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 system for quantifying viewer engagement with a video playing on a display includes at least one camera to acquire image data of a viewing area in front of the display. A microphone acquires audio data emitted by a speaker coupled to the display. The system also includes a memory to store processor-executable instructions and a processor. Upon execution of the processor-executable instructions, the processor receives the image data and the audio data and determines an identity of the video displayed on the display based on the audio data. The processor also estimates a first number of people present in the viewing area and a second number of people engaged with the video. The processor further quantifies the viewer engagement of the video based on the first number of people and the second number of people.

A mark irradiation unit (130) irradiates an object with a mark. An image capture unit (140) captures an image of the object, and generates image data. Then, an image capture area data generation unit recognizes a position of the mark in the object, and cuts out image capture area data which is a part of the image data on the basis of the mark. For this reason, the mark irradiation unit (130) irradiates the object with the mark, and thus even when a positioning symbol is not printed on the object to be stored as the image data, only a necessary portion in the image data is cut out.

Apparatus comprises a camera configured to capture images of a user in a scene; a depth detector configured to capture depth representations of the scene, the depth detector comprising an emitter configured to emit a non-visible signal; a mirror arranged to reflect at least some of the non-visible signal emitted by the emitter to one or more features within the scene that would otherwise be occluded by the user and to reflect light from the one or more features to the camera; a pose detector configured to detect a position and orientation of the mirror relative to at least one of the camera and depth detector; and a scene generator configured to generate a three-dimensional representation of the scene in dependence on the images captured by the camera and the depth representations captured by the depth detector and the pose of the mirror detected by the pose detector.

A method and an apparatus include a display, an iris scanning sensor, and a processor functionally coupled with the display, and the iris scanning sensor, wherein the processor activates the iris scanning sensor when receiving a display-on event in a display-off state that is an intended user input.

Methods and systems for activity monitoring include capturing an infrared image of an environment that comprises at least one patient being monitored and at least one infrared-emitting tag. A relationship between the patient being monitored and the at least one infrared-emitting tag is determined. An activity conducted by the patient being monitored is determined based on the relationship between the patient being monitored and the at least one infrared-emitting tag. A course of treatment for the patient being monitored is adjusted based on the determined activity.

A photoacoustic image evaluation apparatus includes a processor configured to acquire a first photoacoustic image generated at a first point in time and a second photoacoustic image generated at a second point in time before the first point in time, the first and second photoacoustic images being photoacoustic images generated by detecting photoacoustic waves generated inside a subject, who has been subjected to blood vessel regeneration treatment, by emission of light into the subject; acquire a blood vessel regeneration index, which indicates a state of a blood vessel by the regeneration treatment, based on a difference between a blood vessel included in the first photoacoustic image and a blood vessel included in the second photoacoustic image; and display the blood vessel regeneration index on a display.

A photoacoustic image evaluation apparatus includes a processor configured to acquire a first photoacoustic image generated at a first point in time and a second photoacoustic image generated at a second point in time before the first point in time, the first and second photoacoustic images being photoacoustic images generated by detecting photoacoustic waves generated inside a subject, who has been subjected to blood vessel regeneration treatment, by emission of light into the subject; acquire a blood vessel regeneration index, which indicates a state of a blood vessel by the regeneration treatment, based on a difference between a blood vessel included in the first photoacoustic image and a blood vessel included in the second photoacoustic image; and display the blood vessel regeneration index on a display.

A method for optimizing a neural network is provided, including: (1) capturing, via a first sensor group having a first field of view, a first sample set having a first sensor domain corresponding to the first field of view; (2) capturing, via a second sensor group having a second field of view, a second sample set having a second sensor domain corresponding to the second field of view; (3) generating regions of interest of the second sample set; (4) translating the regions of interest to the first sensor domain; (5) identifying nodes of the neural network which correspond to the translated regions; and (6) optimizing the neural network by at least one of (a) increasing the weight value of the nodes corresponding to the one or more translated regions and (b) decreasing the weight value of the nodes not corresponding to the one or more translated regions.

Methods and apparatus for applying data analytics such as deep learning algorithms to sensor data. In one embodiment, an electronic device such as a camera apparatus including a deep learning accelerator (DLA) communicative with an image sensor is disclosed, the camera apparatus configured to evaluate unprocessed sensor data from the image sensor using the DLA. In one variant, the camera apparatus provides sensor data directly to the DLA, bypassing image signal processing in order to improve the effectiveness the DLA, obtain DLA results more quickly than using conventional methods, and further allow the camera apparatus to conserve power.