An example embodiment includes: an extraction unit that extracts a determination object image including a side part of an outer circumference of an iris from an image including an eye; and a determination unit that determines whether or not a colored contact lens is worn based on the determination object image.

An apparatus and method for encoding objects in a camera-captured image with a deep neural network pipeline including multiple convolutional neural networks or convolutional layers. After identifying at least a portion of the camera-capture image, a first convolutional layer is applied to the at least the portion of the camera-captured image and multiple subregion representations are pooled from the output of the first convolutional layer. One or more additional convolutions are performed. At least one deconvolution is performed and concatenated with the output of one or more convolutions. One or more final convolutions are performed. The at least the portion of the camera-captured image is classified as an object category in response to an output of the one or more final convolutions.

Systems and methods for tracking the location of a non-destructive inspection (NDI) scanner using images of a target object acquired by the NDI scanner. The system includes a frame, an NDI scanner supported by the frame, a system configured to enable motorized movement of the frame, and a computer system communicatively coupled to receive sensor data from the NDI scanner and track the location of the NDI scanner. The NDI scanner includes a two-dimensional (2-D) array of sensors. Subsurface depth sensor data is repeatedly (recurrently, continually) acquired by and output from the 2-D sensor array while at different locations on a surface of the target object. The resulting 2-D scan image sequence is fed into an image processing and feature point comparison module that is configured to track the location of the scanner relative to the target object using virtual features visible in the acquired scan images.

A computer readable medium for sizing a product includes instructions, that when executed by at least one processor, cause a computing device to: retrieve from a webpage information on a product including product dimensions; present on a display of a client device a graphical button that upon access by a user activates a camera for capturing an image of an object positioned at a focal distance from the camera, the object having a surface; prompt the user to enter boundary information of an imaginary housing to be placed on the surface; generate the imaginary housing dimensions in two dimensions (2D) based on the boundary information and the focal distance; and determine whether the product fits within the imaginary housing by comparing the product dimensions against the imaginary housing dimensions.

A method for training a face recognition model includes: acquiring a plurality of first training images being uncovered face images, and acquiring a plurality of covering object images; generating a plurality of second training images by separately fusing the plurality of covering object images with the uncovered face images; and training the face recognition model by inputting the plurality of first training images and the plurality of second training images into the face recognition model.

A learning device includes: a data acquisition unit that acquires learning target data that is full-size data of a learning target; a data generation unit that divides the learning target data to generate multiple pieces of first divided data that is divided data of the learning target data, and adds, to each piece of the first divided data, first identification information for identifying a region of the first divided data in the learning target data; and a model generation unit that generates a learned model for determining an anomaly in the first divided data using first correspondence information that is a set of the first divided data and the first identification information corresponding to the first divided data.

A head-counter device (100) comprising a digital camera (1) adapted to provide a first digital image (IM1) representative of a counting zone (STR) of persons, the first image defining a first horizontal dimension (N) and a first vertical dimension (M), and a cropping module (7) configured for: analyzing the first image (IM1) and identifying a noise area (PCR) according to at least one of the following features: pixel light intensity, pixel color and/or presence of predefined patterns, cropping the noise area (PCR) from the first image (IM1) to obtain a second image (IM2) without the noise area, the noise area (PCR) being a peripheral portion of the first image having said first horizontal dimension and having a second vertical dimension (M−DSK+SM) shorter than the first vertical dimension.

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.

The present disclosure provides an electronic device. The electronic device comprises a memory circuitry, an interface circuitry, and a processor circuitry. The processor circuitry is configured to obtain first image data associated with a first image. The processor circuitry is configured to obtain a primary object based on the first image data. The processor circuitry is configured to generate one or more secondary objects based on a first augmentation operation of the primary object. The processor circuitry is configured to obtain primary background data from the first image data. The processor circuitry is configured to generate secondary background data based on a second augmentation operation of the primary background data. The processor circuitry is configured to provide a first data set by combining the primary object and/or the one or more secondary objects with the primary background data and/or the secondary background data.

A vehicle occupant moved-distance measuring device includes a center estimator, a stable area setter, a start-point setter, and a moved-distance calculator. The center estimator estimates a center of an occupant represented by a target human image from frame images captured by an image capturing device. The stable area setter sets a stable area in which the estimated center obtained from a temporary reference image is placed at the center. The start-point setter sets, as a start point, a predefined point within the stable area when the start-point setter determines that the estimated center has remained within the stable area longer than a threshold based on estimated center obtained from subsequent images. The moved-distance calculator calculates a moved distance of the occupant based on a distance between the start point and an estimated center obtained from a measurement-target image.