A gas detection device includes: a processor that visualizes a gas by performing image processing on infrared image data in an inspection region imaged by an imaging device; a display that displays an inspection image that reflects a result of the image processing; and an input interface that receives an input of supplementary information on the inspection image displayed on the display.

Systems and methods for detecting udder disease based on machine learning methods and complementary supporting techniques are presented. Included are methods for assembling time sequences of images of each animal of a herd or set for subsequent use in per-animal image analysis for disease detection. Methods presented also include image pre-processing methods used prior to image analysis, resulting in contrast and resolution optimization such as appropriate image intensity level adjustment and resolution downsampling for more rapid and more accurate disease detection. Combinatorial techniques for compositing whole-udder images or udder-quarter images from partial images captures are described. Methods are provided for power usage optimization in regard to computing resources used in the computing-intensive AI analysis methods. Location-based and animal history-based detection refinements are incorporated into described systems. Further presented are methods for multi-modal and multi-factor detection of udder disease, as well as methods for infection type classification.

In one embodiment, a method includes accessing first image data generated by a first image sensor having a first filter array that has a first filter pattern. The first filter pattern includes a number of first filter types. The method also includes accessing second image data generated by a second image sensor having a second filter array that has a second filter pattern different from the first filter pattern. The second filter pattern includes a number of second filter types, the number of second filter types and the number of first filter types have at least one filter type in common. The method also includes determining a correspondence between one or more first pixels of the first image data and one or more second pixels of the second image data based on a portion of the first image data associated with the filter type in common.

Circuit board inspection by receiving a near infrared (NIR) image of at least a portion of a circuit board, analyzing the NIR image using a machine learning model, and detecting anomalous circuit board portions according to the analysis.

A system for dense depth computation aided by sparse feature matching generates a first image using a first camera, a second image using a second camera, and a third image using a third camera. The system generates a sparse disparity map using the first image and the third image by (1) identifying a set of feature points within the first image and a set of corresponding feature points within the third image, and (2) identifying feature disparity values based on the set of feature points and the set of corresponding feature points. The system also applies the first image, the second image, and the sparse disparity map as inputs for generating a dense disparity map.

Methods, devices, and systems for identifying charging devices are provided. In one aspect, a method of identifying a charging device include: capturing an infrared image and a depth image of a current field of view with a depth camera; determining, according to the infrared image, that there are one or more suspected charging device areas that satisfy first specified conditions; determining, according to the depth image, that there is a target charging device area whose height relative to a depth camera is within a specified range in the one or more suspected charging device areas; and identifying the charging device according to the target charging device area. The first specified conditions indicate that a gray-scale value of each of pixels in an area is greater than a second specified value, and a number of the pixels in the area is greater than a third specified value.

Related methods are provided for establishing a baseline value to represent an eyelid opening dimension for a person engaged in an activity, where the activity may be driving a vehicle, operating industrial equipment, or performing a monitoring or control function; and for operating a system for monitoring eyelid opening values with real time video data.

A dual sensor imaging system and an imaging method thereof are provided. The method includes: identifying an imaging scene; controlling a color sensor and an IR sensor to respectively capture color images and IR images by adopting capturing conditions suitable for the imaging scene; calculating a signal-to-noise ratio (SNR) difference between each color image and the IR images, and a luminance mean value of each color image; selecting the color image and IR image captured under capturing conditions of having the SNR difference less than an SNR threshold and the luminance mean value greater than a luminance threshold to execute a feature domain transformation to extract partial details of the imaging scene; and fusing the selected color image and IR image to adjust the partial details of the color image according to a guidance of the partial details of the IR image to obtain a scene image with full details.

A monitoring system that is configured to monitor a property is disclosed. The monitoring system includes a thermal camera that is configured to generate a thermal image of the property. The monitoring system further includes a monitor control unit that is configured to receive, from the thermal camera, the thermal image. The monitor control unit is further configured to, based on the thermal image, determine a temperature of a portion of the property depicted in the thermal image. The monitor control unit is further configured to determine that the temperature of the portion of the property depicted in the thermal image satisfies a temperature threshold. The monitor control unit is further configured to, based on determining that the temperature of the portion of the property depicted in the thermal image satisfies the temperature threshold, select and perform a monitoring system action.

A device for determining a face of a dice resting on a surface allowing an optical signal to pass, the dice being composed of a plurality of faces each including a visual marking uniquely identifying the face, and the device including: illumination means for illuminating a face of a dice through the surface, the illumination means being placed under the surface and oriented in the direction of the surface, the illumination means including a plurality of optical signal sources disposed at various positions under the surface; means for acquiring at least one image of the optical signals reflected by the face of the dice resting on the surface, the acquisition means being placed under the surface and being facing the surface; and an analysis unit including means for processing the image to determine the face of the dice resting on the surface allowing the optical signal to pass.