A stored volumetric scene model of a real scene is generated from data defining digital images of a light field in a real scene containing different types of media. The digital images have been formed by a camera from opposingly directed poses and each digital image contains image data elements defined by stored data representing light field flux received by light sensing detectors in the camera. The digital images are processed by a scene reconstruction engine to form a digital volumetric scene model representing the real scene. The volumetric scene model (i) contains volumetric data elements defined by stored data representing one or more media characteristics and (ii) contains solid angle data elements defined by stored data representing the flux of the light field. Adjacent volumetric data elements form corridors, at least one of the volumetric data elements in at least one corridor represents media that is partially light transmissive. The constructed digital volumetric scene model data is stored in a digital data memory for subsequent uses and applications.

An image processing method includes a first step of acquiring a first image having disparity information and refocus information, and a second step of inputting the first image or the disparity information and the refocus information into a machine learning model, and of generating a second image having an in-focus position different from an in-focus position of the first image based on the refocus information. The refocus information is information on a distance between the in-focus position of the first image and the in-focus position of the second image.

A metrology system is provided including a projected pattern for points-from-focus type processes. The metrology system includes an objective lens portion, a light source, a pattern projection portion and a camera. Different lenses (e.g., objective lenses) having different magnifications and cutoff frequencies may be utilized in the system. The pattern projection portion includes a pattern component with a pattern. At least a majority of the area of the pattern includes pattern portions that are not recurring at regular intervals across the pattern (e.g., as corresponding to a diverse spectrum of spatial frequencies that result in a relatively flat power spectrum over a desired range and with which different lenses with different cutoff frequencies may be utilized). The pattern is projected on a workpiece surface (e.g., for producing contrast) and an image stack is acquired, from which focus curve data is determined that indicates 3 dimensional positions of workpiece surface points.

A method includes capturing an image using a content capture device with an initial image setting. The image includes a plurality of pixel groups, and a pixel group can have one or more pixels. A plurality of edge pixel groups is identified and then classified into two subsets. A first subset of saturated edge pixel groups includes edge pixel groups that have at least one neighboring pixel group with an image intensity exceeding a saturated intensity value. A second subset of non-saturated edge pixel groups includes edge pixel groups that have no neighboring pixel groups with an image intensity exceeding a saturated intensity value. An adjustment value to the image setting is determined based on a total number of saturated edge pixel groups and a total number of non-saturated edge pixel groups. An updated image is captured with an updated image setting based on the adjustment value.

A coordinate measuring machine (CMM) system is provided including utilization of a vision probe (e.g., for performing operations for determining and/or measuring surface profiles of workpieces, etc.) The angular orientation of the vision probe may be adjusted using a rotation mechanism so that the optical axis of the vision probe is directed toward an angled surface of a workpiece (e.g., in some implementations the optical axis may be approximately perpendicular to the angled workpiece surface). X-axis, y-axis and z-axis slide mechanisms (e.g., moving in mutually orthogonal directions) may in conjunction move the vision probe to acquisition positions along an image stack acquisition axis (which may approximately coincide with the optical axis) for acquiring a stack of images of the angled workpiece surface. Focus curve data may be determined from analysis of the image stack, which indicates 3-dimensional positions of surface points on the angled surface of the workpiece.

An imaging system includes an imaging device having a holder configured to hold a cell culture plate with a plurality of wells. The imaging device also includes an imaging assembly having a plurality of imaging units, each of which is configured to image one well of the plurality of wells. The imaging system also includes a storage platform in communication with the imaging device configured to receive a plurality of images from the imaging device. The system further includes a computer in communication with the imaging device and the storage platform. The computer is configured to control the imaging device and to display at least one image of the plurality of images.

A 3D measuring instrument includes a registration camera and a surface measuring system having a projector and autofocus camera. In a first pose, the registration camera captures a first registration image of first registration points. The autofocus camera captures a first surface image of first light projected onto the object by the projector and determines first 3D coordinates of points on the object. In a second pose, the registration camera captures a second registration image of second registration points. The autofocus camera adjusts the autofocus mechanism based at least in part on adjusting a focal length to reduce a difference between positions of the first and second registration points. A second surface image of second light is captured. A compensation parameter is determined based at least in part on the first registration image, the second registration image, the first 3D coordinates, the second surface image, and the projected second light.

A method of analyzing a blood sample from a subject includes loading the blood sample into a single chamber; acquiring, via an imaging system, a stack of serial focal plane images of the blood sample from a plurality of fields of view of the chamber; creating a virtual three dimensional image of the blood sample from selected ones of the stacks of serial focal plane images; and analyzing the virtual three dimensional image to identify blood formed elements within the blood sample. Identifying blood formed elements within the blood sample may include identifying a type and amount of white blood cells, and/or identifying an amount of red blood cells and/or hematocrit, and/or identifying an amount of platelets. Identifying blood formed elements within the blood sample may include determining numbers and percentage by volume in the blood sample of one or more types of white blood cells.

An image processing system includes a plurality of imaging units configured to capture optical images including a low-distortion region and a high-distortion region; a first image recognition unit configured to perform image recognition on at least a partial region out of image data obtained from the imaging unit and output a first image recognition result; a second image recognition unit configured to perform image recognition on image data in a wider region than the partial region out of the image data obtained from at least one of the imaging units and output a second image recognition result; and an integration processing unit configured to output an image recognition result integrated on the basis of the first image recognition result and the second image recognition result.

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.