Fujitsu Corporation

Joseph Townsend

Eduarda Mendes Rodrigues

Serban Georgescu

Artur Garcez

Kwun Ho Ngan

Theodoros Kasioumis

Savvas Makariou

A computer-implemented method comprising: obtaining an output from each of a plurality of kernels in an extraction layer of a first trained convolutional neural network, wherein the first convolutional neural network is configured to identify one or more features in an image; aggregating the outputs corresponding to at least some input samples of a first domain to generate an aggregate map corresponding to that kernel; resizing the aggregate maps to a lower resolution to generate a plurality of region maps corresponding to the aggregate maps, respectively; clustering the region maps to generate clusters of region maps, each cluster comprising region maps having similar regions; and training, using input samples of a second domain, a second convolutional neural network with a kernel weight of at least one of the kernels which corresponds to at least one of the image regions of at least one of the clusters.

Kernel transfer

An image processing method to sample the image to generate patches. Feature vectors are extracted from the patches, and the extracted feature vectors are partitioned into clusters, where feature vectors in the same cluster share a common characteristic. A portion of interest in the image is segmented. An aggregate bounding region creation process is carried out by finding the largest segment and creating a bounding box around it; determining which cluster contains the most patches within the bounding box of the segment; and adding the patches of the determined cluster to an aggregate bounding region for the portion of interest. The aggregate bounding region creation process is repeated for each other segment in order of size. The resulting aggregate bounding region contains all the patches associated with the portion of interest. The patches which fall outside the resulting aggregate bounding region are then removed from the image.

Processing digital image to remove unwanted portion

A computer-implemented method of automatically locating a portion of interest in image or matrix data derived from an item under consideration includes: identifying parts of the image or matrix data that satisfy a preset threshold as objects which are possibly parts of the portion of the interest; applying at least one preselected filter to the data corresponding to the objects to find a set of objects consisting of the objects most likely to be part of the portion of interest; sorting the objects of the set into clusters according to a predefined criterion; and using a known characteristic of the portion of interest to identify which one of the clusters corresponds to the portion of interest.

Detecting portions of interest in images

A computer-implemented method of generating, from time-series data, a time-series of data sets for predictive analysis, comprises dividing the time-series data into evenly-sized overlapping segments of data, generating an image representing data for each segment, using the time-series data to determine a trend associated with each image, and storing each of the generated images and its associated trend as a data set. In some embodiments of the method the image from each stored data set is transformed into numerical vectors through a feature extraction process using a pre-trained convolutional neural network. The numerical vectors are stored in association with the data set, and the data sets and associated numerical vectors are used to predict the trend for a new time-series image which has been generated from any time-series data.

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High Wycombe, United Kingdom

Joseph Townsend