Huawei Technologies Co., Limited

Samsung Electronics Co., Ltd.

Siemens Aktiengesellschaft

Siemens Audiologische Technik Gmbh

Medicsight Plc

Gregory Gibran Slabaugh

Tong Fang

Ales Leonardis

Xujiong Ye

Ioannis Marras

Yannick Verdie

Abdel Douiri

Tao Wang

Liu Liu

Gang Chen

Jiangwei Li

Yunqiang Chen

Gozde Unal

Sean Moran

Youliang Yan

Zhenguo Li

Brian Whited

Fenglong Song

Jaroslaw Rossignac

Steven George McDonagh

Rupen Melkisetoglu

Benjamin Busam

Ioannis Alexiou

Axel Barroso Laguna

Matthieu Hog

Steven George Mcdonagh

Sarah Parisot

Kooksang Moon

Muhammad Musib Siddique

Simon Floery

Richard Graham Boyes

Jose Costa Pereira

Viktor Vladimirovich Smirnov

Gareth Richard Beddoe

Francesca Babiloni

Xinyu Lin

Patrick Ruhkamp

A processing entity generates a model for estimating scene illumination colour for a source image captured by a camera The processing entity acquires a set of images, captured by a respective camera, the set of images as a whole including images captured by multiple cameras; forms a set of tasks by assigning each image of the images set to a respective task such that images in the same task have in common that a the images are in a predetermined range; trains model parameters by repeatedly: selecting at least one of the tasks, forming an interim set of model parameters based on a first subset of the images of that task, estimating the quality of the interim set of model parameters against a second subset of the images of that task and updating the parameters of the model based on the interim set of parameters and the estimated quality.

Meta-learning for camera adaptive color constancy

The technology of this application related to an image processor comprising a plurality of modules, the plurality of modules comprising a first module and a second module, wherein the image processor is configured to receive an input image and output a plurality of mathematical descriptors for characteristic regions of the input image. The first module is configured to implement a first trained artificial intelligence model to detect a set of characteristic regions in the input image; and the second module is configured to implement a second trained artificial intelligence model to determine a mathematical descriptor for each of said set of characteristic regions. The first and second trained artificial intelligence models are collectively trained end to end.

Feature detector and descriptor

An image processing system configured to receive an input time-of-flight depth map representing the distance of objects in an image from a camera at a plurality of locations of pixels in the respective image, and in dependence on that map to generate an improved time-of-flight depth map for the image, the input time-of-flight depth map having been generated from at least one correlation image representing the overlap between emitted and reflected light signals at the plurality of locations of pixels at a given phase shift, the system being configured to generate the improved time-of-flight depth map from the input time-of-flight depth map in dependence on a colour representation of the respective image and at least one correlation image.

Time-of-flight depth enhancement

An image processor comprising a plurality of processing modules configured to transform a raw image into an output image. The plurality of processing modules comprise a first module and a second module, each of which implements a respective trained artificial intelligence model. The first module is configured to implement an image transformation operation that recovers luminance from the raw image. The second module is configured to implement an image transformation operation that recovers chrominance from the raw image.

Trained artificial intelligence model for raw to RGB image transformation

An image processing apparatus and a method are provided. The apparatus comprises a plurality of processing modules configured to operate in series to refine a raw image captured by a camera, the modules comprising a first module and a second module, each of which independently implements a respective trained artificial intelligence model, wherein: the first module implements an image transformation operation that performs an operation from the set comprising: (i) an essentially pixel-level operation that increases sharpness of an image input to the module, (ii) an essentially pixel-level operation that decreases sharpness of an image input to the module, (iii) an essentially pixel-block-level operation on an image input to the module; and the second module as a whole implements a different operation from the said set.

Image processor and method

A system and method for characterizing a point in a three-dimensional digital medical image includes determining third order derivatives of the three-dimensional digital medical image at one or more points in the three-dimensional digital medical image. At each of the one or more points, one or more invariant or semi-invariant features that characterize a local geometry at the point is determined using the third order derivatives of the three-dimensional digital medical image. The invariant or semi-invariant features are used for automatic detection of lesions, abnormalities, and other anatomical structures of interest in the three-dimensional digital medical image.

System and method for detection of lesions in three-dimensional digital medical image

A segmentation method comprises clustering spatial, intensity and volumetric shape index to automatically segment a medical lesion. The algorithm has the following steps: (1) calculating volumetric shape index (SI) for each voxel in the image; (2) combining the SI features with the intensity range and the spatial position (x, y, z) to form a 5-dimensional feature vector set; (3) grouping the 5-dimensional feature vector set into clusters; (4) employing a modified expectation-maximization algorithm (EM) considering not only spatial but also shape features on an intensity mode map from the clustering algorithm to merge the neighbouring regions or modes. The joint spatial-intensity-shape feature provides rich information for the segmentation of the anatomic structures of interest, such as lesions or tumours.

Medical image segmentation

A computer-implemented method of detecting an object in a three-dimensional medical image comprises determining the values of a plurality of features at each voxel in at least a portion of the medical image. Each feature characterises a respective property of the medical image at a particular voxel. The likelihood probability distribution of each feature is calculated based on the values of the features and prior medical knowledge. A probability map is generated by using Bayes' law to combine the likelihood probability distributions, and the probability map is analysed to detect an object.

Medical image processing

A method of computing a continuous interpolation of a discrete set of three-dimensional (3D) balls, including generating an initial skin, wherein the initial skin is a surface comprised of splines and wherein the splines touch each ball along a circle that is tangent to the ball, solving a first differential equation to minimize the initial skin's surface area or solving a second differential equation to minimize a squared mean curvature of the initial skin's surface, wherein the result of solving the first or second differential equations is an updated skin; and repeating the steps of solving the first or second differential equations for the updated skin, and then, repeating the steps of solving the first or second differential equations for each subsequently updated skin until a desired skin is realized.

3D ball skinning using partial differential equations for generation of smooth tubular surfaces

A method and system for suppressing bone structures based on a single x-ray image is disclosed. The bone structure suppressing method predicts a soft-tissue image without bone structures from an input x-ray image. A set of features is extracted for each pixel of the input x-ray image. A soft-tissue image is then generated from the input x-ray image using a trained regression function to determine an intensity value for the soft-tissue image corresponding to each pixel of the input x-ray image based on the set of features extracted for each pixel of the input x-ray image. The extracted features can be wavelet features and the regression function can be trained using Bayesian Committee Machine (BCM) to approximate Gaussian process regression (GPR).

Method and system for bone suppression based on a single x-ray image

A method and system for detecting the second bend plane of an ear canal surface is disclosed. A region of interest of the ear canal is defined between the first bend plane and the ear canal tip. A set of curves is defined in the region of interest and a set of high curvature points is detected from the set of curves. The second bend plane is detected using the set of high curvature points.

Method and system for second bend plane detection of ear canal surfaces

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London, United Kingdom

Gregory Gibran Slabaugh