A scanner acquires a set of images of a hand of a user to facilitate identification. These images may vary, due to changes in relative position, pose, lighting, obscuring objects such as a sleeve, and so forth. A first neural network determines output data comprising a spatial mask and a feature map for individual images in the set. The output data for two or more images is combined to provide aggregate data that is representative of the two or more images. The aggregate data may then be processed using a second neural network, such as convolutional neural network, to determine an embedding vector. The embedding vector may be stored and associated with a user account. At a later time, images acquired from the scanner may be processed to produce an embedding vector that is compared to the stored embedding vector to identify a user at the scanner.

A system identifies anomalies in an image of an object. An input image of the object containing zero or more anomalies is supplied to an image encoder. The image encoder generates an image model. The image model is applied to an image decoder that forms a substitute non-anomalous image of the object. Differences between the input image and the substitute non-anomalous image identify zero or more areas of the input image that contain the zero or more the anomalies. The system implements a flow-based model and has been trained using (a) a set of augmented anomaly-free images of the object applied at the image encoder and (b) a reconstruction loss calculated based on a norm of differences between each augmented anomaly-free image of the object and a corresponding output image from the image decoder.

A method for detecting bone structure includes allocating at least one bone portion from a bone image composed by pixels each including luminance value relating to bone structural parameter; aligning a major axis of principal axes of moment of inertia of the bone portion to a principal axis of Cartesian coordinate system; a cortical bone area of the bone portion intersecting at least one principal plane perpendicular to the principal axis, and each principal plane forming an outer and inner contour line of the cortical bone area; processing an analytic algorithm for the bone structural parameter; calculating distributed state of the bone structural parameter in each principal plane to obtain a distributed state of the bone structural parameter of the bone portion; and obtaining a distributed state of the bone structural parameter of the bone portion by assembling distributed state of the bone structural parameter of each bone portion.

A medical-image processing apparatus according to an embodiment includes processing circuitry. The processing circuit acquires an initial value of an outline corresponding vector that corresponds to an outline of a subject included in medical image data. The processing circuitry updates the outline corresponding vector based on a derivative that is acquired by differentiating a cost function with respect to the outline corresponding vector by the outline corresponding vector, and on the initial value of the outline corresponding vector.

Shadow-grams are used for edge inspection and metrology of a stacked wafer. The system includes a light source that directs collimated light at an edge of the stacked wafer, a detector opposite the light source, and a controller connected to the detector. The stacked wafer can rotate with respect to the light source. The controller analyzes a shadow-gram image of the edge of the stacked wafer. Measurements of a silhouette of the stacked wafer in the shadow-gram image are compared to predetermined measurements. Multiple shadow-gram images at different points along the edge of the stacked wafer can be aggregated and analyzed.

A system for vector extraction comprising a vector extraction engine stored and operating on a network-connected computing device that loads raster images from a database stored and operating on a network-connected computing device, identifies features in the raster images, and computes a vector based on the features, and methods for feature and vector extraction.

A method for detecting collisions in a video is provided. In the method, first bounding boxes of dynamic virtual elements are acquired, wherein the dynamic virtual elements are added into a video picture; target contour points corresponding to an original target object in the video picture are identified, wherein the target contour points are positioned on a contour line of the original target object; one second bounding box is created based on each two adjacent target contour points of the original target object; and the collisions between the first bounding boxes and the second bounding boxes are detected. A device and a computer-readable storage medium are further provided.

Among other things, one or more travel signals are identified by analyzing one or more images and data from sensors, classifying candidate travel signals into zero, one or more true and relevant travel signals, and estimating a signal state of the classified travel signals.

An image processing apparatus comprises: a model obtaining unit configured to obtain a learned model that has learned, based on a position of a predetermined feature point, a contour of a target in an image obtained by capturing the target; an image obtaining unit configured to obtain an input image; a position obtaining unit configured to obtain a position of an input point input on the input image by a user; a normalization unit configured to obtain a normalized image generated by coordinate-transforming the input image such that the position of the input point matches the position of the predetermined feature point in the learned model; and an estimation unit configured to estimate the contour of the target in the input image using the normalized image and the learned model.

Disclosed herein is an image frame extraction apparatus that acquires a video image; extracts features of each of a plurality of image frames of the acquired video image; analyzes the extracted features of each of the plurality of image frames, and extracts candidates of a representative frame from the plurality of image frames; and, for each of the extracted candidates of the representative frame, calculates a displacement in a shape space of a pose of an object in the image frame with respect to a reference pose, and select the representative frame from the candidates of the representative frame based on the calculated displacement in the shape space.