Apparatus and associated methods relate to a method of non-contact motion detection. A one-dimensional optical sensor detects motion of a target or objects on a conveyor belt through a continuous measurement of targets or objects and a real-time comparison of the pixel images captured by the one-dimensional optical sensor. In an illustrative embodiment, a one-dimensional sensor may be configured to determine motion of objects based on changes to the captured intensities of pixel images over time. The sensor may continually capture photoelectric pixel images and compare a current pixel image with a previous pixel image to determine a frame differential image value. The frame differential image value is evaluated against a predetermined threshold over a predetermined time period. Based on the evaluation, a signal is output indicating whether the objects on the conveyor belt are moving or jammed.

An autonomous vehicle control system is provided. The control system may include a plurality of cameras to acquire a plurality of images of an area in a vicinity of a vehicle; and at least one processing device configured to: recognize a curve to be navigated based on map data and vehicle position information; determine an initial target velocity for the vehicle based on at least one characteristic of the curve as reflected in the map data; adjust a velocity of the vehicle to the initial target velocity; determine, based on the plurality of images, observed characteristics of the curve; determine an updated target velocity based on the observed characteristics of the curve; and adjust the velocity of the vehicle to the updated target velocity.

An autonomous vehicle control system is provided. The control system may include a plurality of cameras to acquire a plurality of images of an area in a vicinity of a vehicle; and at least one processing device configured to: recognize a curve to be navigated based on map data and vehicle position information; determine an initial target velocity for the vehicle based on at least one characteristic of the curve as reflected in the map data; adjust a velocity of the vehicle to the initial target velocity; determine, based on the plurality of images, observed characteristics of the curve; determine an updated target velocity based on the observed characteristics of the curve; and adjust the velocity of the vehicle to the updated target velocity.

A neural network model receives color data for a sequence of images corresponding to a dynamic scene in three-dimensional (3D) space. Motion of objects in the image sequence results from a combination of a dynamic camera orientation and motion or a change in the shape of an object in the 3D space. The neural network model generates two components that are used to produce a 3D motion field representing the dynamic (non-rigid) part of the scene. The two components are information identifying dynamic and static portions of each image and the camera orientation. The dynamic portions of each image contain motion in the 3D space that is independent of the camera orientation. In other words, the motion in the 3D space (estimated 3D scene flow data) is separated from the motion of the camera.

A neural network model receives color data for a sequence of images corresponding to a dynamic scene in three-dimensional (3D) space. Motion of objects in the image sequence results from a combination of a dynamic camera orientation and motion or a change in the shape of an object in the 3D space. The neural network model generates two components that are used to produce a 3D motion field representing the dynamic (non-rigid) part of the scene. The two components are information identifying dynamic and static portions of each image and the camera orientation. The dynamic portions of each image contain motion in the 3D space that is independent of the camera orientation. In other words, the motion in the 3D space (estimated 3D scene flow data) is separated from the motion of the camera.

A system is disclosed that includes an optical tracking device and a surgical computing device. The optical tracking device includes a structured light module and an optical module that includes an image sensor and is spaced from the structured light module at a known distance. The surgical computing device includes a display device, a non-transitory computer readable medium including instructions, and processor(s) configured to execute the instructions to generate a depth map from a first image captured by the image sensor during projection of a pattern into a surgical environment by the structured light module. The pattern is projected in a near-infrared (NIR) spectrum. The processor(s) are further configured to execute the stored instructions to reconstruct a 3D surface of anatomical structure(s) based on the generated depth map. Additionally, the processor(s) are configured to execute the stored instructions to output the reconstructed 3D surface to the display device.

Systems and methods for macro stereo time-lapse photography, producing a stereographic time-lapse digital video, and macro stereographic time-lapse digital videos. A method of producing a sequence of time-lapse stereographic images of a subject, by positioning a camera with a macro lens at a first position relative to the subject; using the camera to obtain a first stack of images of the subject from the first position; positioning the camera at a second position relative to the subject; using the camera to obtain a second stack of images of the subject from the second position; and storing the first stack of images and the second stack of images as a stack pair; and then selectively repeating.

Systems and methods for macro stereo time-lapse photography, producing a stereographic time-lapse digital video, and macro stereographic time-lapse digital videos. A method of producing a sequence of time-lapse stereographic images of a subject, by positioning a camera with a macro lens at a first position relative to the subject; using the camera to obtain a first stack of images of the subject from the first position; positioning the camera at a second position relative to the subject; using the camera to obtain a second stack of images of the subject from the second position; and storing the first stack of images and the second stack of images as a stack pair; and then selectively repeating.

A method and system for determining if an individual is impaired. In one embodiment, physical and cognitive testing of the individual are conducted in the field or at the scene of an event. The test results are compared to previously stored baseline test results taken for the specific individual while the individual is known to be in an unimpaired state or condition. The current test results are electronically compared to the baseline test results and if the results differ or deviate beyond a predetermined level or amount the individual is considered to be impaired. If no baseline test results exist for the specific individual, the current test results can alternatively be compared to previously determined or known scientifically accepted or minimums for the specific tests given to the individual.

A method and system for determining if an individual is impaired. In one embodiment, physical and cognitive testing of the individual are conducted in the field or at the scene of an event. The test results are compared to previously stored baseline test results taken for the specific individual while the individual is known to be in an unimpaired state or condition. The current test results are electronically compared to the baseline test results and if the results differ or deviate beyond a predetermined level or amount the individual is considered to be impaired. If no baseline test results exist for the specific individual, the current test results can alternatively be compared to previously determined or known scientifically accepted or minimums for the specific tests given to the individual.