Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for image processing that involves annotating landmarks on real two-dimensional images. In one aspect, the methods include generating a training set of images of an object for landmark detection. This includes receiving a collection of real images of an object, estimating a pose of the object in each real image in a proper subset of the collection of real images, creating a collection of surrogate images of the object for the training set using the estimated poses and a three-dimensional model of the object.

Abstract: Application of the output from a recurrent artificial neural network to a variety of different applications. A method can include identifying topological patterns of activity in a recurrent artificial neural network, outputting a collection of digits, and inputting a first digit of the collection to a first application that is designed to fulfil a first purpose and to a second application that is designed to fulfil a second purpose, wherein the first purpose differs from the second purpose. The topological patterns are responsive to an input of data into the recurrent artificial neural network and each topological pattern abstracts a characteristic of the input data. Each digit represents whether one of the topological patterns of activity has been identified in the artificial neural network.

Abstract: Abstracting data that originates from different sensors and transducers using artificial neural networks. A method can include identifying topological patterns of activity in a recurrent artificial neural network and outputting a collection of digits. The topological patterns are responsive to an input, into the recurrent artificial neural network, of first data originating from a first sensor and second data originating from a second sensor. Each topological pattern abstracts a characteristic shared by the first data and the second data. The first and second sensors sense different data. Each digit represents whether one of the topological patterns of activity has been identified in the artificial neural network.

Abstract: Application of the output from a recurrent artificial neural network to a variety of different applications. A method can include identifying topological patterns of activity in a recurrent artificial neural network, outputting a collection of digits, and inputting a first digit of the collection to a first application that is designed to fulfil a first purpose and to a second application that is designed to fulfil a second purpose, wherein the first purpose differs from the second purpose. The topological patterns are responsive to an input of data into the recurrent artificial neural network and each topological pattern abstracts a characteristic of the input data. Each digit represents whether one of the topological patterns of activity has been identified in the artificial neural network.

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for annotation of 3D models with signs of use that are visible in 2D images. In one aspect, methods are performed by data processing apparatus. The methods can include projecting signs of use in a relatively larger field of view image of an instance of an object onto a 3D model of the object based on a pose of the instance in the relatively larger field of view image, and estimating a relative pose of the instance of the object in a relatively smaller field of view image based on matches between the signs of use in the relatively larger field of view image and the same signs of use in the relatively smaller field of view image.

Abstract: Abstracting data that originates from different sensors and transducers using artificial neural networks. A method can include identifying topological patterns of activity in a recurrent artificial neural network and outputting a collection of digits. The topological patterns are responsive to an input, into the recurrent artificial neural network, of first data originating from a first sensor and second data originating from a second sensor. Each topological pattern abstracts a characteristic shared by the first data and the second data. The first and second sensors sense different data. Each digit represents whether one of the topological patterns of activity has been identified in the artificial neural network.

Abstract: A method includes defining a plurality of different windows of time in a recurrent artificial neural network, wherein each of the different windows has different durations, has different start times, or has both different durations and different start times, identifying occurrences of topological patterns of activity in the recurrent artificial neural network in the different windows of time, comparing the occurrences of the topological patterns of activity in the different windows, and classifying, based on a result of the comparison, a first decision that is represented by a first topological pattern of activity that occurs in a first of the windows as less robust than a second decision that is represented by a second topological pattern of activity that occurs in a second of the windows.

Abstract: A method of image processing can include receiving an image of an instance of an object and a three-dimensional model of the object, detecting a first plurality of landmarks of the instance of the object in the two-dimensional image, estimating a pose of the instance of the object in the received image relative to an imaging device that acquired the image, wherein the relative pose in the received image is estimated from the first plurality of the detected landmarks, using the estimated relative pose, projecting landmarks from the three-dimensional model of the object into a dimensional space of the received image of the instance of the object, comparing, in the dimensional space, characteristics of corresponding of the projected landmarks and the first plurality of the detected landmarks, and determining whether a threshold level of positional correspondence exists between positions of corresponding of the projected landmarks and the first plurality of the detected landmarks.

Abstract: A method includes defining a plurality of different windows of time in a recurrent artificial neural network, wherein each of the different windows has different durations, has different start times, or has both different durations and different start times, identifying occurrences of topological patterns of activity in the recurrent artificial neural network in the different windows of time, comparing the occurrences of the topological patterns of activity in the different windows, and classifying, based on a result of the comparison, a first decision that is represented by a first topological pattern of activity that occurs in a first of the windows as less robust than a second decision that is represented by a second topological pattern of activity that occurs in a second of the windows.

Abstract: A method for characterizing correctness of image processing includes receiving images of an instance of an object, wherein the images were acquired at different relative poses, identifying positions of corresponding landmarks on the object in each of the received images, receiving information characterizing a difference in position or a difference in orientation of at least one of the instance of the object and one or more imaging devices when the images were acquired, transferring the positions of landmarks identified in a first of the images based on the difference in position or the difference in orientation, and comparing the positions of the transferred landmarks with the positions of the corresponding of the landmarks identified in a second of the received images, and characterizing a correctness of the identification of the positions of the landmarks in at least one of the received images.

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for annotation of 3D models with signs of use that are visible in 2D images. In one aspect, methods are performed by data processing apparatus. The methods can include projecting signs of use in a relatively larger field of view image of an instance of an object onto a 3D model of the object based on a pose of the instance in the relatively larger field of view image, and estimating a relative pose of the instance of the object in a relatively smaller field of view image based on matches between the signs of use in the relatively larger field of view image and the same signs of use in the relatively smaller field of view image.

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for annotation of 3D models with signs of use that are visible in 2D images. In one aspect, methods are performed by data processing apparatus. The methods can include projecting signs of use in a relatively larger field of view image of an instance of an object onto a 3D model of the object based on a pose of the instance in the relatively larger field of view image, and estimating a relative pose of the instance of the object in a relatively smaller field of view image based on matches between the signs of use in the relatively larger field of view image and the same signs of use in the relatively smaller field of view image.

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for image processing that involves annotating landmarks on real two-dimensional images. In one aspect, the methods include generating a training set of images of an object for landmark detection. This includes receiving a collection of real images of an object, estimating a pose of the object in each real image in a proper subset of the collection of real images, creating a collection of surrogate images of the object for the training set using the estimated poses and a three-dimensional model of the object.

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for processing images that involves annotation of landmarks on two-dimensional images. In one aspect methods are performed by data processing apparatus for training a device for estimating the relative pose of an imaging device and an object in a two-dimensional image. The methods include identifying a 3D model of the object, identifying landmarks on the 3D model of the object, projecting the 3D model into a collection of two-dimensional images with knowledge of the location of the landmarks from the 3D model on the projection, and training a landmark-detection machine learning model to identify the landmarks in the collection of two-dimensional images. The landmark-detection machine learning model is part of a device for estimating the relative pose of an imaging device.

Abstract: Abstracting data that originates from different sensors and transducers using artificial neural networks. A method can include identifying topological patterns of activity in a recurrent artificial neural network and outputting a collection of digits. The topological patterns are responsive to an input, into the recurrent artificial neural network, of first data originating from a first sensor and second data originating from a second sensor. Each topological pattern abstracts a characteristic shared by the first data and the second data. The first and second sensors sense different data. Each digit represents whether one of the topological patterns of activity has been identified in the artificial neural network.

Abstract: Application of the output from a recurrent artificial neural network to a variety of different applications. A method can include identifying topological patterns of activity in a recurrent artificial neural network, outputting a collection of digits, and inputting a first digit of the collection to a first application that is designed to fulfil a first purpose and to a second application that is designed to fulfil a second purpose, wherein the first purpose differs from the second purpose. The topological patterns are responsive to an input of data into the recurrent artificial neural network and each topological pattern abstracts a characteristic of the input data. Each digit represents whether one of the topological patterns of activity has been identified in the artificial neural network.

Abstract: Robust recurrent artificial neural networks and techniques for improving the robustness of recurrent artificial neural networks. For example, a system can include a plurality of nodes and links arranged in a recurrent neural network, wherein either transmissions of information along the links or decisions at the nodes are non-deterministic, and an output configured to output indications of occurrences of topological patterns of activity in the recurrent artificial neural network.

Abstract: A method includes defining a plurality of different windows of time in a recurrent artificial neural network, wherein each of the different windows has different durations, has different start times, or has both different durations and different start times, identifying occurrences of topological patterns of activity in the recurrent artificial neural network in the different windows of time, comparing the occurrences of the topological patterns of activity in the different windows, and classifying, based on a result of the comparison, a first decision that is represented by a first topological pattern of activity that occurs in a first of the windows as less robust than a second decision that is represented by a second topological pattern of activity that occurs in a second of the windows.