Abstract: A highly versatile data processing is implemented on data collected in a manufacturing process. A data processing device includes: a calculation part configured to collect a plurality of data groups associated with a predetermined step of a process, and calculate effects in the predetermined step for each of the plurality of data groups; a dividing part configured to divide a feature space such that a distribution of each of the plurality of data groups associated with the predetermined step in the feature space is classified for each of the calculated effects; and an output part configured to output specific data that specifies respective regions of the divided feature space.

Abstract: A crop prediction system performs various machine learning operations to predict crop production and to identify a set of farming operations that, if performed, optimize crop production. The crop prediction system uses crop prediction models trained using various machine learning operations based on geographic and agronomic information. Responsive to receiving a request from a grower, the crop prediction system can access information representation of a portion of land corresponding to the request, such as the location of the land and corresponding weather conditions and soil composition. The crop prediction system applies one or more crop prediction models to the access information to predict a crop production and identify an optimized set of farming operations for the grower to perform.

Abstract: An authentication engine, residing at one or more computing machines, receives, from a vision device comprising one or more cameras, a probe image. The authentication engine generates, using a trained facial classification neural engine, one or more first labels for a person depicted in the probe image and a probability for at least one of the one or more first labels. The authentication engine determines that the probability is within a predefined low accuracy range. The authentication engine generates, using a supporting engine, a second label for the person depicted in the probe image. The supporting engine operates independently of the trained facial classification neural engine. The authentication engine further trains the facial classification neural engine based on the second label.

Abstract: Approaches to zero-shot learning include partitioning training data into first and second sets according to classes assigned to the training data, training a prediction module based on the first set to predict a cluster center based on a class label, training a correction module based on the second set and each of the class labels in the first set to generate a correction to a cluster center predicted by the prediction module, presenting a new class label for a new class to the prediction module to predict a new cluster center, presenting the new class label, the predicted new cluster center, and each of the class labels in the first set to the correction module to generate a correction for the predicted new cluster center, augmenting a classifier based on the corrected cluster center for the new class, and classifying input data into the new class using the classifier.

Abstract: A system for physiological motion measurement is provided. The system may acquire a reference image corresponding to a reference motion phase of an ROI and a target image of the ROI corresponding to a target motion phase, wherein the reference motion phase may be different from the target motion phase. The system may identify one or more feature points relating to the ROI from the reference image, and determine a motion field of the feature points from the reference motion phase to the target motion phase using a motion prediction model. An input of the motion prediction model may include at least the reference image and the target image. The system may further determine a physiological condition of the ROI based on the motion field.

Abstract: The present embodiments relate generally to a system and method for generating images from ultrasound imaging data. The system can include a data acquisition processor, and administrator processor, a user device, and a server. The method can include transmitting images to a server, applying an implicit misfit function to generate a first set of images, then, based on this first set, apply an explicit misfit function to generate a second set of images of higher accuracy.

Abstract: A distance information generation apparatus includes a generation unit configured to generate distance information using first and second image signals captured from different viewpoints, a detection unit configured to detect a known-shape subject using the image signals, an extraction unit configured to extract, from the distance information generated by the generation unit, distance information corresponding to the subject, a calculation unit configured to calculate, based on the distance information and the shape of the subject detected by the detection unit, a correction parameter for correcting the distance information extracted by extraction unit, and a correction unit configured to correct, using the correction parameter calculated by the calculation unit, the distance information generated by the generation unit.

Abstract: There is provided systems and methods for performing actions based on light signatures. An exemplary system includes a light source, a light detector, a non-transitory memory storing a plurality of light signatures and a hardware processor. The hardware processor executes an executable code to illuminate, using the light source, a target object with a first light, collect, using the light detector, a second light being a reflection of the first light by the target object, match the second light with one of the plurality of light signatures, and perform an action in response to matching the second light with the one of the plurality of light signatures.

Abstract: The present embodiments include a system and method for processing multi-dimensional images in real time through the use of third-party servers and cloud computing. The system includes a data acquisition processor, a data storage unit, an administrator processor, and a server. The server can be a cloud-based server. The method includes receiving multi-dimensional imaging data, compressing and blending the image data, transmitting the image data to a server, decompressing and deblending the data, generating multi-dimensional images, and transmitting the imaging data back to the administrator processor.

Abstract: A method for generating a synthetic dataset from an original dataset includes encoding categorical features of the original dataset, embedding the encoded dataset in a low-dimensional space, selecting a seed record from the embedded dataset, identifying a plurality of nearest neighbor records to the seed record, generating a new record by randomly selecting features from the plurality of nearest neighbor records, and concatenating the new record into the synthetic dataset. For a synthetic dataset that contains N records, which may be the same as or different from the number of records in the original dataset, the selecting, identifying, generating, and concatenating operations operate a total of N times on the records in the embedded dataset.

Abstract: Disclosed are an image semantic segmentation method, a logical integrated circuit, a system and an electronic device. The logical integrated circuit includes a convolution processing module and a deconvolution processing module. The convolution processing module performs convolution operation processing on an image to generate a piece of feature data of each and every feature image block of the image. The deconvolution processing module is configured to perform deconvolution operation processing on each piece of feature data to obtain an respective image block region; determine an approximation degree of the each piece of feature data and each and every preset semantic category of multiple preset semantic categories, and classify the each piece of feature data into a preset semantic category; and fill each image block region corresponding to the each piece of feature data with a filling color to achieve semantic segmentation of the image.

Abstract: The invention provides a method, apparatus, device and storage medium for predicting a protein binding site. The method comprises the steps of: receiving a protein sequence to be predicted, dividing the protein sequence by using a preset sliding window and sliding step to obtain a plurality of amino acid sub-sequences, building word vectors for the protein sequence according to the amino acid sub-sequences, extracting document features from word elements, building document feature vectors for the protein sequence according to the extracted document features, extracting protein chain biological features from the amino acid sub-sequences, building biological feature vectors for the protein sequence according to the extracted biological features, classifying the amino acid sub-sequences expressed with the document feature vectors and the biological feature vectors by using a preset amino acid residue classification model to obtain amino acid residue types for the protein sequence.

Abstract: Various aspects of a system and a method for determining a position of one or more multi-dimensional objects are disclosed herein. In accordance with an embodiment, the system may include a memory and a processor. The processor may be configured to obtain, from a plurality of satellite images, shadow data of a first multi-dimensional object from one or more multi-dimensional objects on a visible surface. The processor may be configured to obtain, from a server, base elevation data and height data of the first multi-dimensional object. The processor may be further configured to generate a Digital Elevation Model (DEM) of the plurality of multi-dimensional objects. The processor may be further configured to determine a position of a second multi-dimensional object of the plurality of multi-dimensional objects on the visible surface, based on the generated DEM.

Abstract: The invention relates to a method for online quality control of decorative prints on substrate materials, including similarity comparisons of actual and target images and adjusting decorative prints if deviations of color values are detected. The method may include the steps of: a) producing a hyperspectral digital image of a print decoration; b) calibrating the print decoration via a hyperspectral digital image; c) producing and storing a digital target image of the print decoration; d) creating a first print decoration on a first substrate material; e) producing and storing a digital actual image of the printed decoration on the first substrate material; f) determining color deviations between the digital target image and the digital actual image via a computer program; and g) printing on at least one side of substrate materials so as to form a decorative layer. The invention also relates to a device for carrying out the method.

Abstract: One or more computer processors creating a plurality of k-hop neighborhood contextual subgraphs utilizing extracted labelled nodes from an input graph; compute an eigenvector centrality score for each node contained in each created subgraph in the plurality of k-hop neighborhood contextual subgraphs; propagate a label for each node in each subgraph in the plurality of k-hop neighborhood contextual subgraphs leveraging an aggregated mathematical decay function, preserving a topical context of the label; calculate an attributable prestige vector for each node in each subgraph in the plurality of k-hop neighborhood contextual subgraphs based on the propagated label and the computed eigenvector centrality score associated with each node in each subgraph in the plurality of k-hop neighborhood contextual subgraph; and unsupervised predict a subsequent label for one or more subsequent nodes, subgraphs, or graphs utilizing the calculated attributable prestige vectors for each node in each subgraph.

Abstract: Disclosed is a detection target positioning method and device. The method comprises: acquiring an original image and pre-processing the original image to obtain a gradation of each pixel in a target gradation image corresponding to a target region including a detection target; calculating first gradation sets corresponding to rows of pixels of the target gradation image and second gradation sets corresponding to columns of pixels of the target gradation image; and determining rows of two ends of the detection target in a column direction according to the first gradation sets, determining columns of two ends of the detection target in a row direction according to the second gradation sets, and determining a center of the detection target according to the row of two ends of the detection target in the column direction and the columns of two ends of the detection target in the row direction.

Abstract: There is provided systems and methods for training a classifier. The method comprises: obtaining a classifier for classifying data into one of a plurality of classes; retrieving training data comprising a set of observations and a set of corresponding labels, each label representing an assigned class for a corresponding observation; and applying an agent trained by a reinforcement learning system to generate labeled data from unlabeled observations and train the classifier using the training data and the labeled data according to a policy determined by the reinforcement learning system.

Abstract: An image processing system has one or more memories and image processing circuitry coupled to the one or more memories. The image processing circuitry, in operation, compares a first image to feature data in a comparison image space using a matching model. The comparing includes: unwarping keypoints in keypoint data of the first image; and comparing the unwarped keypoints and descriptor data associated with the first image to the feature data of the comparison image. The image processing circuitry determines whether the first image matches the comparison image based on the comparing.

Abstract: The disclosure provides a pedestrian re-identification method based on a spatio-temporal joint model of a residual attention mechanism and a device thereof. The method includes: performing feature extraction for an input pedestrian with a pre-trained ResNet-50 model; constructing a residual attention mechanism network including a residual attention mechanism module, a feature sampling layer, a global average pooling layer and a local feature connection layer; calculating a feature distance by using a cosine distance and denoting the feature distance as a visual probability according to the trained residual attention mechanism network; performing modeling for a spatio-temporal probability according to camera ID and frame number information in a pedestrian tag of a training sample, and performing Laplace smoothing for a probability model; and calculating a final spatio-temporal joint probability by using the visual probability and the spatio-temporal probability to obtain a pedestrian re-identification result.

Abstract: Free space machine interface and control can be facilitated by predictive entities useful in interpreting a control object's position and/or motion (including objects having one or more articulating members, i.e., humans and/or animals and/or machines). Predictive entities can be driven using motion information captured using image information or the equivalents. Predictive information can be improved applying techniques for correlating with information from observations.