Disclosed are various embodiments for automated pill identification using lighting devices and machine learning routines. A computing device may selectively control illumination of a pill provided at an imaging position by a pill dispensing system. The computing device may direct an imaging device to capture image data of the pill during illumination of the pill. Also, the computing device may generate a digital fingerprint of the pill and determine an identity of the pill based at least in part on a comparison of the digital fingerprint to a digital fingerprint library. A machine learning routine may be applied to improve future detection of the identity of the pill.

A non-transitory computer-readable medium storing a computer program executed by a computer, a method, and an image processing device are disclosed that are capable of compensating a missing region in a tomographic image in a state in which a part of a lumen organ is missing. In accordance with the program, a computer acquires a plurality of tomographic images of a cross section of the lumen organ captured at a plurality of places using a catheter. In addition, the computer extracts, from the plurality of tomographic images, a tomographic image in which the part of the lumen organ is missing. Then, the computer compensates a missing region of the lumen organ for the extracted tomographic image.

Examples for determining a confidence level associated with image segmentation are disclosed. A confidence level associated with a collective image segmentation result can be determined by generating multiple individual segmentation results each from the same image data. These examples can then aggregate the individual segmentation results to form the collective image segmentation result and measure the spread of each individual segmentation result from the collective image segmentation result. The measured spread of each individual segmentation result can then be used to determine the confidence level associated with the collective image segmentation result. This can allow a confidence level associated with the collective image segmentation result to be determined. This confidence level may be determined without needing a ground truth to compare to the collective image segmentation result.

A method includes capturing a raw image from a semiconductor wafer, using graphic data system (GDS) information corresponding to the wafer to assign a measurement box in the raw image, performing a distance measurement on a feature of the raw image in the measurement box, and performing a manufacturing activity based on the distance measurement.

A system includes a computer programmed to detect a first and a second object in received image data, determine a mesh of cells on each of the first and second object surface, upon identifying a cell of the mesh on the first object mismatched to a corresponding cell on the second object to refine the mismatched cell to a plurality of cells, wherein identifying the mismatch is based on a at least one of a mismatch in a color, texture, shape, and dimensions, stop refining the cell upon determining that a refinement of the refined cell of the first object results in a refined cell that is matched to a corresponding refined cell of the second object, and output location data of mismatched cells of the first and second objects. A mismatched cell has at least one of a color mismatch, texture mismatch, and shape mismatch.

An object detection method includes: step (a1), a control device obtains an image and an outline of a specific object; step (a2), the control device obtains reference position information with respect to the center position of the specific object in the image; step (b1), the control device detects the outline of an unknown object from an image of the unknown object; step (b2), the control device obtains patch images including a region of the outline of the unknown object, by means of the reference position information; and step (b3), the control device determines whether the specific object exists in the image of the unknown object, by means of the similarity of the shape of the outline of the unknown object with respect to the shape of the outline of the specific object and the similarity of each of the patch images with respect to the image of the specific object.

Exemplary embodiments relate to a method for unlocking a mobile device using authentication based on ear recognition including obtaining an image of a target showing at least part of the target's body in a lock state, extracting a set of ear features of the target from the image of the target, when the image of the target includes at least part of the target's ear, and determining if the extracted set of ear features of the target satisfies a preset condition, and a mobile device performing the same.

A system and method for scoring trained probes for use in analyzing one or more candidate poses of a runtime image is provided. A set of probes with location and gradient direction based on a trained model are applied to one or more candidate poses based upon a runtime image. The applied probes each respectively include a discrete set of position offsets with respect to the gradient direction thereof. A match score is computed for each of the probes, which includes estimating a best match position for each of the probes respectively relative to one of the offsets thereof, and generating a set of individual probe scores for each of the probes, respectively at the estimated best match position.

A method includes capturing a raw image from a semiconductor wafer, assigning a measurement box in the raw image, arranging a pair of indicators in the measurement box according to graphic data system (GDS) information of the semiconductor wafer, measuring a distance between the indicators, and performing a manufacturing activity based on the measured distance.

A CPU of a health management apparatus functions as a first acquisition unit, a second acquisition unit, and a screen output control unit. The first acquisition unit acquires medical examination results which are results of a medical examination of a pet. The second acquisition unit acquires an optical image of the pet. The screen output control unit controls an output of a medical examination result display screen including the medical examination results and at least one of the optical image or a schema diagram created on the basis of the optical image.