An image processing method, an image processing device, an electronic device, and a storage medium are provided. The method comprises: performing image segmentation on a first image which is to be processed, obtaining an initial mask image according to an image segmentation result, in response to determining that the first image satisfies a preset sky area replacement condition according to the initial mask image, obtaining a target mask image by performing guided filtering on the initial mask image by using a greyscale image of the first image as a guide image, acquiring a target sky scene, and obtaining a second image by performing replacement on the sky area in first the image according to the target mask image and the target sky scene.

According to an embodiment, a mobile object control device includes a recognizer configured to recognize a surrounding situation of a mobile object on the basis of an output of an external sensor and a marking recognizer configured to recognize markings for dividing an area through which the mobile object passes on the basis of the surrounding situation recognized by the recognizer. The marking recognizer extracts a prescribed area from the surrounding situation when it is determined that marking recognition accuracy has been lowered, extracts an edge within the extracted prescribed area, and recognizes the markings on the basis of an extraction result.

An on-board outside recognition apparatus extracts a feature point from an image including an environment around a user's own vehicle, measures a three-dimensional position of the feature point based on movement of the feature point tracked in time series on the image and calculates a foot position of the feature point on the image from the three-dimensional position of the feature point is performed. Then, the on-board outside recognition apparatus extracts a road surface area where the user's own vehicle can travel, from the image using a degree of similarity of a texture of the image, and judges a feature point the foot position of which is judged not to be included in the road surface area to be highly reliable.

A measurement method, which is performed by a measurement device that measures a displacement of a target object, includes: receiving designation of a designation point on a first image that includes the target object; setting a plurality of set points, based on the designation point; identifying a direction of a line that connects two of the plurality of set points; generating a second image by rotating the first image, the second image being an image in which the identified direction of the line is horizontal or vertical; setting, in the second image, a measurement region that partially includes the line; and measuring the displacement of the target object in the measurement region, the displacement being a displacement in a direction orthogonal to the line.

Embodiments of the disclosure provide a method for detecting bad points in a video, including: performing extreme filtering respectively on first, second and third frames of images which are sequentially and continuously in the video to obtain first, second and third filtered images, respectively; wherein the extreme filtering is one of maximum filtering and minimum filtering; determining first and second difference images according to the first, second and third filtered images; determining a candidate image according to the first and second difference images; and determining that at least part of points in the second frame of image corresponding to the valid point in the candidate image are bad points. The embodiment of the disclosure also provides a device and a computer-readable medium for detecting bad points in the video.

A method for adjusting camera intrinsic parameters of a multi-camera visual tracking device is described. In one aspect, a method for calibrating the multi-camera visual tracking system includes disabling a first camera of the multi-camera visual tracking system while a second camera of the multi-camera visual tracking system is enabled, detecting a first set of features in a first image generated by the first camera after detecting that the temperature of the first camera is within the threshold of the factory calibration temperature of the first camera, and accessing and correcting intrinsic parameters of the second camera based on the projection of the first set of features in the second image and a second set of features in the second image.

A method for mitigating motion blur in a visual-inertial tracking system is described. In one aspect, the method includes accessing a first image generated by an optical sensor of the visual tracking system, accessing a second image generated by the optical sensor of the visual tracking system, the second image following the first image, determining a first motion blur level of the first image, determining a second motion blur level of the second image, identifying a scale change between the first image and the second image, determining a first optimal scale level for the first image based on the first motion blur level and the scale change, and determining a second optimal scale level for the second image based on the second motion blur level and the scale change.

An image processing apparatus acquires a first pixel size and a second pixel size that are pixel sizes in a predetermined axis direction of a first image and a second image captured at different points in time, respectively, determines whether the first pixel size and second pixel size differ from each other, and decides, if the first pixel size differs from the second pixel size, a size in a predetermined axis direction of a comparison area based on a larger one of the first pixel size and the second pixel size. The comparison area includes a plurality of gray levels, and is compared to a gray level of a position of interest in one of the first and second image, and the comparison area existing in the other of the first and second image, different from the one image.

Provided is a medical image processing apparatus including: an image acquisition unit that obtains a medical image of a subject; and an image processing unit that generates a second medical image by applying predetermined image processing on the medical image. The image processing unit includes: a surface area extraction unit that extracts a surface area including information that can lead to individuality determination or identification of the subject, from the medical image; a body orientation determination unit that determines body orientation on the basis of the surface area; a surface area deformation unit that deforms the surface area of the medical image; and an object assignment unit that assigns an object indicating the body orientation to the medical image in which the surface area has been deformed and that generates the second medical image.

An operating method with goods information is applicable to an electronic device. The operation method includes: obtaining image information associated with one or more goods objects on a target electronic shelf among a plurality of electronic shelves in a network; and performing first communicating with a server for controlling the electronic shelves in the network according to either or both of the image information and feature information associated with the one or more goods objects, wherein feature information is extracted from the image information and the first communicating includes wirelessly transmitting either or both of the image information and the feature information associated with the one or more goods objects to the server.