There is provided a method of re-classifying a clinically significant feature of a medical image as an artifact, comprising: feeding a target medical image captured by a specific medical imaging sensor at a specific setup into a machine learning model, obtaining a target feature map as an outcome of the machine learning model, wherein the target feature map includes target features classified as clinically significant, analyzing the target feature map with respect to sample feature map(s) obtained as an outcome of the machine learning model fed a sample medical image captured by at least one of: the same specific medical imaging sensor and the same specific setup, wherein the sample feature map(s) includes sample features classified as clinically significant, identifying target feature(s) depicted in the target feature map having attributes matching sample feature(s) depicted in the sample feature map(s), and re-classifying the identified target feature(s) as an artifact.

Exemplary embodiments of the present disclosure are directed towards system and method for generating scores and assigning quality index to videos on digital platform, comprising computing device that comprises video uploading module configured to allow user to record and upload videos on computing device, thereby transferring user uploaded videos to server over network. Server comprising video evaluating module configured to receive user uploaded videos and identifying video frames, thereby identifying different criteria. Video evaluating module configured to evaluate different criteria assigning scores to video frames and computing plurality of metrics of video frames based on assigned scores, then calculates mean and median values of metrics and assign mean and median values of video frame vectors, and combine video frame vectors of each video frame to obtain final video vector. Video evaluating module configured to assign weight to each value of final video vector to identify video quality index.

An apparatus acquires a plurality of candidate correct answer images for generating a correct answer image that is used for image quality evaluation, and detects a candidate correct answer image to be excluded from the acquired plurality of candidate correct answer images, based on differences between the acquired plurality of candidate correct answer images.

A system of image processing based emotion recognition is disclosed. The system principally comprises a camera and a main processor. Particularly, there a plurality of function units provided in the main processor, including: face detection unit, feature processing module, feature combination unit, conversion module, facial action judging unit, and emotion recognition unit. According to the present invention, the emotion recognition unit is configured to utilize a facial emotion recognition (FER) model to evaluate or distinguish an emotion state of a user based on at least one facial action, at least one emotional dimension, and a plurality of emotional scores. As a result, the accuracy of the emotion recognition conducted by the emotion recognition unit is significantly enhanced because basis of the emotion recognition comprises basic emotions, emotional dimension(s) and the user's facial action.

A device, a memory medium, a computer program, and a computer-implemented method for validating a data-based model for classifying an object into a class for an object type or a function type for a driver assistance system of a vehicle. The classification is determined as a function of a digital signal using the data-based model. A reference classification for the object is determined as a function of the digital signal, using a reference model. It is checked, as a function of the classification and the reference classification, whether or not the classification of the data-based model for the object is correct, and the data-based model is validated or not validated, depending on whether or not the classification is correct. The classification and the reference classification are determined for a set of digital signals that are associated with different distances between the object and a reference point.

A system and method allows a light colored image of an object such as a check to be detected and captured on a light colored background for uploading to a server for processing. Detection involves detecting edges of objects on the image, drawing a rectangle around the detected edges, testing for an aspect ratio of the rectangle within an approved range, testing for the rectangle being outside of a margin of the image and being a certain percentage of the image, and testing for blur within a tolerable range.

Aspects of the invention provide, in some aspects, a method of face recognition that includes receiving plural frames of a video stream imaging a candidate individual, e.g., in the field of view of a camera, and generating for each of those frames a score of the image and/or of the candidate therein. This can include, for example, a score (or count) indicative of the number of individuals present in the frame, a pose of the candidate individual (e.g., face-on or otherwise), blur in the image, and so forth. The method further includes selecting, based on the respective scores of the frames, a subset of the frames for matching by a face recognizer against a set of one or more images of designated individuals. That set may be of individuals approved for access, individuals to be prevented for access, or otherwise. An output is generated, according to the method, based on such matching by the face recognizer.

A trained model is retrained for video quality assessment and used to identify sets of adaptive compression parameters for transcoding user generated video content. Using transfer learning, the model, which is initially trained for image object detection, is retrained for technical content assessment and then again retrained for video quality assessment. The model is then deployed into a transcoding pipeline and used for transcoding an input video stream of user generated content. The transcoding pipeline may be structured in one of several ways. In one example, a secondary pathway for video content analysis using the model is introduced into the pipeline, which does not interfere with the ultimate output of the transcoding should there be a network or other issue. In another example, the model is introduced as a library within the existing pipeline, which would maintain a single pathway, but ultimately is not expected to introduce significant latency.

A face authentication environment determination method according to the present disclosure includes: capturing an image of a skin color chart (10) including a first skin color display part (BK) configured to show a skin color of a

Negroid and a second skin color display part (WT) configured to show a skin color of a Caucasoid, the first and the second skin color display parts being formed on a substrate; analyzing luminance of each of the first and the second skin color display parts (BK) and (WT) in the captured image of the skin color chart (10); and determining whether or not an image capturing environment is suitable for face authentication based on the luminance of each of the first and the second skin color display parts (BK) and (WT).

This disclosure relates to a system for performing efficient learning of a specific portion. To achieve this purpose, there is proposed a system configured to generate a converted image on the basis of input of an input image, the system comprising a learning model in which parameters are adjusted so as to suppress an error between the input image and a second image converted upon input of the input image, the learning model being subjected to different learning at least between a first area in the image and a second area different from the first area.