A prism constant can be obtained more efficiently, using a laser positioning device including an image data obtaining portion obtaining an image data which is obtained by photographing a reflection prism device, a data storing portion storing a relationship of a prism constant of the reflection prism device and the image data, and a prism constant obtaining portion obtaining the prism constant of the reflection prism device based on the relationship.

A prism constant can be obtained more efficiently, using a laser positioning device including an image data obtaining portion obtaining an image data which is obtained by photographing a reflection prism device, a data storing portion storing a relationship of a prism constant of the reflection prism device and the image data, and a prism constant obtaining portion obtaining the prism constant of the reflection prism device based on the relationship.

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.

A determination method according to an example aspect of the present disclosure includes: acquiring a plurality of captured images of an object successively captured by an image capturing apparatus in a state in which a positional relation between the object and the image capturing apparatus is changing; dividing each of the plurality of captured images that are continuous in time into a plurality of image areas; associating the plurality of divided image areas among the plurality of captured images that are continuous in time; calculating a luminance difference between a maximum luminance value and a minimum luminance value in each of the image areas that have been associated; and determining the type of the paint applied to the object based on changes in the luminance differences in the plurality of image areas associated among the plurality of captured images that are continuous in time.

A 3D printer includes a nozzle configured to jet a drop of liquid metal therethrough. The 3D printer also includes a light source configured to illuminate the drop with a pulse of light. A duration of the pulse of light is from about 0.0001 seconds to about 0.1 seconds. The 3D printer also includes a camera configured to capture an image, video, or both of the drop. The 3D printer also includes a computing system configured to detect the drop in the image, the video, or both. The computing system is also configured to characterize the drop after the drop is detected. Characterizing the drop includes determining a size of the drop, a location of the drop, or both in the image, the video, or both.

A system and corresponding method are disclosed for biometric operations. The system may comprise an illumination source, a polarization selector, an imager, and a controller. The illumination source may illuminate a biometric feature. The polarization selector may receive light reflected from the biometric feature and be operable between two states selectively transmitting different polarizations. The imager may receive the light selectively transmitted by the polarization selector and generate one or more images. The controller may perform a biometric authentication based, at least in part, on the one or more images. In operation, the polarization selector may switch between states so that the system may capture two separate image sets, each based on light of a different polarization. Accordingly, adverse reflections, such as those off an eyeball or glasses, may be substantially eliminated or reduced in at least one of the image sets.

An onboard aircraft landing system includes one or more event-based cameras disposed at known locations to capture the runway and visible surrounding features such as lights and runway markings. The event-based cameras produce a continuous stream of event data that may be quickly processed to identify both light and dark features contemporaneously, and calculate an aircraft pose relative to the runway based on the identified features and the known locations of the event-based cameras. Composite features are identified via the relative location of individual features corresponding to pixel events.

An intelligent insect trap and identification system is disclosed. The intelligent insect trap and identification system can include an insect imaging chamber and identification system. The chamber can include a first cell for accepting insects, a second cell, a first reflector in the second cell, and a first imaging device in the second cell for recording one or more first visual images of the one or more insects in the first cell. Based on the image, the insect imaging chamber can detect and identify the insects. Other aspects, embodiments, and features are also claimed and described.

An image processing device (1) includes an exposure control unit (102), a Δ pose estimating unit (109), and an integration unit (111). The exposure control unit (102) controls exposure by sequentially performing positive correction of increasing exposure from proper exposure or negative correction of decreasing exposure from the proper exposure as the exposure at a time of acquiring each of a plurality of image frames in time series in a predetermined execution order. The Δ pose estimating unit (109) estimates first position pose of the image processing device based on matching between image frames subjected to the positive correction and second position pose of the image processing device based on matching between image frames subjected to the negative correction. The integration unit (111) integrates the first position pose and the second position pose.

Apparatus and methods for detecting an object in an image. One aspect of the method includes receiving a first image from an image sensor. The first image is obtained by the image sensor using a first focal length. A second image is received from the image sensor. The second image is obtained by the image sensor using a second focal length. One or more objects are detected in the first image and the second image. The one or more objects detected in the first image are combined with the one or more objects detected in the second image.