Systems, methods, and non-transitory media are provided for adjusting camera settings based on event data. An example method can include obtaining, via an image capture device of a mobile device, an image depicting at least a portion of an environment; determining a match between one or more visual features extracted from the image and one or more visual features associated with a keyframe; and based on the match, adjusting one or more settings of the image capture device.

Systems and methods herein describe using a neural network to identify a first set of joint location coordinates and a second set of joint location coordinates and identifying a three-dimensional hand pose based on both the first and second sets of joint location coordinates.

Systems, devices, and methods are presented for segmenting an image of a video stream with a client device by receiving one or more images depicting an object of interest and determining pixels within the one or more images corresponding to the object of interest. The systems, devices, and methods identify a position of a portion of the object of interest and determine a direction for the portion of the object of interest. Based on the direction of the portion of the object of interest, a histogram threshold is dynamically modified for identifying pixels as corresponding to the portion of the object of interest. The portion of the object of interest is replaced with a graphical interface element aligned with the direction of the portion of the object of interest.

A method to identify positions of fingers of a hand is described. The method includes capturing images of a first hand using a plurality of cameras that are part of a wearable device. The wearable device is attached to a wrist of a second hand and the plurality of cameras of the wearable device is disposed around the wearable device. The method includes repeating capturing of additional images of the first hand, the images and the additional images captured to produce a stream of captured image data during a session of presenting the virtual environment in a head mounted display (HMD). The method includes sending the stream of captured image data to a computing device that is interfaced with the HMD. The computing device is configured to process the captured image data to identify changes in positions of the fingers of the first hand.

A system includes a lighting module, a processing module, and photovoltaic units. Each of the photovoltaic units receives light reflected off a body portion which is illuminated by light from the lighting module, and converts light energy of the reflected light into electricity. The processing module stores modes each of which specifies a code set. When one of the modes is selected, the processing module activates the lighting module to emit light based on the code set specified by the mode thus selected. The processing module converts electrical quantities measured individually for the photovoltaic units into respective code parameters, and composes an identification code using the code parameters.

A system provides assistance during bottle-feeding. Video images of a subject bottle-feeding an infant are captured and displayed. Using image analysis, a reorientation of the bottle and/or infant is determined that is required in order to reach a desired bottle orientation and/or infant orientation. Reorientation instructions are provided in combination with the video images to assist the subject in reorienting the bottle and/or the infant to achieve the desired bottle orientation.

A safety belt detection method, apparatus, computer device and computer readable storage medium are disclosed. The safety belt detection method includes the steps as follows. An image to be detected is obtained. The image to be detected is inputted into a detection network which includes a global dichotomous branch network and a grid classification branch network. A dichotomous result, which indicates whether a driver is wearing a safety belt and is output from the global dichotomous branch network, is obtained. A grid classification diagram, which indicates a position information of the safety belt and is output from the grid classification branch network, is obtained based on image classification. A detection result of the safety belt, indicating whether the driver is wearing the safety belt normatively, is obtained based on the dichotomous result and the grid classification diagram.

Vision-based stimulus monitoring and response systems and methods are presented, wherein detection, via image(s) of a patient through an external stimulus, such as a caregiver, prompts analysis of the response of the patient, via secondary patient sensors or via analysis of patient image(s), to determine an autonomic nervous system (ANS) state.

The present technology relates to an image processing apparatus, an image processing method, and a recording medium capable of appropriately determining a direction in which a subject being imaged faces.

The present technology includes a detector that detects a face and a predetermined part of a subject in a captured image, a face direction determiner that determines a direction in which the face detected by the detector faces, a part direction determiner that determines a direction in which the predetermined part detected by the detector faces, and a first direction decider that decides a direction in which the subject faces by using a determination result by the face direction determiner and a determination result by the part direction determiner. The present technology can be applied to an image processing apparatus that controls framing.

A method for detecting a finger occlusion image includes: N first original occlusion images and M first non-occlusion images are acquired, and first training data set is generated based on first original occlusion images and first non-occlusion images; first training is performed, based on first training data set, on neural network model for detection of finger occlusion image; L second original occlusion images and K second non-occlusion images are acquired, and second training data set is generated based on second original occlusion images and second non-occlusion images; linear classifier in neural network model having completed first training is replaced with iterative training module to form finger occlusion image detection model; second training is performed on finger occlusion image detection model based on second training data set; image to be detected is input into trained finger occlusion image detection model, to determine whether image to be detected is finger occlusion image.