feature vectors of the ordered set of images using a global spatio-temporal processing module to refine the determined characteristics associated with each subset of images, and calculate one or more values for each image using a timeseries analysis module, the values being representative of the feature of interest and calculated using the refined characteristics associated with each subset of images and spatio-temporal information.

Abstract: A computer-implemented method for detecting at least one feature of interest in images captured with an imaging device includes: receiving an ordered set of images and analyzing one or more subsets of the ordered set using a local spatio-temporal processing module. The local spatio-temporal processing module determines presence of characteristics related to the feature of interest in each image of each subset of images and annotates the subset of images. The method also includes processing a set of feature vectors of the ordered set of images using a global spatio-temporal processing module to refine the determined characteristics associated with each subset of images, and calculate one or more values for each image using a timeseries analysis module, the values being representative of the feature of interest and calculated using the refined characteristics associated with each subset of images and spatio-temporal information.

Abstract: A computer-implemented system is provided that includes at least one processor that is adapted to analyze a plurality of frames from a real-time video to identify frames during which an operator is interacting with an image device to examine areas of a patient. The at least one processor is further configured to generate, from the identified frames, data representations of a first area examined by the operator interacting with the image device and further generate data representations of one or more further areas examined by the operator interacting with the image device.

Abstract: A computer-implemented system is provided that receives a real-time video captured from a medical image device during a medical procedure. The real-time video may include a plurality of frames. The system may be adapted to detect an object of interest in the plurality of frames and apply one or more neural networks configured to identify a plurality of characteristics of the detected object of interest, such as classification, size, and/or location. In some embodiments, the system is adapted to identify, based on one or more of the plurality of characteristics, a medical guideline and present, in real-time on a display device during the medical procedure, information for the medical guideline.

Abstract: A method for a computing device to recalibrate a multiple camera system includes collecting calibration data from one or more pictures captured by the multiple camera system of the computing device. The multiple camera system includes two or more cameras that are each physically separated by a distance from one another. The method further includes detecting decalibration of the camera system. The method further includes, when the camera system is decalibrated, generating recalibration parameters based on the calibration data. The method further includes determining whether the recalibration parameters are valid parameters and, when they are, updating the multiple camera system based on the recalibration parameters.

Abstract: A method for capturing a depth map includes: controlling a plurality of cameras to capture, concurrently, a plurality of first images during a first exposure interval, each of the cameras concurrently capturing a corresponding one of the first images, the cameras having overlapping fields of view; controlling a projection source to emit light at a first illumination level during the first exposure interval; controlling the cameras to capture, concurrently, a plurality of second images during a second exposure interval, each of the cameras concurrently capturing a corresponding one of the second images; controlling the projection source to emit light at a second illumination level during the second exposure interval, the second illumination level being different from the first illumination level; combining the first images with the second images to generate a depth map; and outputting the depth map.

Abstract: A method for detecting defects in objects includes: controlling, by a processor, one or more depth cameras to capture a plurality of depth images of a target object; computing, by the processor, a three-dimensional (3-D) model of the target object using the depth images; rendering, by the processor, one or more views of the 3-D model; computing, by the processor, a descriptor by supplying the one or more views of the 3-D model to a convolutional stage of a convolutional neural network; supplying, by the processor, the descriptor to a defect detector to compute one or more defect classifications of the target object; and outputting the one or more defect classifications of the target object.

Abstract: A method for detecting a defect in an object includes: capturing, by one or more depth cameras, a plurality of partial point clouds of the object from a plurality of different poses with respect to the object; merging, by a processor, the partial point clouds to generate a merged point cloud; computing, by the processor, a three-dimensional (3D) multi-view model of the object; detecting, by the processor, one or more defects of the object in the 3D multi-view model; and outputting, by the processor, an indication of the one or more defects of the object.

Abstract: A method for detecting decalibration of a depth camera system including a first, second, and third cameras having overlapping fields of view in a direction includes: detecting a feature in a first image captured by the first camera; detecting the feature in a second image captured by the second camera; detecting the feature in a third image captured by the third camera, the third camera being non-collinear with the first and second cameras; identifying a first conjugate epipolar line in the second image in accordance with a detected location of the feature in the first image and calibration parameters; identifying a second conjugate epipolar line in the second image in accordance with a detected location of the feature in the third image and the calibration parameters; and calculating a difference between a detected location of the feature in the second image and the first and second conjugate epipolar lines.

Abstract: A method for detecting decalibration of a depth camera system including a first, second, and third cameras having overlapping fields of view in a direction includes: detecting a feature in a first image captured by the first camera; detecting the feature in a second image captured by the second camera; detecting the feature in a third image captured by the third camera, the third camera being non-collinear with the first and second cameras; identifying a first conjugate epipolar line in the second image in accordance with a detected location of the feature in the first image and calibration parameters; identifying a second conjugate epipolar line in the second image in accordance with a detected location of the feature in the third image and the calibration parameters; and calculating a difference between a detected location of the feature in the second image and the first and second conjugate epipolar lines.

Abstract: A method for detecting decalibration of a depth camera system including a first, second, and third cameras having overlapping fields of view in a direction includes: detecting a feature in a first image captured by the first camera; detecting the feature in a second image captured by the second camera; detecting the feature in a third image captured by the third camera, the third camera being non-collinear with the first and second cameras; identifying a first conjugate epipolar line in the second image in accordance with a detected location of the feature in the first image and calibration parameters; identifying a second conjugate epipolar line in the second image in accordance with a detected location of the feature in the third image and the calibration parameters; and calculating a difference between a detected location of the feature in the second image and the first and second conjugate epipolar lines.

Abstract: A method for detecting decalibration of a depth camera system including a first, second, and third cameras having overlapping fields of view in a direction includes: detecting a feature in a first image captured by the first camera; detecting the feature in a second image captured by the second camera; detecting the feature in a third image captured by the third camera, the third camera being non-collinear with the first and second cameras; identifying a first conjugate epipolar line in the second image in accordance with a detected location of the feature in the first image and calibration parameters; identifying a second conjugate epipolar line in the second image in accordance with a detected location of the feature in the third image and the calibration parameters; and calculating a difference between a detected location of the feature in the second image and the first and second conjugate epipolar lines.

Abstract: A method for capturing a depth map includes: controlling a plurality of cameras to capture, concurrently, a plurality of first images during a first exposure interval, each of the cameras concurrently capturing a corresponding one of the first images, the cameras having overlapping fields of view; controlling a projection source to emit light at a first illumination level during the first exposure interval; controlling the cameras to capture, concurrently, a plurality of second images during a second exposure interval, each of the cameras concurrently capturing a corresponding one of the second images; controlling the projection source to emit light at a second illumination level during the second exposure interval, the second illumination level being different from the first illumination level; combining the first images with the second images to generate a depth map; and outputting the depth map.

Abstract: A method for a computing device to recalibrate a multiple camera system includes collecting calibration data from one or more pictures captured by the multiple camera system of the computing device. The multiple camera system includes two or more cameras that are each physically separated by a distance from one another. The method further includes detecting decalibration of the camera system. The method further includes, when the camera system is decalibrated, generating recalibration parameters based on the calibration data. The method further includes determining whether the recalibration parameters are valid parameters and, when they are, updating the multiple camera system based on the recalibration parameters.