A processing system includes a hardware processor. The hardware processor obtains first read data of a sample image for image data included in user desired job data for image formation, searches job data stored in a storage section for data of a similar image to an image included in the obtained first read data to detect the data of the similar image, outputs a list of the detected data of the similar image, causes an image forming apparatus to form an image based on selected data selected from the data included in the list, obtains second read data of the formed image, and performs color adjustment on the selected data based on the first read data and the second read data.

Methods and image processing systems are provided for determining a dominant gradient orientation for a target region within an image. A plurality of gradient samples are determined for the target region, wherein each of the gradient samples represents a variation in pixel values within the target region. The gradient samples are converted into double-angle gradient vectors, and the double-angle gradient vectors are combined so as to determine a dominant gradient orientation for the target region.

The current document is directed to automated methods and systems, controlled by various constraints and parameters, that identify document sub-images within digital images. Certain of the parameters constrain contour identification and document-subimage-hypothesis generation. The currently described methods and systems identify contours within the digital image, partition the identified contours into four contour sets corresponding to four different regions of the original digital image, construct hypotheses based on these contours for the edges or boundaries of a digital sub-image, and evaluate the hypotheses in order to select a most highly scored hypotheses as a representation of the borders of a digital sub-image within the original received digital image.

The current document is directed to automated methods and systems, controlled by various constraints and parameters, that identify contours in digital images, including curved contours. Certain of these parameters constrain contour identification to those contours in which the local curvature of a contour does not exceed a threshold local curvature and to those contours orthogonal to intensity gradients of at least threshold magnitudes. The currently described methods and systems identify seed points within a digital image, extend line segments from the seed points as an initial contour coincident with the seed point, and then iteratively extend the initial contour by adding line segments to one or both ends of the contour. The identified contours are selectively combined and filtered in order to identify a set of relevant contours for use in subsequent image-processing tasks.

The present application provides a method and system for extracting nail contours. The method is based on the system and comprises: the step of data acquisition: carrying out 3D scanning to obtain finger 3D data of a target finger, wherein the finger 3D data include complete nail 3D data: the step of point cloud processing of data: converting the finger 3D data converted into 3D point cloud data to obtain a finger point cloud, wherein the finger point cloud includes a nail curved-surface point cloud; the step of nail point cloud acquisition: segmenting the finger point cloud to obtain the nail curved-surface point cloud; the step of nail point cloud flattening: flattening the nail curved-surface point cloud in an equal surface area manner to obtain a nail plane point cloud in a planar state; and the step of nail contour drawing: drawing a nail contour according to the contour of the nail plane point cloud. According to the method and system for extracting nail contours, the nail plane point cloud formed after the nail curved-surface point cloud of the curved nail is flattened is obtained, and the shape of an outer contour point cloud of the flattened nail plane point cloud better fits the outer contour of the curved nail.

Disclosed herein is an image frame extraction apparatus that acquires a video image; extracts features of each of a plurality of image frames of the acquired video image; analyzes the extracted features of each of the plurality of image frames, and extracts candidates of a representative frame from the plurality of image frames; and, for each of the extracted candidates of the representative frame, calculates a displacement in a shape space of a pose of an object in the image frame with respect to a reference pose, and select the representative frame from the candidates of the representative frame based on the calculated displacement in the shape space.

The inventive concepts provide a method of providing a stochastic prediction system. The method includes extracting contours of patterns corresponding to a first design layout from a plurality of scanning electron microscope (SEM) images, respectively, generating a first contour histogram image based on the contours, and training a stochastic prediction model by using the first contour histogram image as an output, and by using the first design layout and a first resist image, a first aerial image, a first slope map, a first density map, and/or a first photo map corresponding to the first design layout as inputs, in which the stochastic prediction model comprises a cycle generative adversarial network (GAN).

A computer device obtains a to-be-segmented image that includes a plurality of extreme points corresponding to a target object in the to-be-segmented image. The device generates first image feature information according to the to-be-segmented image. The first image feature information includes N image matrices and a first heatmap that is generated according to the extreme points. The device obtains a first image segmentation region corresponding to the first image feature information using a first image segmentation model. The device also obtains a second heatmap according to an annotation point corresponding to the first image segmentation region and the first image segmentation region. The device obtains a second image segmentation region corresponding to the to-be-segmented image using a second image segmentation model. The device generates an image recognition result according to the second image segmentation region.

The present disclosure relates to a method for controlling a movable platform. The method may include obtaining a target object selection operation input by a user on an interaction interface, the interaction interface displaying a three-dimensional model of an operation area, the target object selection operation configured to determine a position of a target object in the operation area; determining a target orientation of the movable platform when the target object is operated based on an orientation of the three-dimensional model displayed on the interaction interface when the target object selection operation is obtained; and determining a target position of the movable platform when the movable platform performs operation on the target object based on the position of the target object, the target orientation, and an operation distance of the movable platform.

Disclosed is a data processing method including obtaining deviation information indicating a degree of deviation of target data from reference data, and displaying, on the target data, information corresponding to the deviation information, wherein the obtaining of the deviation information includes obtaining the deviation information by comparing data other than data that deteriorates reliability of the deviation information from among the target data with the reference data.