Technologies are described for a segment eraser, where predefined graphical shapes or ink entries (hand drawn shapes through touch, gesture, or similar input) are modified through deletion of one or more segments, rotation, resizing, color changes, and comparable ones. Binary shape operations such as union combination, fragmenting intersection, and subtraction are performed on underlying shapes to create new shapes in predictable ways. A segment eraser according to embodiments works from endpoint to endpoint of the underlying shape outlines, on dosed shape areas when the shapes have no outline, and on dry ink strokes up to intersections with other ink entries.

An appearance inspection apparatus includes an imaging unit, a reconstructed image generation unit, and an image comparison unit. The imaging unit images an object. The reconstructed image generation unit generates a reconstructed image by using a model, the reconstructed image being an image to be obtained by reconstruction of an input image, image data on the object imaged by the imaging unit being used as the input image, the model being used for attempting to reproduce the image data. The image comparison unit generates a difference image corresponding to a difference between the input image and the reconstructed image.

An image processing method. At least one light ray presented by a vector in a first affine coordinate system with a tip of the probe as an origin is projected from the tip of the probe. The light ray is intercepted from a projection surface satisfying a function in the first affine coordinate. A distance between the tip of the probe and an interception point of the light ray on the projection surface is obtained based on a rotation angle of the probe, a wavelength of the light ray, and a deflection angle of the light ray from the probe. A relationship between the first coordinate and a second affine coordinate system defined with the projection surface as a reference is obtained. Image data are acquired from the light ray reflected from the target surface presented in the first affine coordinate. The image data presented in the first affine coordinate are converted into image data presented in the second affine coordinate, and the image data in the second coordinate system are resampled by interpolating or extrapolating a gray scale.

An image generation device performs coordinate transformation, based on a coordinate transformation table, to a two-dimensional first image having a span in a horizontal direction and a vertical direction and acquired by overlooking and imaging an object from a first viewpoint at a first depression angle, and generates and outputs a second image which was obtained by overlooking the object from a second viewpoint which is different from the first viewpoint at a second depression angle which is different from the first depression angle. The coordinate transformation table is a table for transforming coordinates of a plurality of first selected pixels selected from a plurality of first pixels constituting the first image into coordinates of second selected pixels corresponding to a plurality of second pixels constituting the second image.

A system and a method for computing the relative rotation and the relative translation of back-to-back cameras comprise: a fixed mode and an adjustment mode. In the fixed mode, a reversible external camera is installed next to the back-to-back cameras, the external camera is flipped to shoot a calibration pattern from different positions and angles, the intrinsic and extrinsic parameters of each camera are computed, and then a reverse calculation is performed on the extrinsic parameters, as such the amount of translation and rotation of the back-to-back cameras are obtained. When the relative angle of the back-to-back cameras changes, the angle is automatically detected and the extrinsic parameters of the camera computed at the fixed mode is modified, and then the reverse calculation is performed on the extrinsic parameters to adjust the amount of translation and rotation of the back-to-back cameras.

Disclosed is a computer-implemented method of determining an assignment of an object acquire patient image data of interest recognizable in a digital medical patient image such as a tumour or other medical anomaly such as an implant to an anatomical region. The medical patient image is registered with atlas data, The assignment is then determined by calculating a score value defining an amount of volume intersection between the object of interest and a digital object defining a specific anatomic region, for example a bounding box around a specific organ, which is defined in the atlas data.

A phase estimation method estimates the phase of signal components using a point spread function. The method obtains a point spread function that expresses complex frequencies at a non integer location in terms of integral frequencies, for a complex frequency of a signal at a non integer location in a complex frequency domain. It obtains complex frequencies of the signal for the integral frequencies, and computes a sum of products of the complex frequencies of the signal at the integral frequencies with the corresponding complex values of the point spread function to provide an estimate of phase of the signal at the non integer location.

A card has a plurality of contact points attached on the same face thereof, the plurality of contact points being detectable by a detecting method for a touchscreen. In the case where the card is placed on the touchscreen, a recognition unit recognizes a kind of the card and a relative position and orientation of the card relative to the touchscreen on the basis of at least some of the plurality of contact points detected by the touchscreen. On the basis of the results of recognition by the recognition unit, a determining unit determines a relevant image to be displayed on the touchscreen and determines a size, position, and orientation for displaying the relevant image on the touchscreen.

A method for generating an output image from an input image and an input text instruction that specifies a location and a modification of an edit applied to the input image using a neural network is described. The neural network includes an image encoder, an image decoder, and an instruction attention network. The method includes receiving the input image and the input text instruction; extracting, from the input image, an input image feature that represents features of the input image using the image encoder; generating a spatial feature and a modification feature from the input text instruction using the instruction attention network; generating an edited image feature from the input image feature, the spatial feature and the modification feature; and generating the output image from the edited image feature using the image decoder.

Described herein are systems, methods, and instrumentalities associated with segmenting and/or determining the shape of an anatomical structure. An artificial neural network (ANN) is used to perform these tasks based on a statistical shape model of the anatomical structure. The ANN is trained by evaluating and backpropagating multiple losses associated with shape estimation and segmentation mask generation. The model obtained using these techniques may be used for different clinical purposes including, for example, motion estimation and motion tracking.