Learning image processing tasks from scene reconstructions
First Claim
1. A method of image processing comprising:
- receiving a plurality of first input empirical images of a scene from an image capture device in real-time;
at a processor, calculating a 2D or higher dimensional reconstruction of the scene from the first input images, reconstruction being based at least in part on a real-time frame alignment engine;
forming training data from the reconstruction of the scene and the first input images;
using the training data to learn at least one parameter of a function for transforming an image;
receiving a second input image; and
transforming the second input image using the function and the at least one parameter,wherein forming the training data comprises rendering images from the reconstruction of the scene according to specified poses of an image capture apparatus used to capture the empirical first images.
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Abstract
Learning image processing tasks from scene reconstructions is described where the tasks may include but are not limited to: image de-noising, image in-painting, optical flow detection, interest point detection. In various embodiments training data is generated from a 2 or higher dimensional reconstruction of a scene and from empirical images of the same scene. In an example a machine learning system learns at least one parameter of a function for performing the image processing task by using the training data. In an example, the machine learning system comprises a random decision forest. In an example, the scene reconstruction is obtained by moving an image capture apparatus in an environment where the image capture apparatus has an associated dense reconstruction and camera tracking system.
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Citations
19 Claims
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1. A method of image processing comprising:
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receiving a plurality of first input empirical images of a scene from an image capture device in real-time; at a processor, calculating a 2D or higher dimensional reconstruction of the scene from the first input images, reconstruction being based at least in part on a real-time frame alignment engine; forming training data from the reconstruction of the scene and the first input images; using the training data to learn at least one parameter of a function for transforming an image; receiving a second input image; and transforming the second input image using the function and the at least one parameter, wherein forming the training data comprises rendering images from the reconstruction of the scene according to specified poses of an image capture apparatus used to capture the empirical first images. - View Dependent Claims (2, 3, 4, 5, 6, 7)
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8. A method of image processing comprising:
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receiving at least one input image from an image capture device in real-time; and at a processor, transforming the input image using a function having at least one parameter which has been learnt from training data which has been obtained from a 2D, or higher dimensional, reconstruction of a scene reconstructed from empirical data, the reconstruction being based at least in part on real-time frame alignment engine, wherein the training data is obtained by rendering images from the reconstruction of the scene according to specified poses of an image capture apparatus used to capture the empirical data. - View Dependent Claims (9, 10, 11)
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12. An image processing system comprising:
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an input arranged to receive a sequence of first input empirical images of a scene obtained from a camera moving in the scene; a processor arranged to calculate a 2D or higher dimensional reconstruction of the scene from the first input images and also to track a location and orientation of the camera, the location and orientation of the camera being based at least in part on camera-mounted orientation and motion sensors; the processor being arranged to form training data from the reconstruction of the scene, the tracked camera location and orientation, and at least some of the first input images; a machine learning system arranged to use the training data to learn at least one parameter of a function for transforming an image; the input being arranged to receive a second input image; and the machine learning system being arranged to transform the second input image using the function and the at least one parameter, wherein forming the training data comprises rendering images from the reconstruction of the scene according to specified poses of an image capture apparatus used to capture the empirical first images. - View Dependent Claims (13, 14, 15, 16, 17, 18, 19)
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Specification