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.

Object detection is performed for an image acquired by imaging using an array sensor in which a plurality of imaging elements are arranged one-dimensionally or two-dimensionally, some of the imaging elements are configured as color-filter-disposed pixels in which a color filter is disposed in an incident optical path, and color information acquisition points are formed by the color-filter-disposed pixels. Then, coloring processing in a pixel range of a detected object is performed by referring to color information acquired at the color information acquisition points corresponding to the inside of the pixel range of the detected object.

Object detection is performed for an image acquired by imaging using an array sensor in which a plurality of imaging elements are arranged one-dimensionally or two-dimensionally, some of the imaging elements are configured as color-filter-disposed pixels in which a color filter is disposed in an incident optical path, and color information acquisition points are formed by the color-filter-disposed pixels. Then, coloring processing in a pixel range of a detected object is performed by referring to color information acquired at the color information acquisition points corresponding to the inside of the pixel range of the detected object.

An electronic device is provided. The electronic device includes a memory, an image sensor including light receiving elements each including at least two sub light receiving elements, and an image signal processor. The image signal processor is configured to obtain images corresponding to light from outside by using the image sensor, the images including at least a raw image, a first sub image, and a second sub image, the first sub image being an image corresponding to light detected by at least one first sub light, the second sub image being an image corresponding to light detected by at least one second sub light, identify a luminance ratio between the first sub image and the second sub image, identify a defect in the raw image, based on the luminance ratio, and perform a function corresponding to a type of the defect.

An electronic device is provided. The electronic device includes a memory, an image sensor including light receiving elements each including at least two sub light receiving elements, and an image signal processor. The image signal processor is configured to obtain images corresponding to light from outside by using the image sensor, the images including at least a raw image, a first sub image, and a second sub image, the first sub image being an image corresponding to light detected by at least one first sub light, the second sub image being an image corresponding to light detected by at least one second sub light, identify a luminance ratio between the first sub image and the second sub image, identify a defect in the raw image, based on the luminance ratio, and perform a function corresponding to a type of the defect.

A computer-implemented method is described. The method is be implemented by processors of an aircraft system. The method includes receiving images of an eye and a lighting configuration associated with a cockpit of an aircraft. The method further includes detecting a position of the eye within each of the images. The method further includes compensating for a pupillary light response of the eye based on the position of the eye within the image and the lighting configuration. By compensating for the pupillary light response, a fatigue level of the operator is estimated with reduced noise.

Disclosed are an image processing method and apparatus, a computer device and a storage medium, which relate to the field of artificial Intelligence. The method includes receiving an original image; performing image photographing defect detection and color deviation detection on the original image, the image photographing defect detection determining whether there exists a photographing defect that is irreparable through image processing, the color deviation detection determining whether there exists color cast in the original image; performing color correction on the original image if the original image passes the image photographing defect detection and does not pass the color deviation detection; and generating a target image based on the color-corrected original image.

An apparatus comprises at least one processor; at least one memory storing at least one program; a light source; and a capturing unit configured to acquire image data by capturing an image formed on a medium, wherein the program, when executed by the processor, causes the processor to extract information multiplexed in the image from an image captured by the capturing unit, adjust the light amount of the light source to a light amount that accords with a reflection intensity of the medium on which the image is formed, capture the image, and extract information from the image.

An apparatus comprises at least one processor; at least one memory storing at least one program; a light source; and a capturing unit configured to acquire image data by capturing an image formed on a medium, wherein the program, when executed by the processor, causes the processor to extract information multiplexed in the image from an image captured by the capturing unit, adjust the light amount of the light source to a light amount that accords with a reflection intensity of the medium on which the image is formed, capture the image, and extract information from the image.

Methods of autonomous diagnostic verification and detection of defects in the optical components of a vision-based inspection system are provided. The method includes illuminating a light panel with a first light intensity pattern, capturing a first image of the first light intensity pattern with a sensor, illuminating the light panel with a second light intensity pattern different than the first light intensity pattern, capturing a second image of the second light intensity pattern with the sensor, comparing the first image and the second image to generate a comparison of images, and identifying defects in the light panel or the sensor based upon the comparison of images. Systems adapted to carry out the methods are provided as are other aspects.