Abstract: A method for generating an augmented reality image from first and second images, wherein at least a portion of at least one of the first and the second image is captured from a real scene, identifies a confidence region in which a confident determination as to which of the first and second image to render in that region of the augmented reality image can be made, and identifies an uncertainty region in which it is uncertain as to which of the first and second image to render in that region of the augmented reality image. At least one blending factor value in the uncertainty region is determined based upon a similarity between a first colour value in the uncertainty region and a second colour value in the confidence region, and an augmented reality image is generated by combining, in the uncertainty region, the first and second images using the at least one blending factor value.

Abstract: A method for generating an augmented reality image from first and second images, wherein at least a portion of at least one of the first and the second image is captured from a real scene, identifies a confidence region in which a confident determination as to which of the first and second image to render in that region of the augmented reality image can be made, and identifies an uncertainty region in which it is uncertain as to which of the first and second image to render in that region of the augmented reality image. At least one blending factor value in the uncertainty region is determined based upon a similarity between a first colour value in the uncertainty region and a second colour value in the confidence region, and an augmented reality image is generated by combining, in the uncertainty region, the first and second images using the at least one blending factor value.

Abstract: A method of analyzing one or more objects in a set of frames. A first frame is segmented to produce a plurality of first masks each identifying pixels belonging to a potential object-instance detected in the first frame. A first feature vector is extracted from the first frame for each potential object-instance detected therein, characterizing the potential object-instance. A second frame is segmented to produce a plurality of second masks each identifying pixels belonging to a potential object-instance detected in the second frame. A second feature vector is extracted for each potential object-instance detected in the second frame, characterizing the potential object-instance. A potential object-instance in the first frame is matched with one of the potential object-instances in the second frame.

Abstract: A method of generating a training dataset suitable for training machine learning algorithms to estimate the motion of objects, and for training a machine learning algorithm to perform motion estimation. A plurality of pairs of synthetic images are generated from obtained objects and backgrounds, each pair have a first frame and a second frame. The first frame includes a selection of objects in first positions and first orientations superimposed on a selected background, and the second frame includes the selection of objects in second positions and second orientations superimposed on the selected background. Also provided are processing systems configured to carry out these methods.

Abstract: A method for generating an augmented reality image from first and second images, wherein at least a portion of at least one of the first and the second image is captured from a real scene, identifies a confidence region in which a confident determination as to which of the first and second image to render in that region of the augmented reality image can be made, and identifies an uncertainty region in which it is uncertain as to which of the first and second image to render in that region of the augmented reality image. At least one blending factor value in the uncertainty region is determined based upon a similarity between a first colour value in the uncertainty region and a second colour value in the confidence region, and an augmented reality image is generated by combining, in the uncertainty region, the first and second images using the at least one blending factor value.

Abstract: A method of rendering an image of a 3-D scene includes rendering a noisy image at a first resolution; obtaining one or more guide channels at the first resolution, and obtaining one or more corresponding guide channels at a second resolution. The second resolution may be the same resolution as, or a higher resolution than, the first resolution. For each of a plurality of local neighbourhoods, the method comprises: calculating the parameters of a model that approximates the noisy image as a function of the one or more guide channels (at the first resolution), and applying the calculated parameters to the one or more guide channels at the second resolution, to produce a denoised image at the second resolution.

Abstract: A method for creating a metamer for an image for a display, the method comprising receiving a first input image, dividing the input image into a plurality of regions comprising a foveal region and at least one peripheral region, wherein each region of the plurality of regions comprises a plurality of pixels, determining, for each of the at least one peripheral region, the distribution of statistics associated with each of the at least one peripheral region, for each of the at least one peripheral region, identifying a metamer for the peripheral region wherein the metamer has similar and/or identical distribution of statistics to the associated peripheral region, and creating an output image based on the foveal region and the metamer for each of the at least one peripheral region such that the peripheral region of the output image is perceived to be the same as the peripheral region of the input image when perceived by a viewer of the image.

Abstract: A method of rendering an image of a 3-D scene includes rendering a noisy image and obtaining one or more guide channels. For each of a plurality of local neighborhoods, the method comprises: calculating the parameters of a model that approximates the noisy image as a function of the one or more guide channels, and applying the calculated parameters to produce a denoised image. At least one of (i) the noisy image, (ii) the one or more guide channels, and (iii) the denoised image, are stored in a quantized low-bitdepth format.

Abstract: A method of analysing objects in a first frame and a second frame is disclosed. The method includes segmenting the frames, and matching at least one object in the first frame with a corresponding object in the second frame. The method optionally includes estimating the motion of the at least one matched object between the frames. Also disclosed is a method of generating a training dataset suitable for training machine learning algorithms to estimate the motion of objects. Also provided are processing systems configured to carry out these methods.

Abstract: A method of generating a training dataset suitable for training machine learning algorithms to estimate the motion of objects, and for training a machine learning algorithm to perform motion estimation. A plurality of pairs of synthetic images are generated from obtained objects and backgrounds, each pair have a first frame and a second frame. The first frame includes a selection of objects in first positions and first orientations superimposed on a selected background, and the second frame includes the selection of objects in second positions and second orientations superimposed on the selected background. Also provided are processing systems configured to carry out these methods.

Abstract: A method of rendering an image of a 3-D scene includes rendering a noisy image and obtaining one or more guide channels. For each of a plurality of local neighborhoods, the method comprises: calculating the parameters of a model that approximates the noisy image as a function of the one or more guide channels, and applying the calculated parameters to produce a denoised image. At least one of (i) the noisy image, (ii) the one or more guide channels, and (iii) the denoised image, are stored in a quantized low-bitdepth format.

Abstract: A method of rendering an image of a 3-D scene includes rendering a noisy image; and obtaining one or more guide channels. For each of a plurality of local neighbourhoods, the method comprises: calculating the parameters of a model that approximates the noisy image as a function of the one or more guide channels, and applying the calculated parameters to produce a denoised image. Tiling is used when calculating the parameters of the model.

Abstract: A method for generating an augmented reality image from first and second images, wherein at least a portion of at least one of the first and the second image is captured from a real scene, identifies a confidence region in which a confident determination as to which of the first and second image to render in that region of the augmented reality image can be made, and identifies an uncertainty region in which it is uncertain as to which of the first and second image to render in that region of the augmented reality image. At least one blending factor value in the uncertainty region is determined based upon a similarity between a first colour value in the uncertainty region and a second colour value in the confidence region, and an augmented reality image is generated by combining, in the uncertainty region, the first and second images using the at least one blending factor value.

Abstract: A method of rendering an image of a 3-D scene includes rendering a noisy image; and obtaining one or more guide channels. For each of a plurality of local neighbourhoods, the method comprises: calculating the parameters of a model that approximates the noisy image as a function of the one or more guide channels, and applying the calculated parameters to produce a denoised image. Tiling is used when calculating the parameters of the model.

Abstract: A method of rendering an image of a 3-D scene includes rendering a noisy image at a first resolution; obtaining one or more guide channels at the first resolution, and obtaining one or more corresponding guide channels at a second resolution. The second resolution may be the same resolution as, or a higher resolution than, the first resolution. For each of a plurality of local neighbourhoods, the method comprises: calculating the parameters of a model that approximates the noisy image as a function of the one or more guide channels (at the first resolution), and applying the calculated parameters to the one or more guide channels at the second resolution, to produce a denoised image at the second resolution.

Abstract: A method of rendering an image of a 3-D scene includes rendering a noisy image and obtaining one or more guide channels. For each of a plurality of local neighborhoods, the method comprises: calculating the parameters of a model that approximates the noisy image as a function of the one or more guide channels, and applying the calculated parameters to produce a denoised image. At least one of (i) the noisy image, (ii) the one or more guide channels, and (iii) the denoised image, are stored in a quantized low-bitdepth format.

Abstract: A method for generating an augmented reality image from first and second images, wherein at least a portion of at least one of the first and the second image is captured from a real scene, identifies a confidence region in which a confident determination as to which of the first and second image to render in that region of the augmented reality image can be made, and identifies an uncertainty region in which it is uncertain as to which of the first and second image to render in that region of the augmented reality image. At least one blending factor value in the uncertainty region is determined based upon a similarity between a first colour value in the uncertainty region and a second colour value in the confidence region, and an augmented reality image is generated by combining, in the uncertainty region, the first and second images using the at least one blending factor value.

Abstract: A method of analysing objects in a first frame and a second frame is disclosed. The method includes segmenting the frames, and matching at least one object in the first frame with a corresponding object in the second frame. The method optionally includes estimating the motion of the at least one matched object between the frames. Also disclosed is a method of generating a training dataset suitable for training machine learning algorithms to estimate the motion of objects. Also provided are processing systems configured to carry out these methods.

Abstract: A method for generating an augmented reality image from first and second images, wherein at least a portion of at least one of the first and the second image is captured from a real scene, identifies a confidence region in which a confident determination as to which of the first and second image to render in that region of the augmented reality image can be made, and identifies an uncertainty region in which it is uncertain as to which of the first and second image to render in that region of the augmented reality image. At least one blending factor value in the uncertainty region is determined based upon a similarity between a first colour value in the uncertainty region and a second colour value in the confidence region, and an augmented reality image is generated by combining, in the uncertainty region, the first and second images using the at least one blending factor value.

Abstract: A method for generating an augmented reality image from first and second images, wherein at least a portion of at least one of the first and the second image is captured from a real scene, identifies a confidence region in which a confident determination as to which of the first and second image to render in that region of the augmented reality image can be made, and identifies an uncertainty region in which it is uncertain as to which of the first and second image to render in that region of the augmented reality image. At least one blending factor value in the uncertainty region is determined based upon a similarity between a first colour value in the uncertainty region and a second colour value in the confidence region, and an augmented reality image is generated by combining, in the uncertainty region, the first and second images using the at least one blending factor value.