Disclosed herein is a method for providing image contents. The method for providing image contents includes: dividing the image contents into a plurality of scenes, each scene including a plurality of shots; classifying image frames for each scene depending on each of a plurality of characters appearing in the image contents; receiving a user input for selecting any one of the plurality of characters; and displaying a scene corresponding to the character selected depending on the user input.

Devices, systems and methods for assessing anatomical surface features are described herein. In some embodiments, a method of assessing a surface feature on a patient's skin surface includes (i) capturing one or more data sets from a patient's skin surface including the skin surface feature, and (ii) determining outline data of the skin surface feature based on at least one of the one or more data sets. The method can further include determining one or more confidence attributes associated with the determined outline data.

A method comprising using at least one hardware processor for computing a patch distinctiveness score for each of multiple patches of a medical image, computing a shape distinctiveness score for each of multiple regions of the medical image, and computing a saliency map of the medical image, by combining the patch distinctiveness score and the shape distinctiveness score.

Provided is an image analysis device for specifying an image region of an object being the basis for design similarity determination. An image analysis device 100 includes units 112, 113. The unit 112 moves original images Im1 and Im2 including a designated image region H with a rotationally symmetry on a first point P relative to a reference image Im2(j), calculates a correlation value cim(j) at each relative position, and detects the position of the point P in the images Im1, Im2, according to a geometric model, based on the amount of rotation (j) at which the cim(j) is the maximum and a vector v(j). The unit 113 specifies the range of the region H in the images Im1, Im2 based on the position of the point P and the distribution of the brightness values of pixels in the images Im1, Im2.

An electronic device and an image processing method in the electronic device are provided. The electronic device includes a display unit configured to display an image; and a controller configured to correct an outline according to a drawing input with a correction scheme on the image and to crop the image within the corrected outline according to the corrected outline.

An electronic device and a method of controlling an electronic device are provided. The method includes obtaining an image comprising a text through a camera, identifying an input text, among texts included in the image, to be translated, obtaining a first vector corresponding to the input text by inputting the identified input text to an encoder of a translation model, identifying whether additional information is necessary to translate the input text by inputting the first vector to a first artificial intelligence model trained to translate the input text, based on identification that the additional information is necessary, identifying additional information among the at least one context information by inputting the first vector and at least one context information obtained from the image, and obtaining an output text corresponding to the input text by inputting the first vector and the identified additional information to a decoder of the translation model.

A computational lithography process uses machine learning models. An aerial image produced by a lithographic mask is first calculated using a two-dimensional model of the lithographic mask. This first aerial image is applied to a first machine learning model, which infers a second aerial image. The first machine learning model was trained using a training set that includes aerial images calculated using a more accurate three-dimensional model of lithographic masks. The two-dimensional model is faster to compute than the three-dimensional model but it is less accurate. The first machine learning model mitigates this inaccuracy.

The present disclosure relates to systems, non-transitory computer-readable media, and methods for adapting generative neural networks to target domains utilizing an image translation neural network. In particular, in one or more embodiments, the disclosed systems utilize an image translation neural network to translate target results to a source domain for input in target neural network adaptation. For instance, in some embodiments, the disclosed systems compare a translated target result with a source result from a pretrained source generative neural network to adjust parameters of a target generative neural network to produce results corresponding in features to source results and corresponding in style to the target domain.

An example vector path trajectory imitation system is configured to create a new vector path or to extend an existing vector path based on a reference. In this manner, a user (e.g., artist, illustrator, or designer) does not need to tweak individual anchor points to align a trajectory of the new vector path with the trajectory of the reference. Instead, the user moves a position indicator (e.g., a mouse cursor) on a digital canvas in a freehand fashion while the vector path trajectory imitation system provides visual feedback to show the user how a resultant curve will look. When the user reaches a position on the digital canvas where a new vector path is to be drawn, the user can perform an action (e.g., releasing a mouse button) and the new vector path, which follows the trajectory of the reference, is created.

An object classification apparatus includes a LiDAR and a processor. The processor may identify that a point corresponding to a first previous object box and a point corresponding to a second previous object box are included in an integrated object box including contour points at the predetermined time and included in an object box representing an integrated object, separate and cluster the contour points at the predetermined time into contour points representing a first object and contour points representing a second object, store the separated contour points representing the first object in association with the first object, and store the separated contour points representing the second object in association with the second object based on a number of separated contour points representing the first object and a number of separated contour points representing the second object.