Disclosed herein are an object recognition apparatus and method based on environment matching. The object recognition apparatus includes memory for storing at least one program, and a processor for executing the program, wherein the program performs extracting at least one key frame from a video that is input in real time, determining a similarity between the key frame extracted from the input video and each of videos used as training data of prestored multiple recognition models, based on a pretrained similarity-matching network, and selecting a recognition model pretrained with a video having a maximal similarity to the key frame extracted from the input video, preprocessing the input video such that at least one of color and size of a video used as training data of an initial model is similar to that of the input video, and recognizing the preprocessed video based on the initial model.

Disclosed herein are an object recognition apparatus and method based on environment matching. The object recognition apparatus includes memory for storing at least one program, and a processor for executing the program, wherein the program performs extracting at least one key frame from a video that is input in real time, determining a similarity between the key frame extracted from the input video and each of videos used as training data of prestored multiple recognition models, based on a pretrained similarity-matching network, and selecting a recognition model pretrained with a video having a maximal similarity to the key frame extracted from the input video, preprocessing the input video such that at least one of color and size of a video used as training data of an initial model is similar to that of the input video, and recognizing the preprocessed video based on the initial model.

Systems and methods including one or more processors and one or more non-transitory storage devices storing computing instructions configured to run on the one or more processors and perform acts of receiving one or more digital images; identifying a foreground of the one or more digital images; analyzing the foreground of the one or more digital images to identify a skin region in the foreground of the one or more digital images; when the skin region is identified, clustering a non-skin remainder of the foreground of the one or more digital images into one or more clusters; extracting one or more patches of the one or more digital images from the one or more clusters of the foreground of the one or more digital images; determining one or more scores for the one or more patches of the one or more digital images; and coordinating displaying a patch of the one or more patches on an electronic display based on the one or more scores for the one or more patches. Other embodiments are disclosed herein.

Techniques are disclosed for determining whether to include a bodyprint in a cluster of bodyprints associated with a recognized person. For example, a device performs facial recognition to identify the identity of a first person. The device also identifies and stores physical characteristic information of the first person, the stored information associated with the identity of the first person based on the recognized face. Subsequently, the device receives a second video feed showing an image of a second person whose face is also determined to be recognized by the device. The device then generates a quality score for physical characteristics in the image of the user. The device can then add the image with the physical characteristics to a cluster of images associated with the person if the quality score is above a threshold, or discard the image if not.

In a disclosed example, a computer-implemented method includes storing image data that includes an input image of a blood sample within a blood monitoring device. The method also includes generating, by a machine learning model, a segmentation mask that assigns pixels in the input image to one of a plurality of classes, which correlate to respective known biophysical properties of blood cells. The method also includes extracting cell images from the input image based on the segmentation mask, in which each extracted cell image includes a respective cluster of the pixels assigned to a respective one of the plurality of classes.

In a disclosed example, a computer-implemented method includes storing image data that includes an input image of a blood sample within a blood monitoring device. The method also includes generating, by a machine learning model, a segmentation mask that assigns pixels in the input image to one of a plurality of classes, which correlate to respective known biophysical properties of blood cells. The method also includes extracting cell images from the input image based on the segmentation mask, in which each extracted cell image includes a respective cluster of the pixels assigned to a respective one of the plurality of classes.

A method and system are disclosed for generating a machine learning model for automatic classification of radiographic images acquired by various acquisition protocols. The method includes the steps of: providing a plurality of radiographic images, detecting and segmenting in each of the radiographic image at least one regions of interest (ROI) as reference ROI, measuring at least one radiomic feature per reference ROI, identifying valid reference ROIs based on the measured radiomics values, and clustering the measured radiomics values of valid reference ROIs into at least two reference clusters according to a set of characteristics of image acquisition. A method and system are disclosed for classifying radiographic images by applying a machine learning model generated for automatic classification of radiographic images.

A method and system are disclosed for generating a machine learning model for automatic classification of radiographic images acquired by various acquisition protocols. The method includes the steps of: providing a plurality of radiographic images, detecting and segmenting in each of the radiographic image at least one regions of interest (ROI) as reference ROI, measuring at least one radiomic feature per reference ROI, identifying valid reference ROIs based on the measured radiomics values, and clustering the measured radiomics values of valid reference ROIs into at least two reference clusters according to a set of characteristics of image acquisition. A method and system are disclosed for classifying radiographic images by applying a machine learning model generated for automatic classification of radiographic images.

The technology disclosed implements Human-in-the-loop (HITL) active learning with a feedback look via a user interface that is expressly designed for the suggested images to admit multiple fast feedbacks, including selection, dismissal, and annotation. Then, the downstream selection policy for subsequent sampling iterations is based on the available data interpreted in the context of the previous selections, dismissals, and annotations.

Techniques are disclosed for generating a sequence of symbols based on input data for a neuro-linguistic model. The model may be used by a behavior recognition system to analyze the input data. A mapper component of a neuro-linguistic module in the behavior recognition system receives one or more normalized vectors generated from the input data. The mapper component generates one or more clusters based on a statistical distribution of the normalized vectors. The mapper component evaluates statistics and identifies statistically relevant clusters. The mapper component assigns a distinct symbol to each of the identified clusters.