An image processing device includes an image processing unit that performs image processing on an observed image in which a cell is imaged and an image processing method selector that is configured to determine an observed image processing method for analyzing the imaged cell on the basis of information of a processed image obtained through image processing of the image processing unit.

An example system includes a stand configured to position a computing device proximate to a physical activity surface. The system further includes a video capture device, a detector, and an activity application. The video capture device is coupled for communication with the computing device and is adapted to capture a video stream that includes an activity scene of the physical activity surface and one or more interface objects physically interactable with by a user. The detector is executable to detect motion in the activity scene based on the processing and, responsive to detecting the motion, process the video stream to detect one or more interface objects included in the activity scene of the physical activity surface. The activity application is executable to present virtual information on a display of the computing device based on the one or more detected interface objects.

A method includes receiving an image from a mammogram, removing noise from the image, computing a point of interest on the de-noised image, creating a mesoscale region of interest on the de-noised image, computing a connectivity for the mesoscale region of interest, identifying a connected component using the computed connectivity, where the connected component represents a branch of a global curvilinear structure, selecting a set of branches based on a physical property for each branch of the global curvilinear structure, pruning each branch based on an error-tolerant, adaptive polynomial fit, identifying remaining regions of interest in each pruned branch, and growing a chain formed by remaining points of interest included in the remaining regions of interest, where the chain represents a macroscopic, global curvilinear calcified arterial structure. The quantitation of the calcified arterial structures may be used as a biomarker for risk stratification of heart disease.

A system identifies strong features in conjugate digital images and correlates the conjugate digital images for an identification of candidate tie points using only portions of the conjugate digital images that include the strong features.

An example system includes a stand configured to position a computing device proximate to a physical activity surface. The system further includes a video capture device, a detector, and an activity application. The video capture device is coupled for communication with the computing device and is adapted to capture a video stream that includes an activity scene of the physical activity surface and one or more interface objects physically interactable with by a user. The detector is executable to detect motion in the activity scene based on the processing and, responsive to detecting the motion, process the video stream to detect one or more interface objects included in the activity scene of the physical activity surface. The activity application is executable to present virtual information on a display of the computing device based on the one or more detected interface objects.

A crosswalk detection device according to an embodiment includes a processor, and the processor configured to perform image scanning processing for scanning aerial image data in unit of window, multiple line segment creation processing for creating a plurality of line segments corresponding to a distance based on a dimension of a crosswalk in any direction within a region scanned in the aerial image data, first calculation processing for calculating an absolute value of a first derivative value indicating a difference in brightness between both ends of the line segment and calculating an average value of the absolute value of the first derivative value for each angle, second calculation processing for calculating an absolute value of a second derivative value indicating a difference between a difference in brightness between a first end and an intermediate point of the line segment and a difference in brightness between the intermediate point and a second end of the line segment and calculating an average value of the absolute value of the second derivative value for each angle, and crosswalk determination processing for determining that coordinates of each of the line segments are coordinates of the crosswalk based on a calculation result.

Method of tracking a plurality of objects comprising: focusing an image of the objects on an imaging element using an optical system; capturing the image of the objects using an imaging element comprising a plurality of pixels; measuring at least one characteristic of the objects from the captured image using an image processor; wherein the field of view is set be the widest field of view for which the image processor is able to measure the at least one characteristic.

Smoke detection based on video images includes performing background image modeling on a current image, to acquire a foreground image and a background image of the current image; acquiring one or more candidate areas in the current image used for detecting a moving object, based on the foreground image; calculating attribute information of a candidate area corresponding to the current image and/or the background image; and determining whether there exists smoke in the candidate area according to the attribute information. The smoke can be detected quickly and accurately through video images, but also the detection accuracy of video-based smoke detection when light changes and at complex environments can be provided.

A method and apparatus for the formation of a simplified model. A computer system comprises a simplification system. The simplification system receives a plurality of components from a complex model, determines a number of moments of inertia for each component of the plurality of components, and creates a simplified model using the number of moments of inertia for the each component of the plurality of components, wherein the complex model is a three-dimensional computer model.

A computing system can receive an image including foreground pixels. The foreground pixels can be determined based on determining eccentricity .sub.k based on a sequence of images acquired by a stationary sensor. The vehicle can determine moving objects in the image based on the foreground pixels. The vehicle can be operated based on the moving objects in the image.