A first six degree-of-freedom (DoF) pose of an object from a perspective of a first image sensor is determined with a neural network. A second six DoF pose of the object from a perspective of a second image sensor is determined with the neural network. A pose offset between the first and second six DoF poses is determined. A first projection offset is determined for a first two-dimensional (2D) bounding box generated from the first six DoF pose. A second projection offset is determined for a second 2D bounding box generated from the second six DoF pose. A total offset is determined by combining the pose offset, the first projection offset, and the second projection offset. Parameters of a loss function are updated based on the total offset. The updated parameters are provided to the neural network to obtain an updated total offset.

Embodiments of the invention provide a camera array imaging architecture that computes depth maps for objects within a scene captured by the cameras, and use a near-field sub-array of cameras to compute depth to near-field objects and a far-field sub-array of cameras to compute depth to far-field objects. In particular, a baseline distance between cameras in the near-field subarray is less than a baseline distance between cameras in the far-field sub-array in order to increase the accuracy of the depth map. Some embodiments provide an illumination near-IR light source for use in computing depth maps.

Methods, systems, and computer-readable media for detecting image degradation during a surgical procedure are provided. A method includes receiving images of a surgical instrument; obtaining baseline images of an edge of the surgical instrument; comparing a characteristic of the images of the surgical instrument to a characteristic of the baseline images of the edge of the surgical instrument, the images of the surgical instrument being received subsequent to obtaining the baseline images of the edge of the surgical instrument and being received while the surgical instrument is disposed at a surgical site in a patient; determining whether the images of the surgical instrument are degraded, based on the comparing of the characteristic of the images of the surgical instrument and the characteristic of the baseline images of the surgical instrument; and generating an image degradation notification, in response to a determination that the images of the surgical instrument are degraded.

A method and a system for intelligently controlling children's usage of a screen terminal are provided in this disclosure, which relate to the technical field of intelligent home terminals. When children use the screen terminal, children's age can be automatically and intelligently identified, real-time intelligent supervision on children's sitting posture, distance and other aspects can be conducted according to different children's ages, so as to intelligently control on and off duration of the screen terminal, so as to guide the children to use the screen terminal device healthily. Compared with prior art schemes, in the disclosure, management of the screen terminal device can be realized without manual operation, reducing trouble from manual equipment management, further realizing specific control of children's usage of the screen terminal by age, increasing intelligence degree, and with advantage of being used in multiple scenes.

An estimation step according to an embodiment causes a computer to execute: a calculation step of using a plurality of images obtained by a plurality of imaging devices imaging a three-dimensional space in which a plurality of objects reside, to calculate representative points of pixel regions representing the objects among pixel regions of the images; a position estimation step of estimating positions of the objects in the three-dimensional space, based on the representative points calculated by the calculation step; an extraction step of extracting predetermined feature amounts from image regions representing the objects; and an attitude estimation step of estimating attitudes of the objects in the three-dimensional space, through a preliminarily learned regression model, using the positions estimated by the position estimation step, and the feature amounts extracted by the extraction step.

The present disclosure provides methods and systems directed to performing real-time and/or AI-assisted radiology. A method for processing an image of a location of a body of a subject may comprise (a) obtaining the image of the location of a body of the subject; (b) using a trained algorithm to classify the image or a derivative thereof to a category among a plurality of categories, wherein the classifying comprises applying an image processing algorithm; (c) directing the image to a first radiologist for radiological assessment if the image is classified to a first category among the plurality of categories, or (ii) directing the image to a second radiologist for radiological assessment, if the image is classified to a second category among the plurality of categories; and (d) receiving a recommendation from the first or second radiologist to examine the subject based at least in part on the radiological assessment.

A long-baseline and long depth-range stereo vision system is provided that is suitable for use in non-rigid assemblies where relative motion between two or more cameras of the system does not degrade estimates of a depth map. The stereo vision system may include a processor that tracks camera parameters as a function of time to rectify images from the cameras even during fast and slow perturbations to camera positions. Factory calibration of the system is not needed, and manual calibration during regular operation is not needed, thus simplifying manufacturing of the system.

A three-dimensional model generation method executed by an information processing device includes: obtaining images generated by shooting a subject from respective viewpoints; searching for a similar point that is similar to a first point in a first image among the images, from second points in a search area in a second image different from the first image, the search area being provided based on the first point; calculating an accuracy of a search result of the searching, using degrees of similarity between the first point and the respective second points; and generating a three-dimensional model using the search result and the accuracy.

A contactless touchscreen interface has a digital display to display digital information; the proximity detector and a proximity detector comprising an image sensor to detect user interaction at a virtual touch intersection plane offset a distance from the digital display and to resolve the interaction into XY offset-plane interaction coordinates with reference to the digital display. A gaze determining imaging system comprising an image sensor determines a gaze relative offset with respect to the digital display using facial image data captured by the image sensor. An interface controller comprising a parallax adjustment controller to convert the XY offset-plane interaction coordinates to XY on-screen apparent coordinates using the gaze relative offset and the distance and an input controller generates an input at the XY on-screen apparent coordinates accordingly.

A novel disparity computation technique is presented which comprises multiple orthogonal disparity maps, generated from approximately orthogonal decomposition feature spaces, collaboratively generating a composite disparity map. Using an approximately orthogonal feature set extracted from such feature spaces produces an approximately orthogonal set of disparity maps that can be composited together to produce a final disparity map. Various methods for dimensioning scenes and are presented. One approach extracts the top and bottom vertices of a cuboid, along with the set of lines, whose intersections define such points. It then defines a unique box from these two intersections as well as the associated lines. Orthographic projection is then attempted, to recenter the box perspective. This is followed by the extraction of the three-dimensional information that is associated with the box, and finally, the dimensions of the box are computed. The same concepts can apply to hallways, rooms, and any other object.