Provided is an information processing apparatus including an information acquisition section (104) that acquires information of a first region (700) specified by a filling input operation on image data (610) of a living tissue by a user, and a region determination section (108) that executes fitting on a boundary of the first region on the basis of the image data and information of the first region and determines a second region (702) to be subjected to predetermined processing.

Systems and methods for removing objects from images are disclosed. An image processing application identifies a boundary of each object of a set of objects in an image. The image processing application identifies a completed boundary for each object of the set of objects by providing the object to a trained model. The image processing application determines a set of masks. Each mask corresponds to an object of the set of objects and represents a region of the image defined by an intersection of the boundary of the object and the boundary of a target object to be removed from the image. The image processing application updates each mask by separately performing content filling on the corresponding region. The image processing application creates an output image by merging each of the updated masks with portions of the image.

An electronic device may include a camera, a display, and at least one processor. The at least one processor may be configured to display a first image obtained through the camera in a first area of the display, identify a plurality of areas included in the first image, identify a plurality of image effects applicable to the plurality of areas, display a plurality of second images to which the plurality of image effects are applied, respectively, in a second area adjacent to the first area, and display a third image resulting from applying an image effect corresponding to an image selected from among the plurality of second images to the first image.

In a method for time-of-flight (ToF) based measurement, a scene is illuminated using a ToF light source modulated at a first modulation frequency F.sub.MOD.sup.(1). While the light is modulated using F.sub.MOD.sup.(1), depths are measured to respective surface points within the scene, where the surface points are represented by a plurality of respective pixels. At least one statistical distribution parameter is computed for the depths. A second modulation frequency F.sub.MOD.sup.(2) higher than F.sub.MOD.sup.(1) is determined based on the at least one statistical distribution parameter. The depths are then re-measured using F.sub.MOD.sup.(2) to achieve a higher depth accuracy.

A method for visualizing data generated by a robotic device is presented. The method includes displaying an intended path of the robotic device in an environment. The method also includes displaying a first area in the environment identified as drivable for the robotic device. The method further includes receiving an input to identify a second area in the environment as drivable and transmitting the second area to the robotic device.

A marker-coordinate detecting unit detects coordinates of a stent marker on a new image when the new image is stored in an image-data storage unit; and then a correction-image creating unit creates a correction image from the new image through, for example, image transformation processing, so as to match up the detected coordinates with reference coordinates that are coordinates of the stent marker already detected by the marker-coordinate detecting unit in a first frame. An image post-processing unit then creates an image for display by performing post-processing on the correction image created by the correction-image creating unit, the post-processing including high-frequency noise reduction filtering-processing, low-frequency component removal filtering-processing, and logarithmic-image creating processing; and then a system control unit performs control of displaying a moving image of an enlarged image of a set region that is set in the image for display, together with an original image.

Disclosed are a high-precision semi-automatic image data labeling method, an electronic apparatus and a non-transitory computer-readable storage medium. The high-precision semi-automatic image data labeling method may include: displaying a to-be-labeled image, the to-be-labeled image comprising a selected area and an unselected area; acquiring a coordinate point of the unselected area and a first range value; executing a grabcut algorithm based on the coordinate point of the unselected area and the first range value acquired, and obtaining a binarized image divided by the grabcut algorithm; executing an edge tracking algorithm on the binarized image to acquire current edge coordinates; updating a local coordinate set based on the current edge coordinates acquired; updating the selected area of the to-be-labeled image based on the local coordinate set acquired.

A localized evaluation network incorporates a discriminator acting as classifier, which may be included within a generative adversarial network (GAN). GAN may include a generative network such as U-NET for creating segmentations. The localized evaluation network is trained on image pairs including medical images of organs of interest and segmentation (mask) images. The network is trained to distinguish whether an image pair does or does not represent the ground truth. GAN examines interior layers of the discriminator and evaluates how much each localized image region contributes to the final classification. The discriminator may analyze regions of the image pair that contribute to a classification by analyzing layer weights of the machine learning model. Disclosed embodiments include a visual attribute, such as a heat map, that represents contributions of localized regions of a contour to an overall confidence score. These localized regions may be highlighted and reported for quality assurance review.

A medical image processing device a reference image that is a medical image with which boundary line information related to a boundary line that is a boundary between an abnormal region and a normal region and landmark information related to a landmark that is a characteristic structure of the subject are associated and a captured image that is the medical image captured in real time, detects the landmark from the captured image, calculates a ratio of match between the landmark included in the reference image and the landmark included in the captured image, estimates a correspondence relationship between the reference image and the captured image on the basis of the ratio of match and information regarding the landmarks included in the reference image and the captured image, and generates a superimposition image in which the boundary line associated with the reference image is superimposed on the captured image on the basis of the correspondence relationship.

An image tracking apparatus, which tracks a subject in a moving image, comprising: an image acquisition device that acquires image data indicating the moving image; a first detector that detects a first area of a subject in acquired image data to track the first area in the moving image; a second detector that detects a second area of a subject in the image data to track the second area in the moving image; and a controller that tracks the subject by switching between a tracking result of the first detector and a tracking result of the second detector. The first and the second detector operate independently. The controller tracks the subject using the tracking result of the first detector when the first area is tracked, and tracks the subject using the tracking result of the second detector when the first area is not tracked and the second area is tracked.