A computer-implemented method and system are described for augmenting image data of an object in an image, the method comprising receiving captured image data defining a respective plurality of augmentation values to be applied to the captured image data, storing a plurality of augmentation representations, each representation identifying a respective portion of augmentation image data, selecting one of said augmentation image data and one of said augmentation representations based on at least one colourisation parameter, determining a portion of the augmentation image data to be applied based on the selected augmentation representation, augmenting the captured image data by applying said determined portion of the augmentation image data to the corresponding portion of the captured image data, and outputting the augmented captured image data.

A method and a system for patch-based scene segmentation using neural networks are presented. In an embodiment, a method comprises: using one or more computing devices, receiving a digital image comprising test image; using the one or more computing devices, creating, based on the test image, a plurality of grid patches; using the one or more computing devices, receiving a plurality of classifiers that have been trained to identify one or more materials of a plurality of materials; using the one or more computing devices, for each patch of the plurality of grid patches, labelling each pixel of a patch with a label obtained by applying, to the patch, one or more classifiers from the plurality of classifiers; using the one or more computing devices, generating, based on labels assigned to pixels of the plurality of grid patches, a grid of labels for the test image.

A system for adding persistence to SPAD imagery is configurable to capture, using a SPAD array, a plurality of image frames. The system is configurable to capture, using an IMU, pose data associated with the plurality of image frames. The pose data includes at least respective pose data associated with each of the plurality of image frames. The system is configurable to determine a persistence term based on the pose data. The system is also configurable to generate a composite image based on the plurality of image frames, the respective pose data associated with each of the plurality of image frames, and the persistence term. The persistence term defines a contribution of each of the plurality of image frames to the composite image.

The present disclosure provides a method and a device for generating a 3D scene map, a related apparatus and a storage medium. The method includes the following. At least two frames of point cloud data collected by a collection device is obtained. Data registration is performed on the at least two frames of point cloud data. First type of point cloud data corresponding to a movable obstacle is deleted from each frame of point cloud data and each frame of point cloud data is merged to obtain an initial scene map. Second type of point cloud data corresponding to a regularly shaped object is replaced with model data of a geometry model matching with the regularly object for the initial scene map to obtain the 3D scene map.

A correspondence between a source image and a reference image is determined. A generative model corresponds to a prior probability distribution of deformation fields, each deformation field corresponding to a respective co-ordinate transformation. A conditional model generates a style transfer probability distribution of reference images, given a source image and a deformation field. The first image data is the source image, and the second image data is the reference image. An initial first deformation field is determined. An update process is iteratively performed until convergence to update the first deformation field, to generate a converged deformation field representing the correspondence between the source image and the reference image. The update process includes: determining a change in one or more characteristics of the first deformation field to increase a posterior probability density associated with the first deformation field, given the source image and reference image; and changing the one or more characteristics in accordance with the determined change.

A computer-implemented method and system are described for augmenting image data of an object in an image, the method comprising receiving captured image data from a camera, storing a plurality of augmentation image data defining a respective plurality of augmentation values to be applied to the captured image data, storing a plurality of augmentation representations, each representation identifying a respective portion of augmentation image data, selecting one of said augmentation image data and one of said augmentation representations based on at least one colourisation parameter, determining a portion of the augmentation image data to be applied based on the selected augmentation representation, augmenting the captured image data by applying said determined portion of the augmentation image data to the corresponding portion of the captured image data, and outputting the augmented captured image data.

Combinatorial shape regression is described as a technique for face alignment and facial landmark detection in images. As described stages of regression may be built for multiple ferns for a facial landmark detection system. In one example a regression is performed on a training set of images using face shapes, using facial component groups, and using individual face point pairs to learn shape increments for each respective image in the set of images. A fern is built based on this regression. Additional regressions are performed for building additional ferns. The ferns are then combined to build the facial landmark detection system.

Certain embodiments involve synthesizing image content depicting facial hair or other hair features based on orientation data obtained using guidance inputs or other user-provided guidance data. For instance, a graphic manipulation application accesses guidance data identifying a desired hair feature and an appearance exemplar having image data with color information for the desired hair feature. The graphic manipulation application transforms the guidance data into an input orientation map. The graphic manipulation application matches the input orientation map to an exemplar orientation map having a higher resolution than the input orientation map. The graphic manipulation application generates the desired hair feature by applying the color information from the appearance exemplar to the exemplar orientation map. The graphic manipulation application outputs the desired hair feature at a presentation device.

In an object detection device, a distance image generation part generates a distance image based of a pair of brightness images transmitted from a stereo camera. A road surface estimation part estimates a road surface based on the generated distance image. An object detection part groups together positions having a distance value located on/over the estimated road surface and satisfying a predetermined relationship, and detects an object based on the grouped positions. An abnormality detection part detecting occurrence of image abnormality in the brightness image captured during a current detection period. The road surface estimation part estimates the road surface at the current detection period based on the road surface estimated at the previous detection period when the abnormality detection part detects the occurrence of image abnormality in the brightness image captured during the current detection period.

A system for adding persistence to SPAD imagery is configurable to capture, using a SPAD array, a plurality of image frames. The system is configurable to capture, using an IMU, pose data associated with the plurality of image frames. The pose data includes at least respective pose data associated with each of the plurality of image frames. The system is configurable to determine a persistence term based on the pose data. The system is also configurable to generate a composite image based on the plurality of image frames, the respective pose data associated with each of the plurality of image frames, and the persistence term. The persistence term defines a contribution of each of the plurality of image frames to the composite image.