Embodiments of the present disclosure provide a method for training a face fusion model and an electronic device. The method includes: performing a first face changing process on a user image and a template image to generate a reference template image; adjusting poses of facial features of the template image based on the reference template image to generate a first input image; performing a second face changing process on the template image to generate a second input image; inputting the first input image and the second input image into a generator of an initial face fusion model to generate a fused face area image; and inputting the fused image and the template image into a discriminator of the initial face fusion model to obtain a result, and performing backpropagation correction on the initial face fusion model based on the result to generate a face fusion model.

An information processing device includes: a memory; and a processor coupled to the memory and configured to: accept specification of a feature region that includes a characteristic portion in a learning image; create first data that indicates a degree of overlap between the feature region and each of divided regions obtained by dividing the learning image; perform image conversion on the learning image by an image classification program in which element filters are combined; calculate an image feature value of each of the divided regions from an image obtained; create second data that indicates the image feature value corresponding to each of the divided regions; calculate a similarity between the first data and the second data; and use a result of evaluating the image classification program on the basis of the similarity, for execution of a genetic programming.

A machine learning system can generate an image mask (e.g., a pixel mask) comprising pixel assignments for pixels. The pixels can he assigned to classes, including, for example, face, clothes, body skin, or hair. The machine learning system can be implemented. using a convolutional neural network that is configured to execute efficiently on computing devices having limited resources, such as mobile phones. The pixel mask can be used to more accurately display video effects interacting with a user or subject depicted in the image.

An occupant monitoring device includes processing circuitry to detect an eye of an occupant on a vehicle and to determine an eye opening degree of the eye using an image captured by an image capturing device having an automatic exposure adjusting function for adjusting an exposure time; to determine that the eye is closed when the eye opening degree of the eye is less than a predetermined eye opening degree threshold value; to deactivate the automatic exposure adjusting function when the automatic exposure adjusting function is active and the eye is determined to be closed; to detect brightness in a vicinity of the eye using an image which is captured by the image capturing device after the automatic exposure adjusting function is deactivated; and when the eye is determined to be closed, to determine that the occupant is in a drowsy state when the brightness in the vicinity of the eye is less than a predetermined brightness threshold value, and to determine that the occupant is in an awake state when the brightness is equal to or greater than the predetermined brightness threshold value.

A data labeling method, apparatus and system are provided. The method includes: sampling a data source according to an evaluation task for the data source to obtain sampled data; generating a labeling task from the sampled data; sending the labeling task to a labeling device; and receiving a labeled result of the labeling task from the labeling device. As such, an automatic evaluation of data can be implemented by using the evaluation task, and evaluation efficiency is improved.

Defects of interest can be captured by a classifier. Images of a semiconductor wafer can be received at a deep learning classification module. These images can be sorted into soft decisions with the deep learning classification module. A class of the defect of interest for an image can be determined from the soft decisions. The deep learning classification module can be in electronic communication with an optical inspection system or other types of semiconductor inspection systems.

A computing system may automatically classify applications that are used via a communication network. Application classification may include identifying a signature or group of signatures that belongs to an application or service associated with data flow through a network. The computer system of the network may collect data regarding the application from a mobile device, from the network, and/or from a digital distribution service accessible via the network. The system may combine such data together to identify and classify the application.

An image processing apparatus that is capable of improving subject detection accuracy with respect to image signals is disclosed. The image processing apparatus applies subject detection processing to an image by using a learning model generated based on machine learning. The image processing apparatus selects the learning model from a plurality of learning models stored in advance, in accordance with characteristics of the image to which the subject detection processing is to be applied.

A target object tracking system (1) includes a processor (5) for receiving image data (S1) captured by one or more sensor (7) disposed on the vehicle (2). The processor (5) is configured to analyse the image data to identify image components (IMC(n)) and to determine a movement vector (V(n)) of each image component (IMC(n)). The movement vectors each include a magnitude and a direction. At least one of the image components (IMC(n)) is classified as a target image component relating to the target object and at least one of the remaining image components (IMC(n)) as a non-target image component. The movement vector (V(n)) of the at least one target image component is modified in dependence on the movement vector of the or each non-target image component. The target object (3) is tracked in dependence on the modified movement vector of the at least one target image component. The disclosure also relates to a method and a non-transitory computer-readable medium.

A robotic fruit picking system includes an autonomous robot that includes a positioning subsystem that enables autonomous positioning of the robot using a computer vision guidance system. The robot also includes at least one picking arm and at least one picking head, or other type of end effector, mounted on each picking arm to either cut a stem or branch for a specific fruit or bunch of fruits or pluck that fruit or bunch. A computer vision subsystem analyses images of the fruit to be picked or stored and a control subsystem is programmed with or learns picking strategies using machine learning techniques. A quality control (QC) subsystem monitors the quality of fruit and grades that fruit according to size and/or quality. The robot has a storage subsystem for storing fruit in containers for storage or transportation, or in punnets for retail.