Embodiments of work stations for use in a medical dose preparation management system are disclosed. A work station may include a camera stand. The camera stand may include a housing enclosing a camera and one or more light sources therein. As such, the camera and light sources may be directed at a medical dose preparation staging region to capture medical dose preparation images of the medical dose preparation staging region. The camera stand may include an adjustable support positionable in a plurality of positions to dispose the camera and light source relative to the medical dose preparation staging region. The work stations may facilitate improved image quality and record work flows carried out at the work station for preparation verification.

Embodiments of work stations for use in a medical dose preparation management system are disclosed. A work station may include a camera stand. The camera stand may include a housing enclosing a camera and one or more light sources therein. As such, the camera and light sources may be directed at a medical dose preparation staging region to capture medical dose preparation images of the medical dose preparation staging region. The camera stand may include an adjustable support positionable in a plurality of positions to dispose the camera and light source relative to the medical dose preparation staging region. The work stations may facilitate improved image quality and record work flows carried out at the work station for preparation verification.

Disclosed are a face image and iris image acquisition method and device, a computer-readable readable storage medium and an apparatus. The method includes rotating the first tripod head to force the face lens and the iris lens to be in acquisition positions; capturing a first face image and a first iris image simultaneously by the face lens and the iris lens; and locating the iris in the first iris image, and if no iris is located, determining whether a condition of light-avoiding rotation is satisfied, and if the condition is satisfied, rotating the second tripod head to adjust an angle or a position of the supplementary light source to enable a light spot region to avoid an iris region.

Disclosed are a face image and iris image acquisition method and device, a computer-readable readable storage medium and an apparatus. The method includes rotating the first tripod head to force the face lens and the iris lens to be in acquisition positions; capturing a first face image and a first iris image simultaneously by the face lens and the iris lens; and locating the iris in the first iris image, and if no iris is located, determining whether a condition of light-avoiding rotation is satisfied, and if the condition is satisfied, rotating the second tripod head to adjust an angle or a position of the supplementary light source to enable a light spot region to avoid an iris region.

Image data is obtained that indicates an extent to which one or more objects reflect, scatter, or absorb light at each of multiple wavelength bands, where the image data was collected while a conveyor belt was moving the object(s). The image data is preprocessed by performing an analysis across frequencies and/or performing an analysis across a representation of a spatial dimension. A set of feature values is generated using the image preprocessed image data. A machine-learning model generates an output using to the feature values. A prediction of an identity of a chemical in the one or more objects or a level of one or more chemicals in the object(s) is generated using the output. Data is output indicating the prediction of the identity of the chemical in the object(s) or the level of the one or more chemicals in at least one of the one or more objects.

Image data is obtained that indicates an extent to which one or more objects reflect, scatter, or absorb light at each of multiple wavelength bands, where the image data was collected while a conveyor belt was moving the object(s). The image data is preprocessed by performing an analysis across frequencies and/or performing an analysis across a representation of a spatial dimension. A set of feature values is generated using the image preprocessed image data. A machine-learning model generates an output using to the feature values. A prediction of an identity of a chemical in the one or more objects or a level of one or more chemicals in the object(s) is generated using the output. Data is output indicating the prediction of the identity of the chemical in the object(s) or the level of the one or more chemicals in at least one of the one or more objects.

The present disclosure relates to a system and method for recognizing objects used in a medical procedure using a convolutional neural network. No database of image information for such objects is used or required. Rather, the neural network is trained to recognize the objects, and does not require any such image database. The system is able to reconcile the recognized objects against a ‘counted-in’ list of objects for the procedure, to ensure that all such objects are accounted for prior to closing the procedure.

Computer-implemented method and system (100) for estimating vegetation coverage in a real-world environment. The system receives an RGB image (91) of a real-world scenery (1) with one or more plant elements (10) of one or more plant species. At least one channel of the RGB image (91) is provided to a semantic regression neural network (120) which is trained to estimate at least a near-infrared channel (NIR) from the RGB image. The system obtains an estimate of the near-infrared channel (NIR) by applying the semantic regression neural network (120) to the at least one RGB channel (91). A multi-channel image (92) comprising at least one of the R-, G-, B-channels (R, G, B) of the RGB image and the estimated near-infrared channel (NIR), is provided as test input (TI1) to a semantic segmentation neural network (130) trained with multi-channel images to segment the test input (TI1) into pixels associated with plant elements and pixels not associated with plant elements. The system segments the test input (TI1) using the semantic segmentation neural network (130) resulting in a vegetation coverage map (93) indicating pixels of the test input associated with plant elements (10) and indicating pixels of the test input not associated with plant elements.

Methods, systems, and apparatus for an image recognition system. The image recognition system includes a memory. The memory is configured to store multiple sequences of movements of multiple standard objects. The image recognition system includes a sensor. The sensor is configured to capture image data of a surrounding environment. The image recognition system includes a processor. The processor is coupled to the memory and the sensor. The processor is configured to recognize an object in the image data. The processor is configured to determine a movement of the object based on the image data. The processor is configured to compare the movement of the object in the image data to a sequence of movements of a standard object of the plurality of standard objects, and determine that the object is a living being based on the comparison.

A novel display apparatus is provided. The display apparatus includes a first layer including a plurality of pixel circuits, a second layer provided over the first layer, a plurality of optical lenses provided over the second layer, a display region, and a plurality of light-receiving regions. The display region includes a first pixel circuit provided in the first layer and a light-emitting device provided in the second layer. The light-receiving region includes a second pixel circuit provided in the first layer and a light-receiving device provided in the second layer. The plurality of light-receiving regions are provided around the display region. The optical lens is provided at a position overlapping with the light-receiving region.