A gallery view or a virtual space view is provided in an online meeting user interface associated with a videoconferencing system. The gallery view or virtual space view displays video feeds of meeting participants on their respective participant computers. The video feeds are camera-captured views of each of the meeting participants. The videoconferencing system receives an electronic request from one or more meeting participants via the meeting participant's respective participant computer to either display a mirrored view of the video feed of the meeting participant to all meeting participants in the online meeting, or display an unmirrored view of the video feed of the meeting participant to all meeting participants in the online meeting. A video processor associated with the videoconferencing system creates the respective mirrored or unmirrored view of the video feed of each of the meeting participants whose participant computer sent the electronic request. The videoconferencing system then generates instructions for a gallery view or virtual space view in the online meeting user interface using the mirrored or unmirrored view of the video feeds created by the video processor, and transmits instructions to display the gallery view or virtual space view to all meeting participants on their respective participant computers.

The invention relates to a method of inserting an insertion object into a sequence of images. The insertion object may be an image, a video, or a three-dimensional model, which could possibly be animated. Particularly, but not exclusively, the invention relates to the insertion of advertisement images into video, such as videos of sporting events. A method comprises capturing a sequence of images, the sequence of images comprising in order a first image, a second image, and a third image; estimating a first homographic transform from the first image to the third image; deriving a second homographic transform from the first image to the second image based on the first homographic transform; transforming the insertion object using the first homographic transformation to form a first warped insertion image, and inserting the first warped insertion image into the third image of the sequence of images; and transforming the insertion object using the second homographic transformation to form a second warped insertion image, and inserting the second warped insertion image into the second image of the sequence of images.

The invention relates to a method of inserting an insertion object into a sequence of images. The insertion object may be an image, a video, or a three-dimensional model, which could possibly be animated. Particularly, but not exclusively, the invention relates to the insertion of advertisement images into video, such as videos of sporting events. A method comprises capturing a sequence of images, the sequence of images comprising in order a first image, a second image, and a third image; estimating a first homographic transform from the first image to the third image; deriving a second homographic transform from the first image to the second image based on the first homographic transform; transforming the insertion object using the first homographic transformation to form a first warped insertion image, and inserting the first warped insertion image into the third image of the sequence of images; and transforming the insertion object using the second homographic transformation to form a second warped insertion image, and inserting the second warped insertion image into the second image of the sequence of images.

An image stitching method is proposed to include: A) acquiring a plurality of segment images for a target scene, each of the segment images containing a part of a target scene; B) for two adjacent segment images, which are two of the segment images that have overlapping fields of view, comparing the two adjacent segment images to determine a stitching position for the two adjacent segment images from a common part of the overlapping fields of view; and C) stitching the two adjacent images together based on the stitching position thus determined.

An image stitching method is proposed to include: A) acquiring a plurality of segment images for a target scene, each of the segment images containing a part of a target scene; B) for two adjacent segment images, which are two of the segment images that have overlapping fields of view, comparing the two adjacent segment images to determine a stitching position for the two adjacent segment images from a common part of the overlapping fields of view; and C) stitching the two adjacent images together based on the stitching position thus determined.

Methods and apparatus to determine the dimensions of a region of interest of a target object and a class of the target object from an image using target object landmarks are disclosed herein. An example method includes identifying a landmark of a target object in an image based on a match between the landmark and a template landmark; classifying a target object based on the identified landmark; projecting dimensions of the template landmark based on a location of the landmark in the image; and determining a region of interest based on the projected dimensions, the region of interest corresponding to text printed on the target object.

Methods and apparatus to determine the dimensions of a region of interest of a target object and a class of the target object from an image using target object landmarks are disclosed herein. An example method includes identifying a landmark of a target object in an image based on a match between the landmark and a template landmark; classifying a target object based on the identified landmark; projecting dimensions of the template landmark based on a location of the landmark in the image; and determining a region of interest based on the projected dimensions, the region of interest corresponding to text printed on the target object.

For displaying a window in an augmented reality view, a processor detects a new augmented reality placetime that includes a new augmented reality position and/or a new augmented reality time of an augmented reality device. The processor calculates new window characteristics for a window at the new augmented reality placetime based on previous window characteristics. The processor further displays the window with the new window characteristics.

For displaying a window in an augmented reality view, a processor detects a new augmented reality placetime that includes a new augmented reality position and/or a new augmented reality time of an augmented reality device. The processor calculates new window characteristics for a window at the new augmented reality placetime based on previous window characteristics. The processor further displays the window with the new window characteristics.

This machine-learning device is provided with: a detection unit which detects a loss of consistency with a lapse of time in a determination result for unit data, the determination result being output from a determination unit that generates a learning model to be used when performing prescribed determination for one or more pieces of the unit data that form time series data; and a selection unit which selects, on the basis of the result of detection by the detection unit, unit data to be used as teacher data when the determination unit updates the learning model, thereby efficiently raising the accuracy of the learning model when machine learning is performed on the basis of the time series data.