Uniform light irradiation to the measurement object is realized, and it is possible to obtain high resolution image information, to provide a reflective optical sensor. An optical sensor comprises a light irradiation unit for performing uniform light irradiation with respect to the measurement object, the viewing angle control plate for limiting the direction of the reflected light in the measurement object to the predetermined direction, a light detecting unit for acquiring image information showing the distribution of the reflected light obtained through the viewing angle control plate. The viewing angle control plate is a louver array film or a fiber optic plate.

An autonomous robotic vehicle is capable of detecting, identifying, and locating the source of gas leaks such as methane. Because of the number of operating components within the vehicle, it may also be considered a robotic system. The robotic vehicle can be remotely operated or can move autonomously within a jobsite. The vehicle selectively deploys a source detection device that precisely locates the source of a leak. The vehicle relays data to stakeholders and remains powered that enables operation of the vehicle over an extended period. Monitoring and control of the vehicle is enabled through a software interface viewable to a user on a mobile communications device or personal computer.

A method includes patterning a hard mask over a target layer, capturing a low resolution image of the hard mask, and enhancing the low resolution image of the hard mask with a first machine learning model to produce an enhanced image of the hard mask. The method further includes analyzing the enhanced image of the hard mask with a second machine learning model to determine whether the target layer has defects.

A medical image processing apparatus includes an image processor configured to: receive a plurality of first image data captured at different times and generated by illumination of light in a first wavelength band in sequence; receive a plurality of second image data captured at different times and generated by illumination of light in a second wavelength band different from the first wavelength band in sequence; generate first and second images based on the received first and second image data, respectively; and output the generated first image and second image to a display in chronological order of the first and second images and in accordance with a preset display pattern of the first and second images.

A medical image processing apparatus includes an image processor configured to: receive a plurality of first image data captured at different times and generated by illumination of light in a first wavelength band in sequence; receive a plurality of second image data captured at different times and generated by illumination of light in a second wavelength band different from the first wavelength band in sequence; generate first and second images based on the received first and second image data, respectively; and output the generated first image and second image to a display in chronological order of the first and second images and in accordance with a preset display pattern of the first and second images.

The present exemplary embodiments provide a hybrid sensor, a Lidar sensor, and a moving object which generate composite data by mapping distance information on an obstacle obtained through the Lidar sensor to image information on an obstacle obtained through an image sensor and predict distance information of composite data based on intensity information of a pixel, to generate precise composite data.

A computer-implemented method for evaluating an image classifier, in which a classifier output of the image classifier is provided for the actuation of an at least semi-autonomous robot. The evaluation method includes: ascertaining a first dataset including image data and annotations being assigned to the image data, the annotations including information about the scene imaged in the respective image and/or about image regions to be classified and/or about movement information of the robot; ascertaining regions of the scenes that are reachable by the robot based on the annotations; ascertaining relevance values for image regions to be classified by the image classifier; classifying the image data of the first image dataset with the aid of the image classifier; evaluating the image classifier based on image regions correctly classified by the image classifier and incorrectly classified image regions, as well as the calculated relevance values of the corresponding image regions.

A learning data generation device that can generate learning data suitable for learning of an identification model. The learning data generation device has a function of cutting out part of first image data as second image data, a function of generating a two-dimensional graphic corresponding to the area of the second image data and representing a pseudo defect, a function of generating third image data by combining the second image data and the two-dimensional graphic, and a function of assigning a label corresponding to the two-dimensional graphic to the third image data. By using the third image data for learning of the identification model, a highly accurate identification model can be generated.

Some example embodiments relate to a super resolution scanning electron microscope (SEM) image implementing device and/or a method thereof. Provided a super resolution scanning electron microscope (SEM) image implementing device comprising a processor configured to crop a low resolution SEM image to generate a first cropped image and a second cropped image, to upscale the first cropped image and the second cropped image to generate a first upscaled image and a second upscaled image, and to cancel noise from the first upscaled image and the second upscaled image to generate a first noise canceled image and a second noise canceled image.

Disclosed are various systems and features for use with a machine, such as a sewing machine, to facilitate augmented-reality features such as projecting assistive visual elements or virtual UI elements into an operational area. Such systems and features may be useful in the context of performing an action along a self-guided path on a substrate.