A skin condition measuring module according to the present invention can be designed to effectively diagnose a user's skin condition while being installed within or connected with: a kiosk; a skin condition measuring mirror; a vending machine for performing skin condition measurement and providing cosmetic products; or a module-embedded skin condition measuring device.

Disclosed are image based counting systems and methods. Image based counting systems and methods employ one or more imaging devices (e.g., one or more color cameras) and one or more illumination devices (e.g., one or more illumination sources to provide variable and/or dynamic lighting). In some examples, the image based counting system provides illumination to a tray upon which one or more items (e.g., pills) are placed. The illuminated items are then imaged by the one or more imaging devices based on one or more imaging techniques or processes to determine and/or present information associated with one or more properties of the item.

The technology disclosed relates to identifying an object in a field of view of a camera. In particular, it relates to identifying a display in the field of view of the camera. This is achieved by monitoring a space including acquiring a series of image frames of the space using the camera and detecting one or more light sources in the series of image frames. Further, one or more frequencies of periodic intensity or brightness variations, also referred to as refresh rate, of light emitted from the light sources is measured. Based on the one or more frequencies of periodic intensity variations of light emitted from the light sources, at least one display that includes the light sources is identified.

A system for real-time depth sensing includes a structured-light stereo device, a time of flight device, and a computing device. The computing device executes instructions that cause the computing device to determine depth measurements of a scene using information received by the structured-light stereo device and to determine time of flight measurements of the scene using information received by the time of flight device. The computing device executes further instructions that cause the computing device to generate a depth map using the depth measurements, to generate calibration points using the time of flight measurements, and to update the depth map using the calibration points.

The technology disclosed relates to identifying an object in a field of view of a camera. In particular, it relates to identifying a display in the field of view of the camera. This is achieved by monitoring a space including acquiring a series of image frames of the space using the camera and detecting one or more light sources in the series of image frames. Further, one or more frequencies of periodic intensity or brightness variations, also referred to as refresh rate, of light emitted from the light sources is measured. Based on the one or more frequencies of periodic intensity variations of light emitted from the light sources, at least one display that includes the light sources is identified.

Systems and methods for monitoring of icon in an external display device are disclosed. Images of an icon displayed in a display device may be continually captured as video frames by a video camera of an icon monitoring system. While operating in a first mode, video frames may be continually analyzed to determine if the captured image matches an active template icon known to match the captured image of the icon. While the captured image matches the active template icon, operating in the first mode continues. Upon detecting a failed match to the active template icon, the system starts operating in a second to search among known template icons for a new match. Upon finding a new match, the active template icon may be updated to the new match, and operation switches back to the first mode. Times of transitions between the first and second modes may be recorded.

Systems and methods are directed to object detection at various ranges for autonomous vehicles. In one example, a system includes a camera providing a first field of view; a machine-learned model that has been trained to generate object detection range estimates based at least in part on labeled training data representing image data having a second field of view different from the first field of view; and a computing system including one or more processors; and memory including instructions that, when executed by the one or more processors, cause the one or more processors to perform operations. The operations include obtaining image data from the camera. inputting the image data from the camera to the machine-learned model; obtaining a first range estimate as an output of the machine-learned model, wherein the first range estimate represents estimates for the second field of view; generating transformed range estimate by applying a range estimate transform to the first range estimate output from the machine-learned model; and providing the transformed range estimate for use in controlling operation of an autonomous vehicle.

Apparatus, system and method for detecting defects in an adhesion area that includes an adhesive mixed with a fluorescent material. One or more illumination devices may illuminate the fluorescent material in the adhesion area with a light of a predetermined wavelength. A camera may be configured to capture an image of the illuminated adhesion area. A processing device, communicatively coupled to the camera, may be configured to process the captured image by applying one or more boundary areas to the captured image and determining an image characteristic within each of the boundary areas, wherein the image characteristic is used by the processing device to determine the presence of a defect in the adhesive, such as an excess of adhesive or an insufficient application of adhesive.

An image processing apparatus includes an information obtaining unit configured to obtain three-dimensional polarization sensitive tomographic information and three-dimensional motion contrast information about a subject based on tomographic signals of lights having different polarizations, the lights being obtained by splitting a combined light obtained by combining a returned light from the subject illuminated with a measurement light with a reference light corresponding to the measurement light, an obtaining unit configured to obtain a lesion region of the subject using the three-dimensional polarization sensitive tomographic information, and an image generation unit configured to generate an image in which the lesion region is superimposed on a motion contrast image generated using the three-dimensional motion contrast information.

A bottle storage includes a housing portion that houses a bottle therein, a cooling-warming portion that is provided around the housing portion, a sensor which detects insertion of the bottle into the housing portion, an image capture portion that captures an image of the bottle housed in the housing portion, a controller that causes the image capture portion to perform image capture for capturing the image of the bottle when the bottle is sensed to be inserted into the housing portion by the sensor, an information manager that identifies at least a brand of the bottle, based on recognition result of the bottle image captured by the control of the controller, and an output portion that outputs characteristics of a beverage of the brand identified by the information manager.