Provided herein are systems and methods for applying adaptive classes thresholds to enhance object detection Machine Learning (ML) models by receiving a plurality of labeled feature vectors extracted from a plurality of images associated with a plurality of objects, one or more subsets of the plurality of feature vectors are associated with respective object(s) and labeled accordingly, computing an adaptive threshold for each object in a plurality of iterations, each iteration comprising: (1) computing deviation of a respective feature vector of the subset from an aggregated feature vector, (2) computing, in case the deviation is within a predefined value, a threshold enclosing the respective feature vector, and (3) adjusting the adaptive threshold to enclose the threshold of the respective feature vector and outputting the adaptive threshold(s) for classifying unlabeled feature vectors to class(s) of respective object(s) associated with the adaptive threshold(s) in which the unlabeled feature vectors fall.

Techniques are described herein that enable advanced gaming and virtual reality control using radar. These techniques enable small motions and displacements to be tracked, even in the millimeter or submillimeter scale, for user control actions even when those actions are optically occluded or obscured.

Simultaneous learning of a plurality of different tasks and domains, with low costs and high precision, is enabled. A learning unit 160, on the basis of learning data, uses a target encoder that takes data of a target domain as input and outputs a target feature expression, a source encoder that takes data of a source domain as input and outputs a source feature expression, a common encoder that takes data of the target domain or the source domain as input and outputs a common feature expression, a target decoder that takes output of the target encoder and the common encoder as input and outputs a result of executing a task with regard to data of the target domain, and a source decoder that takes output of the source encoder and the common encoder as input and outputs a result of executing a task with regard to data of the source domain, to learn so that the output of the target decoder matches training data, and the output of the source decoder matches training data.

Methods and systems are provided for implementing source separation techniques, and more specifically performing source separation on mixed source single-channel and multi-channel audio signals enhanced by inputting lip motion information from captured image data, including selecting a target speaker facial image from a plurality of facial images captured over a period of interest; computing a motion vector based on facial features of the target speaker facial image; and separating, based on at least the motion vector, audio corresponding to a constituent source from a mixed source audio signal captured over the period of interest. The mixed source audio signal may be captured from single-channel or multi-channel audio capture devices. Separating audio from the audio signal may be performed by a fusion learning model comprising a plurality of learning sub-models. Separating the audio from the audio signal may be performed by a blind source separation (BSS) learning model.

The discussion relates to 4D tracking. One example can utilize multiple 3D cameras positioned relative to an environment to sense depth data of the environment from different viewpoints over time. The example can process the depth data to construct 3D solid volume representations of the environment, select subjects from the 3D solid volume representations, and recognize actions of the selected subjects.

Mechanisms are provided to implement an automated echocardiograph measurement extraction system. The automated echocardiograph measurement extraction system receives medical imaging data comprising one or more medical images and inputs the one or more medical images into a deep learning network. The deep learning network automatically processes the one or more medical images to generate an extracted echocardiograph measurement vector output comprising one or more values for echocardiograph measurements extracted from the one or more medical images. The deep learning network outputs the extracted echocardiograph measurement vector output to a medical image viewer.

The present application discloses an image recognition method, apparatus, device, and a computer storage medium, which is related to a technical field of artificial intelligence, and in particular, to a technical field of image processing. The method includes: performing organ recognition on a human face image and marking positions of the human facial five sense organs in the human face image, obtaining a marked human face image; inputting the marked human face image into a backbone network model and performing feature extraction, obtaining defect features of the marked human face image outputted by different convolutional neural network levels of the backbone network model; and fusing the defect features of different levels that are located in a same area of the human face image, obtaining a defect recognition result of the human face image.

An image recognition method is provided, which is related to a technical field of artificial intelligence, and in particular, to a technical field of image processing. An implementation includes: performing five-sense-organ recognition on a preprocessed human face image and marking positions of the human facial five sense organs in the human face image, to obtain the marked human face image; determining human face images at multiple scales of the marked human face image, inputting the human face images of multiple scales into a backbone network model, and performing feature extraction, to obtain a wrinkle feature of the human face image at each of the multiple scales; and fusing the wrinkle feature at each scale that is located in a same area of the human face image, to obtain a wrinkle recognition result of the human face image

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.

An apparatus for motion determination includes: a memory; and a processor coupled to the memory, the processor being configured to (a) execute a specifying process that includes specifying an orientation of a head portion of a subject, (b) execute a calculation process that includes calculating a trajectory of a position of the head portion of the subject in a vertical direction and a trajectory of the position of the head portion of the subject in a transverse direction in a predetermined time range, and (c) execute a determination process that includes when the calculated trajectory in the vertical direction and the calculated trajectory in the transverse direction satisfy a determination condition for the specified orientation of the head portion, determining that there is a predetermined motion mapped to a motion of the head portion of the subject in the vertical direction.