A sensor device includes an array sensor having a plurality of detection elements arrayed in one or two dimensional manner, a signal processing unit configured to acquire a detection signal by the array sensor and perform signal processing, and a calculation unit. The calculation unit detects an object from the detection signal by the array sensor, and gives an instruction, to the signal processing unit, on region information generated on the basis of the detection of the object as region information regarding the acquisition of the detection signal from the array sensor or the signal processing for the detection signal.

The invention provides a positioning system that autonomously and continuously positions interchangeable camera devices on the subject as they move allowing the camera device to leverage its native functionality without modification or connectivity to the MTDS. The embodiments of the MTDS are configured to attach and align varying size of digital cameras, video cameras, mobile devices with a camera, or smart assistants with an embedded camera, action camera, webcam or laptop with embedded camera to the stand. The MTDS tracks the movements of the user during any video conference, photography or videography session. The MTDS includes a sensor, separate from the camera on the camera device, that sends the sensor data stream to a microcontroller in the stand and a machine learning co-processor detects the presence of a person. As the speaker or subject (user) moves, the MTDS tracks their movements and directs motors in the stand to pan or tilt the stand to orient the camera device's camera at the user. Through movement of the stand, the speaker or user stays centered in the camera frame. No data connections or software integrations are required to operate the MTDS.

An electronic device having a camera module for generating image data, a processor for receiving the image data and generating image data restored based on the image data. The electronic device also having a display module displaying the restored image data. The processor being configured to identify a class of the image data, select a filter corresponding to the class, and generate the restored image data by applying the selected filter to the image data.

Methods, systems, and storage media for dynamically identifying visual media capture formats based upon at least one of capture device, resultant visual medium, and/or application-supported conditions are disclosed. Exemplary implementations may: query a visual media capture device for at least a portion of the device-supported visual media capture formats; receive information regarding one or more capture device, resultant visual medium, and/or application-supported conditions relevant to capture of a resultant visual medium; execute an ordered plurality of rules to dynamically identify one or more device-supported visual media capture formats of the device-supported visual media capture formats that is configured to optimize the resultant visual medium; and cause the visual media capture device to initiate capture of the resultant visual medium using one of the identified device-supported visual media capture formats.

A method by a device performing localization using a set of sensors that are transported with the device is disclosed. A confidence score is determined for each of the sensors among the set of sensors. A subset of the sensors is defined from among the set of sensors that are to remain active based on their respective confidence scores satisfying a defined threshold. The method deactivates the sensors within the set of sensors having confidence scores that do not satisfy the defined threshold. The deactivation includes controlling power consumption by deactivated ones of the sensors.

The technical problem of automatically reprocessing an image captured by a camera in a manner that produces a personalized result is addressed by providing a customized image reprocessing system powered by machine learning techniques. The customized image reprocessing system is configured to automatically reprocess an image on a pixel level using a machine learning model that takes, as input, the image represented by pixel values, sensor data detected by the digital sensor of a camera at the time the image was captured, and, also, flash calibration parameters previously generated for that specific user.

An apparatus includes a device that acquires an image signal by photoelectrically converting a light beam of a subject that has entered via an optical system including a focus lens, a setting unit that sets a first region in the image signal, a focus detection unit that detects a defocus amount for each divided region of a region in the image signal, a detection unit that detects a second region as a region of a specific subject, based on the image signal, and a control unit that controls the focus lens based on the defocus amount of the divided region included in the second region if the first region and the second region overlap, and controls the focus lens based on the defocus amount of the divided region included in the first region if the first region and the second region do not overlap.

Example systems and methods for interacting with overlapping regions of interest (ROls) in machine vision applications are disclosed. An example system includes a machine vision camera, and a client computing device coupled to the machine vision camera. The client computing device configured to: receive an image from the machine vision camera; present the image on a canvas, wherein the canvas is part of a first user interface of a machine vision application displayed on the client computing device; present a first ROI associated with a first machine vision tool on the canvas; present a second ROI associated with a second machine vision tool on the canvas; and target the first ROI or the second ROI as a targeted ROI for interaction based upon distances from a pointer displayed on the canvas to features of the first ROI and the second ROI.

An electronic apparatus according to the present invention is an electronic apparatus that is capable of executing eye proximity sensing to sense whether an eye is in proximity of an eyepiece, and line-of-sight detection to detect a line-of-sight of a user, including: a first light source configured to emit light for the eye proximity sensing; a second light source configured to emit light for the line-of-sight detection; an eye proximity sensing sensor configured to receive light for the eye proximity sensing; and a line-of-sight detecting sensor configured to receive light for the line-of-sight detection, wherein a first wavelength, which is a peak wavelength of the light emitted by the first light source, is different from a second wavelength, which is a peak wavelength of the light emitted by the second light source.

A system, method, and apparatus for implementing workflows across multiple differing systems and devices is provided herein. During operation, a security level at an entry point will be increased temporarily when a camera moves its field of view away from the entry point. The security level will return to its original state when the camera moves its field of view back to the entry point.