Abstract: Disclosed is a composition for inhibiting nitrate decomposition and its preparation method, which belongs to a field of photocatalytic technology, comprising: weighing titanium dioxide and pure phase metal carbonate or pure phase metal bicarbonate proportionally; adding the weighed pure phase metal carbonate or the pure phase metal bicarbonate to titanium dioxide for grinding to obtain a metal carbonate/bicarbonate-containing mixture. The method of inhibiting nitrate decomposition using the metal carbonate/bicarbonate of the present disclosure has a significant ability to inhibit nitrate decomposition, and the experimental results show that the method of inhibiting nitrate decomposition using the metal carbonate/bicarbonate can effectively inhibit the decomposition of the nitrate under irradiation for a long time.

Abstract: This application discloses a scene recognition method and apparatus, which may be applied to game scene recognition. The method includes: obtaining one or more drawing instructions, and determining a to-be-drawn target object based on a description parameter carried in the one or more drawing instructions. Based on a particular relationship between scenes and an object inside scenes, a corresponding target scene may be determined based on the target object.

Abstract: Three-dimensional modeling includes obtaining a hemispherical or spherical visible light-depth image capturing an operational environment of a user device, generating a perspective converted hemispherical or spherical visible light-depth image, generating a three-dimensional model of the operational environment based on the perspective converted hemispherical or spherical visible light-depth image, and outputting the three-dimensional model. Obtaining the hemispherical or spherical visible light-depth image includes obtaining a hemispherical or spherical visual light image and obtaining a hemispherical or spherical non-visual light depth image. Generating the perspective converted hemispherical or spherical visible light-depth image includes generating a perspective converted hemispherical or spherical visual light image and generating a perspective converted hemispherical or spherical non-visual light depth image.

Abstract: Spherical or hemispherical non-visible light depth detection includes projecting a hemispherical non-visible light static structured light pattern, in response to projecting the hemispherical non-visible light static structured light pattern, detecting non-visible light, determining three-dimensional depth information based on the detected non-visible light and the projected hemispherical non-visible light static structured light pattern, and outputting the three-dimensional depth information.

Abstract: The present application proposes a display panel, including a display area and a non-display area surrounding the display area, wherein the display panel includes: a plurality of metal traces and a plurality of color resist blocks located in the display area; and a test key located in the non-display area, wherein the test key includes a plurality of color resist patterns and a plurality of metal patterns, each of the color resist patterns corresponds to a corresponding one of the color resist blocks, each of the metal patterns corresponds to one of the metal traces, and each of the color resist patterns is disposed correspondingly between adjacent ones of the metal patterns.

Abstract: A method includes receiving a facial image; receiving a facial image; and obtaining, using a multi-task convolutional neural network, a detected face location and a facial characteristic category set of a plurality of first facial characteristic categories; selecting a first face alignment model from a plurality of face alignment models based on the facial characteristic category set; and obtaining, using the first face alignment model, a plurality of facial landmarks. The first facial characteristic categories are arranged hierarchically. A hierarchy of the first facial characteristic categories includes a plurality of first levels corresponding to a plurality of corresponding facial characteristics.

Abstract: Three-dimensional modeling includes obtaining a hemispherical or spherical visible light-depth image capturing an operational environment of a user device, generating a perspective converted hemispherical or spherical visible light-depth image, generating a three-dimensional model of the operational environment based on the perspective converted hemispherical or spherical visible light-depth image, and outputting the three-dimensional model. Obtaining the hemispherical or spherical visible light-depth image includes obtaining a hemispherical or spherical visual light image and obtaining a hemispherical or spherical non-visual light depth image. Generating the perspective converted hemispherical or spherical visible light-depth image includes generating a perspective converted hemispherical or spherical visual light image and generating a perspective converted hemispherical or spherical non-visual light depth image.

Abstract: Three-dimensional tracking includes obtaining a hemispherical visible light-depth image capturing an operational environment of a user device. Obtaining the hemispherical visible light-depth image includes, obtaining a hemispherical visual light image, and obtaining a hemispherical non-visual light depth image. Three-dimensional tracking includes generating a perspective converted hemispherical visible light-depth image. Generating the perspective converted hemispherical visible light-depth image includes generating a perspective converted hemispherical visual light image, and generating a perspective converted hemispherical non-visual light depth image. Three-dimensional tracking includes generating object identification and tracking data representing an external object in the operational environment based on the perspective converted hemispherical visible light-depth image and outputting the object identification and tracking data.

Abstract: Systems and methods are described for three-dimensional localization using light-depth images. For example, some of the methods include accessing a light-depth image, wherein the light-depth image includes a non-visible light depth channel representing distances of objects in a scene viewed from an image capture device, and the light-depth image includes one or more visible light channels that are temporally and spatially synchronized with the depth channel; determining a set of features of the scene in a space based on the light-depth image; accessing a map data structure that includes features based on light data and position data for the objects in the space; accessing matching data derived by matching the set of features of the scene to features of the map data structure; determining a location of the image capture device relative to objects in the space based on the matching data.

Abstract: The present application proposes a display panel, including a display area and a non-display area surrounding the display area, wherein the display panel includes: a plurality of metal traces and a plurality of color resist blocks located in the display area; and a test key located in the non-display area, wherein the test key includes a plurality of color resist patterns and a plurality of metal patterns, each of the color resist patterns corresponds to a corresponding one of the color resist blocks, each of the metal patterns corresponds to one of the metal traces, and each of the color resist patterns is disposed correspondingly between adjacent ones of the metal patterns.

Abstract: Spherical or hemispherical non-visible light depth detection includes projecting a hemispherical non-visible light static structured light pattern, in response to projecting the hemispherical non-visible light static structured light pattern, detecting non-visible light, determining three-dimensional depth information based on the detected non-visible light and the projected hemispherical non-visible light static structured light pattern, and outputting the three-dimensional depth information.

Abstract: Systems, devices, and methods of displaying and/or recording multiple pictures in a picture (PIP) on the same display of a digital display device are disclosed. The PIPs can show objects from the main field view of the display device, such as a front camera lens, as well as objects from a different field of view, such as a back camera lens. The PIPs can further track the objects that are being displayed.

Abstract: Methods and imaging devices are disclosed for determining a first direction to move a lens in an autofocus operation. For example, one method includes capturing a plurality of frames depicting a scene, selecting a portion of at least one frame that corresponds to an object in a displayed frame, and tracking the object in the plurality of frames, where tracking the object provides a reference parameter of the object for each of the plurality of frames. The method may further includes detecting a change in the reference parameter of at least a first frame and a second frame of the plurality of frames, determining a focus direction based on the change in the reference parameter, and initiating a focus operation by moving a lens of the imaging device based on the determined focus direction.

Abstract: Systems and methods for continuous automatic focus are disclosed. In some aspects, an object to focus on is selected from a field of view of a camera. At least one region of the field of view that includes the object is monitored for change. Regions of the field of view that do not include the object may not be monitored, thus improving efficiency and speed. If a change in the image content of the monitored region(s) occurs, the method may trigger a focus event. If the object moves to different region(s) of the field of view, the method detects the movement and adjusts the regions of the image being monitored to maintain focus on the object. In some aspects, the size of the object is also monitored. If the size of the object changes, a focus event may be triggered, depending on whether the amount of change exceeds predetermined parameters.

Abstract: An apparatus includes a processor and a memory. The memory stores instructions that when executed by the processor cause the processor to perform operations including receiving a command to perform an image capture of an image including an object. The operations further include determining a first speed threshold based on a first light condition at a first time. The operations further include determining a first speed of the object. The operations further include, in response to determining the first speed of the object exceeds the first speed threshold, determining a second speed threshold based on a second light condition detected at a second time. The operations further include determining a second speed of the object. The operations further include initiating the image capture of the image in response to determining the second speed of the object does not exceed the second speed threshold.

Abstract: An apparatus includes a processor and a memory. The memory stores instructions that when executed by the processor cause the processor to perform operations including receiving a command to perform an image capture of an image including an object. The operations further include determining a first speed threshold based on a first light condition at a first time. The operations further include determining a first speed of the object. The operations further include, in response to determining the first speed of the object exceeds the first speed threshold, determining a second speed threshold based on a second light condition detected at a second time. The operations further include determining a second speed of the object. The operations further include initiating the image capture of the image in response to determining the second speed of the object does not exceed the second speed threshold.

Abstract: A method performed by an electronic device is described. The method includes obtaining sensor information. The method also includes identifying a class of an object based on the sensor information. The method further includes determining one or more actions based on the sensor information, the class of the object and an action usage history. The method additionally includes performing at least one of the one or more actions based on at least one application.

Abstract: Systems, devices and methods for improved tracking with an electronic device are disclosed. The disclosures employ advanced exposure compensation and/or stabilization techniques. The tracking features may therefore be used in an electronic device to improve tracking performance under dramatically changing lighting conditions and/or when exposed to destabilizing influences, such as jitter. Historical data related to the lighting conditions and/or to the movement of a region of interest containing the tracked object are advantageously employed to improve the tracking system under such conditions.

Abstract: Systems and methods for continuous automatic focus are disclosed. In some aspects, an object to focus on is selected from a field of view of a camera. At least one region of the field of view that includes the object is monitored for change. Regions of the field of view that do not include the object may not be monitored, thus improving efficiency and speed. If a change in the image content of the monitored region(s) occurs, the method may trigger a focus event. If the object moves to different region(s) of the field of view, the method detects the movement and adjusts the regions of the image being monitored to maintain focus on the object. In some aspects, the size of the object is also monitored. If the size of the object changes, a focus event may be triggered, depending on whether the amount of change exceeds predetermined parameters.

Abstract: Methods and imaging devices are disclosed for determining a first direction to move a lens in an autofocus operation. For example, one method includes capturing a plurality of frames depicting a scene, selecting a portion of at least one frame that corresponds to an object in a displayed frame, and tracking the object in the plurality of frames, where tracking the object provides a reference parameter of the object for each of the plurality of frames. The method may further includes detecting a change in the reference parameter of at least a first frame and a second frame of the plurality of frames, determining a focus direction based on the change in the reference parameter, and initiating a focus operation by moving a lens of the imaging device based on the determined focus direction.