A method according to embodiments of the invention includes creating a three-dimensional profile of a scene, calculating a relative amount of light for each portion of the scene based on the three-dimensional profile, and activating a light source to provide a first amount of light to a first portion of the scene, and a second amount of light to a second portion of the scene. The first amount and the second amount are different. The first amount and the second amount are determined by calculating a relative amount of light for each portion of the scene.

An image capture device is mounted in a vehicle. The image capture device includes: an image capture unit; and a setting unit that sets an image capture condition for each region of the image capture unit each having a plurality of pixels, or for each pixel, based upon at least one of a state exterior to the vehicle and a state of the vehicle.

The iris recognition device includes an iris camera module used for collecting iris characteristics of a user, and at least one fill light component used for providing a supplementary light source for the iris camera module. When the iris recognition device is used for collecting the iris characteristics of the user, the supplementary light source provided by the fill light component reduces reflective spots on the iris or make reflective spots in areas other than iris such as sclera and pupil, thereby improving precision of the collected iris characteristics of the user.

A real time assay monitoring system and method can be used to monitor reagent volume in assays for fluid replenishment control, monitor assays in real-time to obtain quality control information, monitor assays in real-time during development to detect saturation levels that can be used to shorten assay time, and provide assay results before the assay is complete, enabling reflex testing to begin automatically. The monitoring system can include a real time imaging system with a camera and lights to capture images of the assay. The captured images can then be used to monitor and control the quality of the staining process in an assay, provide early assay results, and/or to measure the on-site reagent volume within the assay.

An advanced driving assistance system (ADAS) provides collision avoidance control for a host vehicle. The system can include one or more sensors mounted to the host vehicle and configured to sense a driving lane in which the host vehicle is traveling and to sense an external vehicle partially engaged in the driving lane. A controller controls steering, braking, or acceleration of the host vehicle on the basis of sensing information received from the sensor. The controller determines the external vehicle partially engaged in the driving lane as a target vehicle having at least a part thereof overlapping with a lane mark of the driving lane, and performs longitudinal braking or acceleration control or lateral steering control on the host vehicle based on lateral and longitudinal positional relationships between the host vehicle and the target vehicle.

The disclosure relates to a method for analyzing a plant, in particular for analyzing cannabis, using an illumination unit, a sensor unit, and an analysis unit, said analysis unit having a data-based classifier. The disclosure additionally relates to a device for analyzing a plant, said device comprising an illumination unit for lighting the plant to be analyzed and a sensor unit for receiving analysis input data, wherein the analysis input data contains at least spectral information, in particular an absorption spectrum or a reflection spectrum of the training plant. The device additionally comprises an analysis unit for analyzing the received analysis input data and for determining at least one property of the plant to be analyzed. The analysis unit is also designed to determine at least one property of the plant using a data-based classifier and the previously received analysis input data.

The disclosure relates to a method for analyzing a plant, in particular for analyzing cannabis, using an illumination unit, a sensor unit, and an analysis unit, said analysis unit having a data-based classifier. The disclosure additionally relates to a device for analyzing a plant, said device comprising an illumination unit for lighting the plant to be analyzed and a sensor unit for receiving analysis input data, wherein the analysis input data contains at least spectral information, in particular an absorption spectrum or a reflection spectrum of the training plant. The device additionally comprises an analysis unit for analyzing the received analysis input data and for determining at least one property of the plant to be analyzed. The analysis unit is also designed to determine at least one property of the plant using a data-based classifier and the previously received analysis input data.

Provided by the present invention is a picture focusing method, comprising: acquiring a video picture, and, according to an instruction command, determining a target object in the video picture; determining picture depth information of a video picture region in which the target object is located; acquiring parameter information of a camera corresponding to multiple video picture frames; using the picture depth information of the video picture region and the parameter information of the camera as the input of a preset focusing neural network model; and using focus adjustment information of the camera to perform a focus adjustment operation on the camera.

A face located along a stitch line in a spherical image is detected by rendering views of regions of the spherical image along the stitch line. The spherical image may be produced by combining first and second images. A first view of a projection of the spherical image is rendered. A scaling factor for rendering a second view of the projection is determined based characteristics of the first portion of the face. The second view is then rendered according to the scaling factor. The use of the scaling factor to render the second view causes a change in the depiction of the second portion of the face. For example, the scaling factor can indicate to change the resolution or expected size of the second portion of the face when rendering the second view. A face is then detected within the spherical image based on the rendered first and second views.

A face located along a stitch line in a spherical image is detected by rendering views of regions of the spherical image along the stitch line. The spherical image may be produced by combining first and second images. A first view of a projection of the spherical image is rendered. A scaling factor for rendering a second view of the projection is determined based characteristics of the first portion of the face. The second view is then rendered according to the scaling factor. The use of the scaling factor to render the second view causes a change in the depiction of the second portion of the face. For example, the scaling factor can indicate to change the resolution or expected size of the second portion of the face when rendering the second view. A face is then detected within the spherical image based on the rendered first and second views.