Patents by Inventor Gregory Slabaugh
Gregory Slabaugh has filed for patents to protect the following inventions. This listing includes patent applications that are pending as well as patents that have already been granted by the United States Patent and Trademark Office (USPTO).
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Patent number: 11949996Abstract: A device for estimating a scene illumination color for a source image is configured to: determine a set of candidate illuminants and for each of the candidate illuminants, determine a respective correction of the source image; for each of the candidate illuminants, apply the respective correction to the source image to form a corresponding set of corrected images; for each corrected image from the set of corrected images, implement a trained data-driven model to estimate a respective probability of achromaticity of the respective corrected image; and based on the estimated probabilities of achromaticity for the set of corrected images, obtain a final estimate of the scene illumination color for the source image. This approach allows for the evaluation of multiple candidate illuminates to determine an estimate of the scene illumination color.Type: GrantFiled: May 12, 2022Date of Patent: April 2, 2024Assignee: Huawei Technologies Co., Ltd.Inventors: Daniel Hernandez, Sarah Parisot, Ales Leonardis, Gregory Slabaugh, Steven George McDonagh
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Patent number: 11943419Abstract: An image processing device comprising a processor configured to generate a refocused image from an input image and an map indicating depth information for the image, by the steps of: for each of a plurality of planes associated with respective depths within the image: generating a depth mask having values indicating whether regions of the input image are within a specified range of the plane, wherein an assessment of whether a region is within the specified range of the plane is made through the evaluation of a differentiable function of the range between regions of the input image and the plane as determined from the map; generating a masked image from the input image and the generated depth mask; refocusing the masked image using a blurring kernel to generate a refocused partial image; and generating the refocussed image from the plurality of refocussed partial images.Type: GrantFiled: September 20, 2021Date of Patent: March 26, 2024Assignee: Huawei Technologies Co., Ltd.Inventors: Benjamin Busam, Matthieu Hog, Steven George McDonagh, Gregory Slabaugh
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Patent number: 11625815Abstract: 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.Type: GrantFiled: September 23, 2020Date of Patent: April 11, 2023Assignee: Huawei Technologies Co., Ltd.Inventors: Gregory Slabaugh, Youliang Yan, Fenglong Song, Gang Chen, Jiangwei Li, Tao Wang, Liu Liu, Ioannis Alexiou, Ioannis Marras, Sean Moran, Steven George McDonagh, Jose Costa Pereira, Viktor Vladimirovich Smirnov
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Publication number: 20230043464Abstract: The present disclosure relates to methods and devices for performing depth estimation on image data. In one example, a device performs depth estimation on first and second images captured using one or more cameras having a color filter array. Each, image of the first and second images comprises multiple color channels. Each color channel of the multiple color channels corresponds to a respective color channel of the color filter array. The, device performs the depth estimation by estimating disparity from the color channels of the first and second images.Type: ApplicationFiled: October 20, 2022Publication date: February 9, 2023Inventors: Jifei SONG, Benjamin BUSAM, Eduardo PEREZ PELLITERO, Gregory SLABAUGH, Ales LEONARDIS
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Publication number: 20230033458Abstract: A device comprising an image processor, the image processor being configured to implement: a first machine learning model for performing restoration processing on degraded image data; and a second machine learning model for recognizing areas of an image requiring processing emphasis during the restoration processing; wherein the output of the second machine learning model is an input to the first machine learning model to optimize the restoration processing.Type: ApplicationFiled: September 29, 2022Publication date: February 2, 2023Inventors: Zeju LI, Liang CHEN, Gregory SLABAUGH, Liu LIU, Zhongqian FU
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Publication number: 20220301114Abstract: An apparatus for denoising an image, the apparatus having a processor configured to receive an input image, implement a trained artificial intelligence model to form an estimate of a noise pattern in the input image and form an output image by subtracting the estimate of the noise pattern from the input image, the model being configured to form the estimate of the noise pattern such that the estimate of the noise pattern is representative of a noise pattern that is characteristic to a specific image sensor type.Type: ApplicationFiled: June 3, 2022Publication date: September 22, 2022Inventors: Ioannis MARRAS, Ioannis ALEXIOU, Gregory SLABAUGH, Stefanos ZAFEIRIOU
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Publication number: 20220295030Abstract: A device for estimating a scene illumination color for a source image is configured to: determine a set of candidate illuminants and for each of the candidate illuminants, determine a respective correction of the source image; for each of the candidate illuminants, apply the respective correction to the source image to form a corresponding set of corrected images; for each corrected image from the set of corrected images, implement a trained data-driven model to estimate a respective probability of achromaticity of the respective corrected image; and based on the estimated probabilities of achromaticity for the set of corrected images, obtain a final estimate of the scene illumination color for the source image. This approach allows for the evaluation of multiple candidate illuminates to determine an estimate of the scene illumination color.Type: ApplicationFiled: May 12, 2022Publication date: September 15, 2022Inventors: Daniel HERNANDEZ, Sarah PARISOT, Ales LEONARDIS, Gregory SLABAUGH, Steven George MCDONAGH
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Publication number: 20220277430Abstract: An image processor for transforming an input image, the image processor being configured to implement a trained artificial intelligence model, wherein the image processor is configured to: receive the input image; based on one or both of (i) the content of the input image and (ii) features extracted from the input image, process the image by the trained artificial intelligence model to: (i) determine a set of image filters; and (ii) for each of a plurality of subregions of the image, select an image filter from the set of image filters; and for each of the plurality of subregions of the image, apply the respective image filter to the subregion or to features extracted from that subregion.Type: ApplicationFiled: May 12, 2022Publication date: September 1, 2022Applicant: HUAWEI TECHNOLOGIES CO., LTD.Inventors: Filippos KOKKINOS, Ioannis MARRAS, Matteo MAGGIONI, Stefanos ZAFEIRIOU, Gregory SLABAUGH
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Publication number: 20220270346Abstract: An image processing device for identifying one or more characteristics of an input image, the device including a processor configured to: receive the input image, the input image extending along a first axis and a second axis; form a series of attribute maps based on the received input image; perform a first correlation operation by identifying regions in respect of which the patterns of multiple ones of the series of attribute maps are correlated, and forming a first output in dependence on that operation; perform a second correlation operation for identifying combinations of (i) attributes and (ii) portions of the image having common location in terms of the first axis, and forming a second output in dependence on that operation; and form a representation of the one or more characteristics of the input image in dependence on at least the first output and the second output.Type: ApplicationFiled: May 12, 2022Publication date: August 25, 2022Applicant: HUAWEI TECHNOLOGIES CO., LTD.Inventors: Francesca BABILONI, Ioannis MARRAS, Gregory SLABAUGH, Stefanos ZAFEIRIOU
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Publication number: 20220245841Abstract: A method for training an environmental analysis system, the method comprising: receiving a data model of an environment; forming, in dependence on the data model, a first training input comprising a visual stream representing the environment as viewed from a plurality of locations; forming, in dependence on the data model, a second training input comprising a depth stream representing depths of objects in the environment relative to the plurality of locations; forming a third training input, the third training input being sparser than the second training input; and estimating, using the analysis system, in dependence on the first and third training inputs, a series of depths at less sparsity than the third training input; and adapting the analysis system in dependence on a comparison between the estimated series of depths and the second training input.Type: ApplicationFiled: April 22, 2022Publication date: August 4, 2022Inventors: Adrian LOPEZ RODRIGUEZ, Benjamin BUSAM, Gregory SLABAUGH, Ales LEONARDIS
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Publication number: 20220247889Abstract: An image processor comprising a plurality of processing modules configured to transform a raw image into an output image, the modules comprising a first module and a second module, each of which implements a respective trained artificial intelligence model, wherein: the first module is configured to implement an image transformation operation that recovers luminance from the raw image; and the second module is configured to implement an image transformation operation that recovers chrominance from the raw image.Type: ApplicationFiled: April 15, 2022Publication date: August 4, 2022Inventors: Francesca BABILONI, Ioannis MARRAS, Ales LEONARDIS, Gregory SLABAUGH
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Publication number: 20220245922Abstract: 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.Type: ApplicationFiled: April 22, 2022Publication date: August 4, 2022Inventors: Axel BARROSO LAGUNA, Yannick VERDIE, Benjamin BUSAM, Ales LEONARDIS, Gregory SLABAUGH
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Publication number: 20220222839Abstract: 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.Type: ApplicationFiled: January 27, 2022Publication date: July 14, 2022Applicant: HUAWEI TECHNOLOGIES CO., LTD.Inventors: Benjamin BUSAM, Patrick RUHKAMP, Matthieu HOG, Yannick VERDIE, Ales LEONARDIS, Gregory SLABAUGH
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Publication number: 20220051425Abstract: Disclosed is an image processing device comprising a processor configured to estimate the scale of image features by the steps of: processing multiple images of a scene by means of a first trained model to identify features in the images and to estimate the depths of those features in the images; processing the multiple images by a second trained model to estimate a scaling for the images; and estimating the scales of the features by adjusting the estimated depths in dependence on the estimated scaling. A method for training an image processing model is also disclosed.Type: ApplicationFiled: October 28, 2021Publication date: February 17, 2022Inventors: Benjamin BUSAM, Gregory SLABAUGH
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Publication number: 20220036523Abstract: An image processing module configured to implement a multi-part trained artificial intelligence model, wherein the image processing module is configured to: receive an input image; implement a first part of the model to determine a first transformation for the image in a first colour space; apply the first transformation to the image to form a first adjusted image; implement a second part of the model to determine a second transformation for the image in a second colour space; apply the second transformation to the first adjusted image to form a second adjusted image; and output an image derived from the second adjusted image.Type: ApplicationFiled: September 21, 2021Publication date: February 3, 2022Inventors: Sean MORAN, Gregory SLABAUGH
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Publication number: 20220005159Abstract: One example image processing device is provided. The example image processing device can include at least one processor and one or more memories coupled to the at least one processor and storing programming instructions for execution by the at least one processor to estimate noise in an image, the image being represented by a set of pixels and each pixel of the set of pixels having a value associated with it on each of one or more channels, where estimating the noise comprises processing, using a first trained model that detects stochastic noise, data derived from the image to form a first noise estimate, processing, using a second trained model that detects extreme pixel values, data derived from the image to form a second noise estimate, and combining the first and second noise estimates to form an aggregated noise estimate.Type: ApplicationFiled: September 21, 2021Publication date: January 6, 2022Inventors: Hao GUAN, Gregory SLABAUGH, Liu LIU, Sean MORAN, Zhongqian FU
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Publication number: 20220006998Abstract: An image processing device comprising a processor configured to generate a refocused image from an input image and an map indicating depth information for the image, by the steps of: for each of a plurality of planes associated with respective depths within the image: generating a depth mask having values indicating whether regions of the input image are within a specified range of the plane, wherein an assessment of whether a region is within the specified range of the plane is made through the evaluation of a differentiable function of the range between regions of the input image and the plane as determined from the map; generating a masked image from the input image and the generated depth mask; refocusing the masked image using a blurring kernel to generate a refocused partial image; and generating the refocussed image from the plurality of refocussed partial images.Type: ApplicationFiled: September 20, 2021Publication date: January 6, 2022Inventors: Benjamin BUSAM, Matthieu HOG, Steven George MCDONAGH, Gregory SLABAUGH
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Publication number: 20210073957Abstract: 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.Type: ApplicationFiled: September 23, 2020Publication date: March 11, 2021Inventors: Gregory SLABAUGH, Youliang YAN, Fenglong SONG, Gang CHEN, Jiangwei LI, Tao WANG, Liu LIU, Ioannis ALEXIOU, Ioannis MARRAS, Sean MORAN, Steven George MCDONAGH, Jose Costa PEREIRA, Viktor Vladimirovich SMIRNOV
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Publication number: 20210006760Abstract: 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.Type: ApplicationFiled: September 24, 2020Publication date: January 7, 2021Inventors: Steven George MCDONAGH, Sarah PARISOT, Gregory SLABAUGH, Zhenguo LI
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Publication number: 20080109192Abstract: A method for modeling a 2-dimensional tubular structure in a digitized image includes providing a digitized image of a tubular structure containing a plurality of 2D balls of differing radii, initializing a plurality of connected spline segments that form an envelope surrounding the plurality of 2D balls, each the spline segment Si being parameterized by positions of the ith and i+1th balls and contact angles ?i, ?i+1 from the center of each respective ball to a point on the perimeter of each the ball contacting the spline segment Si, each the ?i affecting spline segment Si and Si?1, and updating the angles by minimizing an energy that is a functional of the angles, where the updating is repeated until the energy is minimized subject to a constraint that the envelope is tangent to each ball at each point of contact, where the envelope is represented by the contact angles.Type: ApplicationFiled: November 1, 2007Publication date: May 8, 2008Applicant: SIEMENS CORPORATE RESEARCH, INC.Inventors: Gregory Slabaugh, Gozde Unal, Tong Fang