A texture recognition device and a display device are provided. The texture recognition device includes a backlight element, configured to provide first backlight; a light constraint element, configured to perform a light divergence angle constraint process on the first backlight to obtain second backlight with a divergence angle within a preset angle range, the second backlight being transmitted to a detection object; and a photosensitive element, configured to detect the second backlight reflected by a texture of the detection object to recognize a texture image of the texture of the detection object.

A method of thermographic inspection is disclosed, including applying a thermal pulse to a surface and capturing an image of a thermal response of the surface. The image is captured with an infrared camera through a polarizer having a first orientation. The method further includes determining, by analysis of the image, whether the thermal response is indicative of a crack on the surface.

A two-dimensional image of a vehicle occupant in a vehicle is collected. The collected two-dimensional image is input to a machine learning program trained to output one or more reference points of the vehicle occupant, each reference point being a landmark of the vehicle occupant. One or more reference points of the vehicle occupant in the two-dimensional image is output from the machine learning program. A location of the vehicle occupant in an interior of the vehicle is determined based on the one or more reference points. A vehicle component is actuated based on the determined location. For each of the one or more reference points, a similarity measure is determined between the reference point and a three-dimensional reference point, the similarity measure based on a distance between the reference point and the three-dimensional reference point.

A smart chip assembly capable of communicating with each other and variable array in packaging contains at least two intelligent single-chip, which can be easily formed into a smart chip assembly by cutting after packaging. Each intelligent single-chip has a camera lens module, and each lens module is arranged in a different position and angle to separately obtain images of different angles or depths of field of the object to be recognized, and provide each intelligent single-chip to independently process the image and judge by itself. The result of each judgment based on the obtained images of different angles or depths of field can also be used as one of the options for reference judgment. Then through the mutual communication between the intelligent single-chips, they calculate and analyze different image and image dynamics, and make comprehensive judgments to obtain the best interpretation. Finally, the command is sent to the connected power mechanism to perform corresponding actions to achieve more flexible application and improve the accuracy of identification.

A smart chip assembly capable of communicating with each other and variable array in packaging contains at least two intelligent single-chip, which can be easily formed into a smart chip assembly by cutting after packaging. Each intelligent single-chip has a camera lens module, and each lens module is arranged in a different position and angle to separately obtain images of different angles or depths of field of the object to be recognized, and provide each intelligent single-chip to independently process the image and judge by itself. The result of each judgment based on the obtained images of different angles or depths of field can also be used as one of the options for reference judgment. Then through the mutual communication between the intelligent single-chips, they calculate and analyze different image and image dynamics, and make comprehensive judgments to obtain the best interpretation. Finally, the command is sent to the connected power mechanism to perform corresponding actions to achieve more flexible application and improve the accuracy of identification.

A drone identification system including a drone having an LED “license plate” and an identification device is disclosed. The drone's LEDs emit a color pattern signal that is captured by the identification device, which is then used to uniquely identify the drone. Specifically, the identification device translates the color pattern signal into a unique identification code that is used to identify the drone. The identification code may be transmitted to a server to store the identification information in a directory for future use.

When it is determined that a presence of a traffic light is not recognized in an image acquired by a first image acquisition operation by a camera, the presence of the traffic light is recognized in an image acquired by a second image acquisition operation in which a recognition accuracy of the presence of the traffic light is improved. In this second image acquisition operation, it is assumed that a pedestrian is stopped in front of a pedestrian crossing, and there is little demand for increasing a detection speed of objects such as moving objects present in a surrounding area. Thus, it is possible to sufficiently obtain the recognition accuracy of the traffic light by the second image acquisition operation that gives priority to the recognition accuracy of the presence of the traffic light.

System that analyzes data from a smart shelf that is monitored by weight sensors and cameras to identify items that are removed from the shelf and the locations of these items on the shelf. By using multiple shelf weight sensors, the location of items removed from or added to a shelf can be calculated from static equilibrium conditions. This weight-based location can be compared to regions of visual change in camera images to cross-check the location of events and to improve accuracy. The location of an item change may also be used in conjunction with a planogram to determine the item expected to be at this location; the expected item can be compared to the item identified using image analysis to further increase item identification accuracy. Weight changes can also be used to determine the quantity of items taken from a shelf.

Disclosed, among other things, is volumetric security, which may provide improved techniques for identifying a subject by receiving inputs from a 3-dimensional (3D), or volumetric, image, a 2-dimensional (2D) image, an audio feed or recording, a biometric scanning system, a password entry system, a location-based technology system such as Global Positioning System (GPS) or interactions between a Bluetooth® beacon and an application on a mobile device, or another data source, comparing the inputs to data stored in one or more databases, assigning weights to the inputs, outputting weighted values, and integrating an algorithm that factors the weighted values to output a subject identifier and a confidence score. The confidence score may define a probability that the identifier correctly identifies a subject.

A method for determining a growth height of a plant, an electronic device, and storage medium are provided. The method includes controlling a camera device to capture a plant to be detected, and obtaining a color image and a depth image of the plant to be detected. The color image and the depth image are aligned and an alignment image is obtained. The color image is detected using a pre-trained mobilenet-ssd network, and a detection box including the plant to be detected is obtained. A depth value of each of pixel points in the detection box is determined, and target depth values are obtained. A mean value and a standard deviation of the target depth values are determined, and a height of the plant to be detected is determined. According to the method, accuracy of the height of the plant can be improved.