One embodiment can include a system for providing an image-capturing recommendation. During operation the system receives, from a mobile computing device, one or more images. The one or more images are captured by one or more cameras associated with the mobile computing device. The system analyzes the received images to obtain image-capturing conditions for capturing images of a target within a physical space; determines, based on the obtained image-capturing conditions and a predetermined image-quality requirement, one or more image-capturing settings; and recommends the determined one or more image-capturing settings to a user.

Provided is a method of providing an image, the method including obtaining an image, extracting a haptic feature from an edge of the obtained image, generating a vibration pattern signal corresponding to the extracted haptic feature based on vibration pattern data, and generating vibration corresponding to a vibration generation request according to the generated vibration pattern signal when there is the vibration generation request.

Provided is a method of providing an image, the method including obtaining an image, extracting a haptic feature from an edge of the obtained image, generating a vibration pattern signal corresponding to the extracted haptic feature based on vibration pattern data, and generating vibration corresponding to a vibration generation request according to the generated vibration pattern signal when there is the vibration generation request.

Described herein are systems and techniques for generating training data for use in training a machine learning model for image enhancement. The system may access a target image of a displayed video frame, wherein the target image represents a target output of the machine learning model. The system may access an input image of the displayed video frame, wherein the input image corresponds to the target image and represents an input to the machine learning model. The system may train the machine learning model using the target image and the input image corresponding to the target image to obtain a trained machine learning model.

Described herein are systems and techniques for generating training data for use in training a machine learning model for image enhancement. The system may access a target image of a displayed video frame, wherein the target image represents a target output of the machine learning model. The system may access an input image of the displayed video frame, wherein the input image corresponds to the target image and represents an input to the machine learning model. The system may train the machine learning model using the target image and the input image corresponding to the target image to obtain a trained machine learning model.

Disclosed is a calibration method for a multi-degree-of-freedom movable vision system. A calibration template is placed in front of a camera component, each degree of freedom of movement of the camera component is rotated, several images including features of the calibration template are recorded using the camera component, position information of each degree of freedom of movement when the corresponding images are acquired is also recorded, and calibration results of the camera component and each degree of freedom of movement are calculated using a calculation component. The movable vision system comprises the camera component, the calculation component and a control component. The method has good calculation real-time performance, parameter information of position changes of the camera component can be acquired in real time by means of the calculation of the position information of each degree of freedom of movement, the problem of errors, caused by machining and assembly, with a theoretical design can be solved, and the method has broad application prospects.

A method for scoring attractiveness of a real estate property including receiving an image from a subject real estate property; converting the image to RGB data; and identifying an attractiveness score from the RGB data. A real estate property attractiveness scoring application including an image processor to receive an image from a subject real estate property and convert the image to RGB data; a database of RGB data and an attractiveness score for the RGB data; and an artificial intelligence engine in communication with the image processor and the database to identify an attractiveness score in the database from the RGB data from the image processor.

A method for scoring attractiveness of a real estate property including receiving an image from a subject real estate property; converting the image to RGB data; and identifying an attractiveness score from the RGB data. A real estate property attractiveness scoring application including an image processor to receive an image from a subject real estate property and convert the image to RGB data; a database of RGB data and an attractiveness score for the RGB data; and an artificial intelligence engine in communication with the image processor and the database to identify an attractiveness score in the database from the RGB data from the image processor.

A peer-review flagging system is operable to receive a medical scan and a medical report written by a medical professional in conjunction with review of the medical scan. Automated assessment data is generated by performing an inference function on the medical scan by utilizing a computer vision model trained on a plurality of medical scans. Human assessment data is generated by performing an extraction function on the medical report. Consensus data is generated by comparing the automated assessment data to the first human assessment data. A peer-review notification is transmitted to a client device for display. The peer-review notification indicates the medical scan is flagged for peer-review in response to determining the consensus data indicates the automated assessment data compares unfavorably to the human assessment data.

A peer-review flagging system is operable to receive a medical scan and a medical report written by a medical professional in conjunction with review of the medical scan. Automated assessment data is generated by performing an inference function on the medical scan by utilizing a computer vision model trained on a plurality of medical scans. Human assessment data is generated by performing an extraction function on the medical report. Consensus data is generated by comparing the automated assessment data to the first human assessment data. A peer-review notification is transmitted to a client device for display. The peer-review notification indicates the medical scan is flagged for peer-review in response to determining the consensus data indicates the automated assessment data compares unfavorably to the human assessment data.