Abstract: A method and system for tracking an object in an input video using online training includes a step for training a classifier model by using global pattern matching, and a step for classifying and tracking each target through online training including the classifier model.

Abstract: A method for training a machine learning model includes obtaining a set of training samples. For each training sample in the set of training samples, during each of one or more training iterations, the method includes cropping the training sample to generate a first cropped image, cropping the training sample to generate a second cropped image that is different than the first cropped image, and duplicating a first portion of the second cropped image. The method also includes overlaying the duplicated first portion of the second cropped image on a second portion of the second cropped image to form an augmented second cropped image. The first portion is different than the second portion. The method also includes training the machine learning model with the first cropped image and the augmented second cropped image.

Abstract: The quality of predictive model outputs is improved by improving input data in the cases where entities in the input data are associated with one or more classes, computed at least in part from one or more subsets of the input data. Class association function characteristic data is derived from information describing a class association function that generates input data from source data. The class association function characteristic data comprises inferences relating to operation of the class association function that are not derivable solely from the input data. The input data is transformed into improved input data using a constructed class-specific transformation function and the class association function characteristic data.

Abstract: Techniques are disclosed for detecting an uncovered portion of a body of a person in a frame of video content. In an example, a first machine learning model of a computing system may output a first score for the frame based on a map that identifies a region of the frame associated with an uncovered body part type. Depending on a value of the first score, a second machine learning model that includes a neural network architecture may further analyze the frame to output a second score. The first score and second score may be merged to produce a third score for the frame. A plurality of scores may be determined, respectively, for frames of the video content, and a maximum score may be selected. The video content may be selected for presentation on a display for further evaluation based on the maximum score.

Abstract: Embodiments relate to tracking and determining a location of an object in an environment surrounding a user. A system includes one or more imaging devices and an object tracking unit. The system identifies an object in a search region, determines a tracking region that is smaller than the search region corresponding to the object, and scans the tracking region to determine a location associated with the object. The system may generate a ranking of objects, determine locations associated with the objects, and generate a model of the search region based on the locations associated with the objects.

Abstract: An image classification system 10 includes: a probability computation means 11 which computes a known-image probability, which is the probability that an input image corresponds to a known image associated with a seen label that indicates the class into which content indicated by the known image is classified; a likelihood computation means 12 which computes both the likelihood that content indicated by the input image is classified into the same class as content indicated by an unseen image associated with an unseen label, and the likelihood that the content indicated by the input image is classified into the same class as the content indicated by the known image; and a correction means 13 which corrects each computed likelihood using the computed known-image probability.

Abstract: An apparatus and method for geometrically correcting an arbitrary shaped input frame and generating an undistorted output frame. The method includes capturing arbitrary shaped input images with multiple optical devices and processing the images, identifying redundant blocks and valid blocks in each of the images, allocating an output frame with an output frame size and dividing the output frame into regions shaped as a rectangle, programming the apparatus and disabling processing for invalid blocks in each of the regions, fetching data corresponding to each of the valid blocks and storing in an internal memory, interpolating data for each of the regions with stitching and composing the valid blocks for the output frame and displaying the output frame on a display module.

Abstract: A generative cooperative network (GCN) comprises a dataset generator model and a learner model. The dataset generator model generates training datasets used to train the learner model. The trained learner model is evaluated according to a reference training dataset. The dataset generator model is modified according to the evaluation. The training datasets, the dataset generator model, and the leaner model are stored by the GCN. The trained learner model is configured to receive input and to generate output based on the input.

Abstract: A training platform, a method and a computer-readable medium for evaluating users in capturing images of an internal anatomical region for the analysis of organs. Automated machine learning models, trained on a dataset of labelled training images associated with different imaging device positions, are used. The one or more automated machine learning models are used to process an image resulting from a user positioning an imaging device at various imaging device positions relative to a training manikin, a human or an animal, to determine whether the generated image corresponds to a predefined view required for the analysis of the organ features shown therein. An output indicative of whether the generated image corresponds to the predefined view expected for organ analysis and measurements is provided.

Abstract: A facility for optimizing machine learning models is described. The facility obtains a description of a machine learning model and a hardware target for the machine learning model. The facility obtains optimization result data from a repository of optimization result data. The facility optimizes the machine learning model for the hardware target based on the optimization result data.

Abstract: A computer-implemented method of training an image generative network f? for a set of training images, in which an output image {circumflex over (x)} is generated from an input image x of the set of training images non-losslessly, and in which a proxy network is trained for a gradient intractable perceptual metric that evaluates a quality of an output image {circumflex over (x)} given an input image x, the method of training using a plurality of scales for input images from the set of training images. In an embodiment, a blindspot network b? is trained which generates an output image {tilde over (x)} from an input image x. Related computer systems, computer program products and computer-implemented methods of training are disclosed.

Abstract: Using a first trained generative adversarial network, a first multimedia content is transformed into a text description of the first multimedia content. The text description is adjusted according to a constraint using a trained attention layer, the adjusting creating an adjusted text description. Using a trained model, the adjusted text description is transformed into a second multimedia content, the second multimedia content comprising an adjustment of the first multimedia content according to the constraint.

Abstract: A semantic segmentation method and apparatus for a three-dimensional image, and a storage medium are provided. The method includes: obtaining a three-dimensional image; slicing the three-dimensional image according to three directional planes, to obtain two-dimensional slice images of an x axis, two-dimensional slice images of a y axis, and two-dimensional slice images of a z axis; invoking a first segmentation model, a second segmentation model, and a third segmentation model to respectively perform semantic segmentation on the two-dimensional slice images of the x axis, the y axis, and the z axis, to obtain distribution probability maps of a target object on the three directional planes; and obtaining a three-dimensional distribution binary image of the target object by invoking an adaptive fusion model to perform three-dimensional fusion on the three distribution probability maps respectively corresponding to an x-axis directional plane, a y-axis directional plane, and a z-axis directional plane.

Abstract: Methods, apparatus, and articles of manufacture providing an efficient safety mechanism for signal processing hardware are disclosed. An example apparatus includes an input interface to receive an input signal; a hardware accelerator to process the input signal, the hardware accelerator including: unprotected memory to store non-critical data corresponding to the input signal; and protected memory to store critical data corresponding to the input signal; and an output interface to transmit the processed input signal.

Abstract: A computer-implemented method comprising: receiving a plurality of CBCT scans, each comprising a series of axial slices, wherein the CBCT scans are associated with a cohort of subjects comprising a first subgroup of subjects having each one or more maxillofacial bone lesions, and a second subgroup of subjects having no maxillofacial bone lesions; applying a feature extraction operation to extract a set of features from the axial slices in each of the CBCT scans; at a training stage, training a machine learning model on a training dataset comprising: (i) all of the extracted sets of features, and (ii) annotations indicating boundaries of bone lesions in the axial slices, to obtain a trained machine learning model configured to detect and segment a bone lesion in an axial slice from a CBCT scan.

Abstract: An image recognition system includes a processing module, a sensor module, and a database. The sensor module is electrically connected to the processing module. The database is electrically connected to the processing module. The sensor module configured for capturing at least one image. The at least one image is stored in the database. The processing captures a contour of an object from the at least one image and separates the contour of the object into a plurality of portions. A plurality of arrangements is defined between the portions of the contour of the object. The processing module determines a state of the contour of the object based on the arrangements.

Abstract: A learning method, a learning model, a classifier, a generator, and a processing system are provided, which consider human vision in learning using a machine learning model for an image. The learning method learns a machine learning model that inputs or outputs image data with data for learning that includes both or either one of image data in which a component that is difficult to judge visually is left out and image data in which a noise component that is difficult to judge visually is added at a predetermined ratio.

Abstract: The present disclosure is directed toward systems, methods, and non-transitory computer readable media for generating a modified digital image by identifying patch matches within a digital image utilizing a Gaussian mixture model. For example, the systems described herein can identify sample patches and corresponding matching portions within a digital image. The systems can also identify transformations between the sample patches and the corresponding matching portions. Based on the transformations, the systems can generate a Gaussian mixture model, and the systems can modify a digital image by replacing a target region with target matching portions identified in accordance with the Gaussian mixture model.

Abstract: In the described examples, a compiled image scaler includes a set of machine executable instructions that generate a scaled image that is a scaled version of a source image with integer and bitwise operations. The compiled image scaler employs filtering to blend colors of adjacent pixels in the source image to generate the scaled image, and each filtering operation concurrently scales three color channels of a pixel in the source image.

Abstract: Method and apparatus for mechanical sex-sorting of mosquitoes by extracting a class of mosquitoes from unsorted mosquitoes comprises obtaining unsorted mosquitoes, obtaining images of individual mosquitoes in a stationary phase, electronically classifying the individuals from the images into male mosquitoes and/or female mosquitoes, and possibly also unclassified objects; obtaining co-ordinates of individuals of at least one of the male mosquito and female mosquito classifications, and using a robot arm to reach an individual identified by the obtained coordinates to store or remove the individuals, thereby to provide sex-sorted mosquitoes.