Examples described herein relate to automatic identification and transformation of a color region. A user can identify a region of a video frame or image that corresponds to a color region that is to be segmented. A color region can include one or more colors that appear to be approximately a uniform color. For one or more video frames, gamma correction can be applied to frames of the video. One or more frames of a video can be mapped to two color spaces. For each pixel in an image, a determination is made if the pixel has the same color as that of the identified region based on each of the at least two color spaces identifying the pixel as the color. The color region can be identified throughout a video and transformed to another color to aid in video editing.

Various embodiments of the present disclosure are directed towards a method for generating a risk group classification for an African American (AA) patient. The method includes extracting a first plurality of architectural features from a digitized H&E slide image of the AA patient. A risk score for the AA patient is generated based on the first plurality of architectural features, where the risk score is prognostic of overall survival (OS) of the AA patient. The risk group classification is generated for the AA patient, where generating the risk group classification includes classifying the AA patient into either a high risk group or a low risk group based on the risk score, where the high risk group indicates the AA patient will die before a threshold date and the low risk group indicates the AA patient will die after or on the threshold date.

An apparatus includes at least one memory configured to store instructions, and at least one processor in communication with the at least one memory and configured to execute the instructions to perform same area dividing on each of a plurality of images, and create a composite image from the plurality of images. A first area of the composite image is composited from corresponding areas in a first number of image among the plurality of images. A second area of the composite image is composited from corresponding areas in a second number of image among the plurality of images.

A method of detecting product defects obtains an image of a product and sets a region of interest (ROI) of the image. A first contour of a first target object is detected in the region of interest. The image is detected according to the first contour to obtain a corrected image. A position difference between the first contour and a second target object in the region of interest is obtained. A second contour of the second target object is detected in the corrected image according to the position difference. A first image area corresponding to the first contour and a second image area corresponding to the second contour are segmented and input into an autoencoder. According to outputs of the autoencoder, whether the product is defective is determined. A detection result of the product is output. The method can detect defects on products quickly and accurately.

An image processing component is provided. The apparatus includes: a display control configured to display an image to be edited in a first region of a display region or display a target image obtained from an image to be edited; an automatic image processing control configured to enter an automatic mode in response to a first control instruction; and a manual image processing control configured to enter a manual mode in response to a second control instruction. In the automatic mode and/or manual mode, the electronic device can switch between different graph repairing functions in response to a function switching instruction.

Discussed herein are devices, systems, and methods for organ mask generation. A device, system and method for organ mask generation including generating a synthetic centroid mask, identifying first and second intensity thresholds, in a first segmentation pass, setting (i) pixels of an image with intensities less than the first threshold to zero and (ii) pixels of the image corresponding to objects with centroids outside the synthetic centroid mask to zero, resulting an initial organ mask, in a second segmentation pass, setting pixels (i) with intensities less than the second threshold, the second threshold less than the first threshold to zero and (ii) setting pixels corresponding to objects with centroids outside the initial organ mask to zero, resulting in a second organ mask, and expanding and filling the second organ mask to generate an organ mask.

An interactive 3D cursor facilitates selection and manipulation of a three-dimensional volume from a three-dimensional image. The selected volume image may be transparency-adjusted and filtered to remove selected tissues from view. Qualitative and quantitative analysis of tissues in a selected volume may be performed. Location indicators, annotations, and registration markers may be overlaid on selected volume images.

An image processing apparatus comprises a changing unit configured to change a display area of an image from a first display area to a second display area including at least a portion of the first display area, an acquiring unit configured to acquire a first value indicating luminance, in which brightness contrast is considered, in an image displayed in the first display area and a second value indicating luminance, in which brightness contrast is considered, in an image displayed in the second display area, and a correcting unit configured to correct luminance of the image displayed in the second display area based on the first value and the second value that are acquired by the acquiring unit.

A processor-implemented method with object tracking includes: determining an initial template image based on an input bounding box and an input image; generating an initial feature map by extracting features from the initial template image; generating a transformed feature map by performing feature transformation adapted to objectness on the initial feature map; generating an objectness probability map and a bounding box map indicating bounding box information corresponding to each coordinate of the objectness probability map by performing objectness-based bounding box regression analysis on the transformed feature map; and determining a refined bounding box based on the objectness probability map and the bounding box map.

A computer vision-based system provides for load distribution estimation and load rating and vibration serviceability assessment of structures. The system integrates evaluates the structural load carrying capacity, the diagnosis and prognosis of performance and safety, and vibration serviceability. Cameras record images of a structure, and regions of interest are monitored in those images for their displacement and velocity as loading varies. Where the displacement determined exceeds a predetermined threshold, or where the acceleration determined exceeds predetermined limits, or where the distribution of displacements of parts of the structure deviates substantially from an estimated displacement distribution, an output indicating potential problems with the structure is output.