A system and method for analyzing bodily fluid include a sample holder holding a bodily fluid sample, an image capture device generating an image of the bodily fluid sample comprising a plurality of fields of view. An image processor is programmed to determine a biofilm in the bodily fluid sample from the image, determine a biofilm area or volume within each of the plurality of fields of view to form a plurality of biofilm areas, determine a total biofilm area or total biofilm volume by adding the plurality of biofilm areas, determine a first value corresponding to a comparison of the total biofilm area or the total biofilm volume and a total volume of the bodily fluid sample, and classify the first value into a classification. An analyzer, using the classification, displays an indicator on a display for indicating the classification of the biofilm within the bodily fluid sample.

A thermal image generating apparatus for generating a thermal image regarding a target object is provided. The apparatus includes a memory configured to store a first thermal image, a first sensor, configured to measure a temperature of the target object, a second sensor configured to measure a distance to the target object, a third sensor configured to detect a movement of the thermal image generating apparatus, and a controller configured to generate a second thermal image based on temperature information received from the first sensor, distance information received from the second sensor, and movement information received from the third sensor, and generate a third thermal image based on the first thermal image and the second thermal image.

Embodiments of the present disclosure provide unmanned aerial vehicle-based measurement techniques for building envelope surfaces One such method comprises acquiring, by an unmanned aerial vehicle, an air velocity measurement at an external surface of the high-rise building at a point on the external surface; acquiring, by the unmanned aerial vehicle, an external temperature at the external surface of the high-rise building at the point on the external surface; acquiring, by an infrared camera sensor of the unmanned aerial vehicle, IR measurements at the external surface of the high-rise building at the point on the external surface; and transferring, by the unmanned aerial vehicle, the IR measurements and the external air velocity and temperature measurements to a remote base station, wherein a current thermal performance of the external surface of the high-rise building is determined using the external air velocity, temperature, and IR measurements.

A far-infrared image acquisition unit acquires a far-infrared image. An image conversion unit converts the acquired far-infrared image into a visible light image. A visible light image trained model storage unit stores a first visible light image trained model having performed learning using the visible light image as training data. A transfer learning unit performs transfer learning on a first visible light image trained model by using the visible light image obtained by conversion as training data to generate a second visible light image trained model.

A visible light image generation model learning unit generates a trained visible light image generation model that generates a visible light image in a second time zone from a far-infrared image in a first time zone. The visible light image generation model learning unit includes a first learning unit that machine-learns the far-infrared image in the first time zone and a far-infrared image in the second time zone as teacher data and generates a trained first generation model that generates the far-infrared image in the second time zone from the far-infrared image in the first time zone, and a second learning unit that machine-learns the far-infrared image in the second time zone and the visible light image in the second time zone as teacher data and generates a trained second generation model that generates the visible light image in the second time zone from the far-infrared image in the second time zone.

Visibility of a license plate and color reproducibility of a vehicle body are improved in a monitoring camera.

A vehicle body area detection unit detects a vehicle body area of a vehicle from an image signal. A license plate area detection unit detects a license plate area of the vehicle from the image signal. A vehicle body area image processing unit performs processing of the image signal corresponding to the detected vehicle body area. A license plate area image processing unit performs processing different from the processing of the image signal corresponding to the vehicle body area on the image signal corresponding to the detected license plate area. A synthesis unit synthesizes the processed image signal corresponding to the vehicle body area and the processed image signal corresponding to the license plate area.

An image processing apparatus that determines settings for capturing an invisible light image to be combined with a visible light image is disclosed. The image processing apparatus determines (i) a first target value relating to a signal level of an invisible light image for adjusting brightness of the visible light image, (ii) a second target value relating to a signal level of an invisible light image for adjusting contrast of the visible light image, (iii) a final target value using the first target value and the second target value, and (iv) settings for capturing the invisible light image based on the final target value.

An image processing apparatus that determines settings for capturing an invisible light image to be combined with a visible light image is disclosed. The image processing apparatus determines (i) a first target value relating to a signal level of an invisible light image for adjusting brightness of the visible light image, (ii) a second target value relating to a signal level of an invisible light image for adjusting contrast of the visible light image, (iii) a final target value using the first target value and the second target value, and (iv) settings for capturing the invisible light image based on the final target value.

Proposed are a system and a method for supporting learning, which can increase learning attitudes and efficiency by determining a learning state of a learner by using learner's bio information and face recognition information and providing a feedback of lighting color, incense spray, and music output based on the learning state. The proposed system for supporting learning configures the learning state including a bio state and a learning focus based on the learner's bio information and the face recognition information during learning, and provides, to the learner, at least one feedback of lighting color change, illuminance change, incense spray, and sound source playback in accordance with the learning state.

Methods, systems, apparatus, and computer-readable media for data band selection using machine learning. In some implementations, image data comprising information for each of multiple wavelength bands is obtained. A multi-layer neural network is trained using the image data to perform one or more classification or regression tasks. A proper subset of the wavelength bands is selected based on parameters of a layer of the trained multi-layer neural network, where the parameters were determined through training of the multi-layer neural network using the image data. Output is provided indicating that the selected wavelength bands are selected for the one or more classification or regression tasks.