A dividing pattern determination device capable of reducing the amount of computation performed when determining a dividing pattern of an image. An image for which a dividing pattern is expressed by a hierarchical structure for each predetermined area is input to a feature extraction section, and the feature extraction section generates, based on the input image, for the predetermined area, a hierarchy map in which a value indicative of a block size is associated with each of a plurality of blocks in the predetermined area. A determination section determines a dividing pattern of the image based on the generated hierarchy map.

Techniques for synthetic data generation in computer-based reasoning systems are discussed and include receiving a request for generation of synthetic data based on a set of training data cases. One or more focal training data cases are determined. For undetermined features (either all of them or those that are not subject to conditions), a value for the feature is determined based on the focal cases. In some embodiments, the generated synthetic data may be checked for similarity against the training data, and if similarity conditions are met, it may be modified (e.g., resampled), removed, and/or replaced.

A method, according to some implementations, includes generating a plurality of base datasets using a random number generator and determining scenario specifications. The method may further include identifying at least one transformation function for at least one of the plurality of base datasets based on the scenario specifications and transforming the at least one of the plurality of base datasets using the at least one transformation function. In some cases, the method may also include generating scenario parameters based on the at least one transformed datasets.

The current invention describes systems, methods and apparatus for the combination of high-throughput flow imaging microscopy coupled with convolutional neural networks to analyze particles, such as aggregated biomolecules, and cells for use in in a variety of diagnostic, therapeutic and industrial applications.

A multi-label object detection method based on an object detection network includes: selecting an image of an object to be detected as an input image; based on a trained object detection network, obtaining a class of the object to be detected, coordinates of a center of the object to be detected, and a length and a width of a detection rectangular box according to the input image; and outputting the class of the object to be detected, the coordinates of the center of the object to be detected, and the length and the width of the detection rectangular box. The method of the present invention can perform real-time and accurate object detection on different classes of objects with improved detection speed and accuracy, and can solve the problem of object overlapping and occlusion during the object detection.

A computer, including a processor and a memory, the memory including instructions to be executed by the processor to train a generative adversarial network (GAN) to reconstruct a missing portion of an image by determining a reconstructed portion of the image based on data from portions of the image surrounding the missing portion and compare an acquired image with the reconstructed portion of the image to determine a damaged portion. The instructions further include instructions to determine estimated damage based on the damaged portion.

Various embodiments of the teachings herein include a method for training and updating a backend-side classifier comprising: receiving, in a backend-device, from at least one vehicle, classification data along with a respective classification result generated by a vehicle-side classifier; and training the backend-side classifier using the classification data and, if available, a corrected respective classification result as annotation.

A computing system generates a training data set for training the prediction model to detect defects present in a target surface of a target specimen and training the prediction model to detect defects present in the target surface of the target specimen based on the training data set. The computing system generates the training data set by identifying a set of images for training the prediction model, the set of images comprising a first subset of images. A deep learning network generates a second subset of images for subsequent labelling based on the set of images comprising the first subset of images. The deep learning network generates a third subset of images for labelling based on the set of images comprising the first subset of images and the labeled second subset of images. The computing system continues the process until a threshold number of labeled images is generated.

An information processing apparatus is provided and includes an obtaining unit obtains an estimation result of a score representing a likelihood for each of a first candidate and a second candidate for a label to be added as an annotation to data to be annotated. A control unit controls processing for displaying, depending on the score for each of the first candidate and the second candidate, first display information and second display information through an output unit, the first display information indicating a display position associated with the first candidate, the second display information indicating a display position associated with the second candidate.

The pose and shape of a human body may be recovered based on joint location information associated with the human body. The joint location information may be derived based on an image of the human body or from an output of a human motion capture system. The recovery of the pose and shape of the human body may be performed by a computer-implemented artificial neural network (ANN) trained to perform the recovery task using training datasets that include paired joint location information and human model parameters. The training of the ANN may be conducted in accordance with multiple constraints designed to improve the accuracy of the recovery and by artificially manipulating the training data so that the ANN can learn to recover the pose and shape of the human body even with partially observed joint locations.