The present disclosure relates to a method for expanding an image database for evaluation of eyewear compatibility. In particular, the present disclosure relates to a method, comprising receiving a user image, receiving a frame image, processing the received frame image by setting, as transparent, pixels of the received frame image except for an anterior face of the frame, defining, within the processed frame image, a left boundary and a right boundary of the anterior face of the frame, the left boundary and the right boundary corresponding to the left eye and the right eye, respectively, receiving a filter image, processing the received filter image by setting, as transparent, pixels in the received filter image outside the frame based on the left boundary and the right boundary, merging the processed frame image and the processed filter image, and overlaying the merged image onto the received user image.

Methods and systems for imaging and analysis are described. Accurate pressure ulcer measurement is critical in assessing the effectiveness of treatment. However, the traditional measuring process is subjective. Each health care provider may measure the same wound differently, especially related to the depth of the wound. Even the same health care provider may obtain inconsistent measurements when measuring the same wound at different times. Also, the measuring process requires frequent contact with the wound, which increases risk of contamination or infection and can be uncomfortable for the patient. The present application describes a new automatic pressure ulcer monitoring system (PrUMS), which uses a tablet connected to a 3D scanner, to provide an objective, consistent, non-contact measurement method. The present disclosure combines color segmentation on 2D images and 3D surface gradients to automatically segment the wound region for advanced wound measurements.

A method for creating artificial vision with an implantable visual stimulation device. The method comprises receiving image data comprising, for each of multiple points of an image, a depth value, performing a local background enclosure calculation on the image data to determine salient object information, and generating a visual stimulus to visualise the salient object information using the implantable visual stimulation device. Performing the local background enclosure calculation is based on a subset of the multiple points of the input image, and the subset of the multiple points is defined based on the depth value of the multiple points.

A method in an illustrative embodiment includes dividing a first image having a first resolution into a plurality of image patches, and converting each of the plurality of image patches into an image patch having a second resolution by using a plurality of candidate conversion models, the second resolution being higher than the first resolution. The method may include determining a plurality of quality factors corresponding to the plurality of candidate conversion models for each of the converted image patches. The method may include selecting a conversion model for each image patch from the plurality of candidate conversion models based on the plurality of quality factors and computation load factors of the plurality of candidate conversion models, and generating metadata for converting the first image into a second image having the second resolution based on position information of each image patch and the conversion model selected for each image patch.

A method for identifying a base in nucleic acid, a computer-readable storage medium, a computer program product, and a system. The method for identifying a base in nucleic acid comprises: mapping a coordinate of each bright spot in a bright spot set corresponding to a template onto an image to be inspected, and determining the position of a corresponding coordinate on said image (S11); determining the intensity of a signal at the position of the corresponding coordinate on said image, the intensity being a corrected intensity (S21); and comparing the intensity of the signal at the position of the corresponding coordinate on said image with the size of a first preset value, and determining a base type corresponding to the position on the basis of the comparison result, so as to achieve base calling (S31). The method may quickly and accurately identify a base, and achieve the determination of an order of nucleotides/bases of at least part of a sequence of a template.

A dynamic image virtualization system and method configured to utilize an AI model in order to conduct a reduced latency real-time prediction process upon at least one input image, wherein said prediction process is designated to create free-viewpoint 3D extrapolated output dynamic images tailored in advance to the preferences or needs of a user and comprising more visual data than the at least one input image.

The present technique relates to an information processing device, an information processing method, and a program that enable a recognition result of an object recognition model to be appropriately interpreted. An information processing device includes an interpreting unit configured to perform interpretation of a recognition result of an object recognition model in units of segments which geometrically divide an image. For example, the present technique can be applied to a device which interprets and explains an object recognition model that performs object recognition in front of a vehicle.

An image processing device is an image processing device configured to cause a display to display, as a three-dimensional image, three-dimensional data representing a biological tissue having a longitudinal lumen. The image processing device includes: a control unit configured to calculate centroid positions of a plurality of cross sections in a lateral direction of the lumen of the biological tissue by using the three-dimensional data, set a pair of planes intersecting at a single line passing through the calculated centroid positions as cutting planes, and form, in the three-dimensional data, an opening exposing the lumen of the biological tissue from a region interposed between the cutting planes in the three-dimensional image.

A computer-implemented method includes receiving a two-dimensional image of a scene captured by a camera, recognizing one or more objects in the scene depicted in the two-dimensional image, and determining whether the one or more recognized objects have known real-world dimensions. The computer-implemented method further includes determining a depth of at least one recognized object having known real-world dimensions from the camera, and overlaying three-dimensional (3-D) augmented reality content over a display the 2-D image of the scene considering the depth of the at least one recognized object from the camera.

A computer-implemented method for processing an electronic image may include receiving, by an artificial intelligence (AI) system at an electronic storage of the AI system, one or more digital whole slide images (WSIs) and extracting one or more vectors of features from one or more foreground tiles of tile images of the one or more digital WSIs. The method may include running a trained machine learning model on the one or more vectors of features and determining, based on an output of the trained machine learning model, whether one or more quality issues are present in the one or more digital WSIs.