Systems, methods, apparatuses, and computer program products for contactless image-based blood oxygen estimation. A method may include receiving an image or video of a part of a subject captured by a camera of a computing device. The method may also include extracting a region of interest of the part of the subject from the image or video. The method may further include performing feature extraction of the region of interest. In addition, the method may include estimating a blood oxygen saturation level of the subject based on a spatial and temporal data analysis of more than two color channels. Feature extraction and estimation of the blood oxygen saturation level may include implementing a combination of spatial averaging, color channel mixing, and temporal trend analysis.

Poses of a person depicted within video frame may be determined. The poses of the person may be used to generate intermediate video frames between the video frames.

Images of a hand may be used to identify users. Quality, detail, and so forth of these images may vary. An image is processed to determine a first spatial mask. A first neural network comprising many layers uses the first spatial mask at a first layer and a second spatial mask at a second layer to process images and produce an embedding vector representative of features in the image. The first spatial mask provides information about particular portions of the input image, and is determined by processing the image with an algorithm such as an orientation certainty level (OCL) algorithm. The second spatial mask is determined using unsupervised training and represents weights of particular portions of the input image as represented at the second layer. The use of the masks allows the first neural network to learn to use or disregard particular portions of the image, improving overall accuracy.

A work instruction system includes: an imaging device that captures a captured image including an operator that performs a plurality of element tasks with respect to a target; an information processing device that includes a completion determination part that determines, for each element task, the completion of the element task, and a work identifier that identifies the element task to be performed next by the operator; and a display device that displays the element task to be performed next by the operator.

Apparatuses, systems, and techniques generate poses of an object based on image data of the object obtained from a first viewpoint of the object and a second viewpoint of the object. The poses can be evaluated to determine a portion of the image data usable by an estimator to generate a pose of the object.

A gesture recognition device that recognizes, based on an image obtained by imaging a person to be imaged with a digital camera having an imaging range changing mechanism for changing an imaging range, a gesture represented by a hand of the person to be imaged includes at least one processor. The at least one processor controls, in a case where a position of a face of the person to be imaged in the image deviates from a predetermined set position, an operation of the imaging range changing mechanism to cause the digital camera to capture a proper face position image with the position of the face as the set position, and determines whether the hand detected from the proper face position image is a right hand or a left hand based on a positional relationship between a position of the detected hand and the set position.

A sensor-based bare hand data labeling method and system are provided. The method comprises: performing device calibration processing on a depth camera and on one or more sensors respectively preset at one or more specified positions of a bare hand, so as to acquire coordinate transformation data; collecting a depth image of the bare hand by the depth camera, and collecting 6DoF data of one or more bone points; acquiring, based on the 6DoF data and the coordinate transformation data, three-dimensional position information of a preset number of bone points; determining two-dimensional position information of the preset number of bone points on the depth image based on the three-dimensional position information of the preset number of bone points; and labeling joint information on all of the bone points in the depth image according to the two-dimensional position information and the three-dimensional position information.

Described are a hand posture estimation method, an electronic device, and a non-transitory computer-readable storage medium. The method includes: obtaining an initial feature map corresponding to a hand region in a candidate image; obtaining a fused feature map by performing feature fusion processing on the initial feature map; wherein the feature fusion processing is configured to fuse features around a plurality of key points; the plurality of key points represent skeleton key nodes of the hand region; obtaining a target feature map by performing deconvolution processing on the fused feature map; wherein the deconvolution processing is configured to adjust a resolution of the fused feature map; and obtaining coordinate information of the plurality of key points based on the target feature map to determine a posture estimation result of the hand region in the candidate image.

A method for hand pose identification in an automated system includes providing map data of a hand of a user to a first neural network trained to classify features corresponding to a joint angle of a wrist in the hand to generate a first plurality of activation features and performing a first search in a predetermined plurality of activation features stored in a database in the memory to identify a first plurality of hand pose parameters for the wrist associated with predetermined activation features in the database that are nearest neighbors to the first plurality of activation features. The method further includes generating a hand pose model corresponding to the hand of the user based on the first plurality of hand pose parameters and performing an operation in the automated system in response to input from the user based on the hand pose model.

The technology described in this document can be embodied in a method that includes obtaining, by one or more image acquisition devices, a first image of a portion of a human body under illumination by electromagnetic radiation in a first wavelength range, and obtaining a second image of the portion of the human body under illumination by electromagnetic radiation in a second wavelength range. The method also includes generating, by one or more processing devices, a third image or template that combines information from the first image with information from the second image. The method also includes determining that one or more metrics representing a similarity between the third image and a template satisfy a threshold condition, and responsive to determining that the one or more metrics satisfy a threshold condition, providing access to the secure system.