An image processing device includes: an obtaining unit configured to obtain an image including at least one body hair; and an analyzing unit configured to analyze the image to output information indicating an orientation of the at least one body hair included in the image.

Customized nipples are provided herein that are designed to mimic a woman’s anatomic nipple to improve experiences such as breast feeding, soothing a baby, and/or superior aesthetics. The customized nipples are created in a noninvasive manner, for example, via a scan of the woman’s breast that is received by a computer. The scan may be processed with enhanced computing power to provide the optimal customized nipple for the woman. The customized nipples may be formed for baby/nipple/nursing products such as bottles, pacifiers, nipple shields, breast pump flanges, attachments for supplemental nursing systems, and prosthetic nipples.

A method for operating an accessory device to capture calibration data. The method can include capturing an image of a portion of a user's body that includes the user's stoma and at least two reference locations, and storing distance scale information representative of a distance between the two reference locations. The method further includes processing the captured image, including: identifying the reference locations, identifying the stoma, and generating calibration data representative of one or more stoma parameters as a function of the identified reference locations, identified stoma and the distance scale information. The calibration data can be stored.

Methods and apparatuses for generating and displaying a model of a subject's teeth. Described herein are intraoral scanning methods and apparatuses for generating a three-dimensional model of a subject's intraoral region (e.g., teeth). These methods and apparatuses may be used for identifying and evaluating lesions, caries and cracks in the teeth.

The present disclosure provides systems and methods for performing an automated scan preparation for a scan of a target subject. The automated scan preparation may include, for example, identifying a target subject to be scanned, generating a target posture model of the target subject, causing a movable component of a medical imaging device to move to its target position, controlling a light field of the medical imaging device, determining a target subject orientation, determining a dose estimation, selecting at least one target ionization chamber, determining whether the posture of the target subject needs to be adjusted, determining one or more scanning parameters (e.g., a size of a light field), performing a preparation check, or the like, or any combination thereof.

The present disclosure relates to an information processing apparatus, an information processing method, and a program capable of providing more effective learning content in learning motion of a body.

An adjustment unit generates an adjusted second virtual object by adjusting, on the basis of feature point information of a first person included in a first virtual object reflecting a body motion of the first person, a second virtual object reflecting a body motion of a second person to be superimposed on the first virtual object. The technology according to the present disclosure can be applied to, for example, an information processing apparatus such as a smartphone.

A femoral neck measuring method includes: measuring an affected limb after a femoral stem prosthesis is installed through an optical localization probe, and calculating an actual length of the femoral neck needed by the affected limb according to an osteotomy surface of the affected limb determined after the femoral stem prosthesis is installed; determining a femoral ball prosthesis and a femoral neck prosthesis to be selected according to the calculated actual length of the femoral neck and a length of a femoral neck of a healthy limb measured before surgery; and measuring a length of the affected limb after the femoral ball prosthesis and the femoral neck prosthesis are implanted, and restraining a length difference between the healthy limb and the affected limb by adjusting a length of the femoral neck of the affected limb.

A method can include capturing an image, images, or a video of a user with the image, images or video capturing system of an exercise device and detecting a health or disease indicator from the images or video. An apparatus includes a camera, a processor, and computer readable medium containing programming instructions that, when executed, will cause the processor to use a machine learning model and one or more images or videos of a subject captured by the camera to detect a health or disease indicator.

Computer implemented methods and devices for determining dimensions or distances of head features are provided. The method includes identifying a plurality of features in an image of a head of a person. A real dimension of at least one target feature of the plurality of features or a real distance between at least one target feature of the plurality features and a camera device used for capturing the image is estimated based on probability distributions for real dimensions of at least one feature of the plurality of features and a pixel dimension of the at least one feature of the plurality of features.

Various examples are provided related to identification of anthropometric information for fitting of compression garments. In one example, a method of generating compression garment fit information includes acquiring images including a selected body part or body area in need of compression therapy; processing the acquired images along with a library of compression garment fit information; generating a 3D reconstruction of the selected body part/area; deriving anthropometric information for the selected body part/area from the 3D reconstruction; providing a compression value corresponding to a prescribed or intended amount of compression therapy applied to the selected body part/area; and generating compression garment fit information for the selected body part/area. In another example, a library of compression garment fit information includes anthropometric information generated for individuals in need of compression therapy; information associated with a health condition for the individuals; and compression garment fit instructions provided by a compression garment manufacturer.