Methods, systems, and articles of manufacture, including computer program products, are provided for image cleanup. In some embodiments, there is provide a method which may include subsampling a first image to a first level image of a multiscale transform; performing, based on a machine learning model, an identification of a foreground portion of the first level image and a background portion of the first level image; generating, based on the identification of the foreground portion and the background portion, a first mask; upscaling the first mask to a resolution corresponding to the first image depicting the foreground item; applying the upscaled first mask to the first image to form a second image depicting the foreground item; and providing the second image depicting the foreground item to a publication system. Related systems and articles of manufacture, including computer program products, are also provided.

Systems and methods are described for performing dynamic time warping using diffusion wavelets. Embodiments of the inventive concept integrate dynamic time warping with multi-scale manifold learning methods. Certain embodiments also include warping on mixed manifolds (WAMM) and curve wrapping. The described techniques enable an improved data analytics application to align high dimensional ordered sequences such as time-series data. In one example, a first embedding of a first ordered sequence of data and a second embedding of a second ordered sequence of data may be computed based on generated diffusion wavelet basis vectors. Alignment data may then be generated for the first ordered sequence of data and the second ordered sequence of data by performing dynamic time warping.

A method and an apparatus for separating a human object from video and estimating a posture, the method including: obtaining video of one or more real people, using a camera; generating a first feature map object having multi-layer feature maps down-sampled to different sizes from a frame image, by processing the video in units of frames; obtaining an upsampled multi-layer feature map by upsampling the multi-layer feature maps of the first feature map object, and obtaining a second feature map object, by performing convolution on the upsampled multi-layer feature map with the first feature map; detecting and separating a human object corresponding to the one or more real people from the second feature map object; and detecting a keypoint of the human object.

The present disclosure discloses a method and equipment for classifying hepatocellular carcinoma images by combining computer vision features and radiomics features, wherein the method comprising: 1) collecting eligible clinical images of patients and preprocessing the collected images; 2) extracting computer vision features from a segmented image of a hepatic tumor region; 3) extracting the manual radiomics features from the segmented image of the hepatic tumor region; 4) by combining the computer vision features and the radiomics features, screening by univariate filtering and then by LASSO regression; 5) using the features resulted from screening and clinical features together for modeling by a multivariable logistic regression model, and using the Akaike information criterion (AIC) to search backward and select clinical features suitable for the best model, so as to implement the prediction of hepatocellular carcinoma pathological grading.

The present disclosure discloses a method and equipment for classifying hepatocellular carcinoma images by combining computer vision features and radiomics features, wherein the method comprising: 1) collecting eligible clinical images of patients and preprocessing the collected images; 2) extracting computer vision features from a segmented image of a hepatic tumor region; 3) extracting the manual radiomics features from the segmented image of the hepatic tumor region; 4) by combining the computer vision features and the radiomics features, screening by univariate filtering and then by LASSO regression; 5) using the features resulted from screening and clinical features together for modeling by a multivariable logistic regression model, and using the Akaike information criterion (AIC) to search backward and select clinical features suitable for the best model, so as to implement the prediction of hepatocellular carcinoma pathological grading.

This disclosure describes methods, non-transitory computer readable storage media, and systems that generate synthetized digital images via multi-resolution generator neural networks. The disclosed system extracts multi-resolution features from a scene representation to condition a spatial feature tensor and a latent code to modulate an output of a generator neural network. For example, the disclosed systems utilizes a base encoder of the generator neural network to generate a feature set from a semantic label map of a scene. The disclosed system then utilizes a bottom-up encoder to extract multi-resolution features and generate a latent code from the feature set. Furthermore, the disclosed system determines a spatial feature tensor by utilizing a top-down encoder to up-sample and aggregate the multi-resolution features. The disclosed system then utilizes a decoder to generate a synthesized digital image based on the spatial feature tensor and the latent code.

Techniques for generating magnetic resonance (MR) images of a subject from MR data obtained by a magnetic resonance imaging (MRI) system, the techniques including: obtaining input MR data obtained by imaging the subject using the MRI system; generating a plurality of transformed input MR data instances by applying a respective first plurality of transformations to the input MR data; generating a plurality of MR images from the plurality of transformed input MR data instances and the input MR data using a non-linear MR image reconstruction technique; generating an ensembled MR image from the plurality of MR images at least in part by: applying a second plurality of transformations to the plurality of MR images to obtain a plurality of transformed MR images; and combining the plurality of transformed MR images to obtain the ensembled MR image; and outputting the ensembled MR image.

Techniques for generating magnetic resonance (MR) images of a subject from MR data obtained by a magnetic resonance imaging (MRI) system, the techniques including: obtaining input MR data obtained by imaging the subject using the MRI system; generating a plurality of transformed input MR data instances by applying a respective first plurality of transformations to the input MR data; generating a plurality of MR images from the plurality of transformed input MR data instances and the input MR data using a non-linear MR image reconstruction technique; generating an ensembled MR image from the plurality of MR images at least in part by: applying a second plurality of transformations to the plurality of MR images to obtain a plurality of transformed MR images; and combining the plurality of transformed MR images to obtain the ensembled MR image; and outputting the ensembled MR image.

The present disclosure relates to systems and methods for object recognition. The system may obtain an image and a model. The image may include a search region in which the object recognition process is performed. In the objection recognition process, for each of one or more sub-regions of the search region, the system may determine a match metric indicating a similarity between the model and the sub-region of the search region. Further, the system may determine an instance of the model among the one or more sub-regions of the search region based on the match metrics.

An illustrative multiscale data system determines a first distance between a first object in a scene and a virtual vantage point at the scene. The multiscale data system also determines a second distance between a second object in the scene and the virtual vantage point. In an example in which the second distance is greater than the first distance, the multiscale data system generates, based on the first and second distances, a tiled representation associated with the virtual vantage point. The tiled representation in this example includes a first representation of the first object rendered at a first quality level and a second representation of the second object rendered at a second quality level lower than the first quality level. Corresponding methods and systems are also disclosed.