The method according to the invention is based on a first image of a first eye region of a person and a second image of a second eye region of the person, wherein the first eye region contains one of the eyes of the person, for example the right eye, and the second eye region contains the other eye of the person, for example the left eye; one of the images is mirrored, and the mirrored and the non-mirrored image are combined in the position space and/or in the feature space, in order to generate a template of an overlaid image. The template contains biometric features for person recognition.

Multi-modal approaches to predict tumor immune infiltration are based on integrating gene expression data and imaging features in a neural network-based framework. This framework is configured to estimate percent composition, and thus immune infiltration score, of a patient tumor biopsy sample. Multi-modal approaches may also be used to predict cell composition beyond immune cells via integrated multi-layer neural network frameworks.

This disclosure relates to improved techniques for performing multi-view image clustering. The techniques described herein utilize machine learning functions to optimize the image clustering process. Multi-view features are extracted from a collection of images. A machine learning function is configured to jointly learn a fused binary representation that combines the multi-view features and one or more binary cluster structures that can be used to partition the images. A clustering function utilizes the fused binary representation and the one or more binary cluster structures to generate one or more image clusters based on the collection of images.

The present invention extends to methods, systems, and computer program products for classifying time series image data. Aspects of the invention include encoding motion information from video frames in an eccentricity map. An eccentricity map is essentially a static image that aggregates apparent motion of objects, surfaces, and edges, from a plurality of video frames. In general, eccentricity reflects how different a data point is from the past readings of the same set of variables. Neural networks can be trained to detect and classify actions in videos from eccentricity maps. Eccentricity maps can be provided to a neural network as input. Output from the neural network can indicate if detected motion in a video is or is not classified as an action, such as, for example, a hand gesture.

A method and system for an Al-based communication training system for individuals and organizations is disclosed. A video analyzer is used to convert a video signal into a plurality of human morphology features with an accompanying audio analyzer converting an audio signal into a plurality of human speech features. A transformation module transforms the morphology features and the speech features into a current multi-dimensional performance vector and combinatorial logic generates an integration of the current multi-dimensional performance vector and one or more prior multi-dimensional performance vectors to generate a multi-session rubric. Backpropagation logic applies a current multi-dimensional performance vector from the combinatorial logic to the video analyzer and the audio analyzer.

Provided are an apparatus and method for detecting an anomaly in a plant pipe using multiple meta-learning. When a multi-sensor data stream about a plant pipe is received, each of a plurality of meta-learning modules for processing different packet section ranges, extracts one or more preset types of features from sensor data of packet section ranges set according to trend from an arbitrary reception time point, generates 2D image features of the features according to multi-sensor-specific times, generates 3D volume features by accumulating the 2D image features in a depth direction according to multiple sensors, and learns the 3D volume features in parallel through multi-sensor-specific learning modules. Results of the learning of the meta-learning modules are aggregated, and it is determined whether there is an anomaly in a plant pipe according to a learning result selected based on an optimal combination of multiple features, multiple sensors, and multiple packet sections.

A method of analyzing a fingerprint, the method comprising the step of acquiring a fingerprint image (20) together with the following steps: performing filtering processing on the fingerprint image to estimate, for each pixel of the fingerprint image, a first frequency of the ridges (21) in the fingerprint, and using the first frequencies associated with the pixels of the fingerprint image to produce a first frequency map (22) of the fingerprint image; subdividing the fingerprint image into a plurality of windows each comprising a plurality of pixels, calculating a Fourier transform for each window in order to estimate a second frequency of the ridges for all of the pixels in said window, and using the second frequencies associated with the pixels of the windows to produce a second frequency map of the fingerprint image; and merging the first frequency map and the second frequency map in order to obtain a map of consolidated frequencies of the fingerprint image.

Disclosed herein are a method for analyzing material using a neural network based on multimodal input and an apparatus for the same. The method includes obtaining multimodal sensor data pertaining to at least one type of material to be analyzed using a multimodal sensor and augmenting the multimodal sensor data; merging the multimodal sensor data; training a neural network, provided for classifying the at least one type of material to be analyzed, using the merged multimodal sensor data; and classifying the type of target material by inputting multimodal sensor data pertaining to the target material, which is obtained using the multimodal sensor data, to the neural network.

One embodiment provides a method comprising receiving data relating to a tenant utilizing a cloud computing environment, and determining one or more classifications for a variation in current workload resource consumption of the tenant based on the data. The current workload resource consumption is indicative of current usage of one or more computing resources of the cloud computing environment.

Systems, methods, and computer-readable storage media for object detection and classification, and particularly produce detection and classification. A system configured according to this disclosure can receiving, at a processor, an image of an item. The system can then perform, across multiple pre-trained neural networks, feature detection on the image, resulting in feature maps of the image. These feature maps can be concatenated and combined, then input into an additional neural network for feature detection on the combined feature map, resulting in tiered neural network features. The system then classifies, via the processor, the item based on the tiered neural network features.