Abstract: Apparatus and methods of passive biometric enrollment are configured to provide an introductory experience upon activation of a computer device. The introductory experience involves a user performing movements that allow collection of passive user biometrics by a sensor of the device. The sensor may collect the passive user biometrics while the user performs the movements. The computer device may calibrate one or more features of the computer device based on the movements. The computer device may receive user credentials from the user. The computer device may store a biometric profile including the passive user biometrics in association with the user credentials.

Abstract: The image processing method includes a luminance value information obtaining step of obtaining effective radiance values from a subject, and an image generating step of generating a picture image as a set of unit regions each of which has a luminance value obtained by at least partially removing a regular reflection light component on a surface of the subject from the effective radiance values.

Abstract: A method for classifying images using a neural network is described. The method utilizes the neural network to generate a deep hash data structure. In one embodiment, the method includes receiving, at a neural network, first data corresponding to a plurality of images of a training image set. The method includes adjusting parameters of the neural network based on concurrent application at the neural network of multiple loss functions to second data corresponding to the plurality of images to generate adjusted parameters. The method includes generating a deep hash data structure based on the adjusted parameters of the neural network. The method further includes sending, via a transmitter to a device, third data corresponding to the deep hash data structure.

Abstract: Systems and methods of identifying unknown video content are described. An example method may include receiving a first fingerprint and a second fingerprint. The first fingerprint may be a color-based fingerprint derived from colors in a portion of the unknown video content, and the second fingerprint may be at least partially based on a feature other than the colors of the unknown video content. A reference database of reference fingerprints may then be queried using one of the first fingerprint or the second fingerprint to obtain a candidate group of fingerprints. The candiate group of fingerprints may then be pried using the other of the first fingerprint and the second fingerprint to identify at least one query fingerprint. The unknown video content may then be identified using the at least one query fingerprint. For example, the second fingerprint may be a luminance-based fingerprint derived from luminance in the unknown video content.

Abstract: An apparatus and a method to control manufacturing and feeding of semifinished products in a tyre building process, includes detecting at least one first image representative of a semifinished product; dividing the at least one first image into a plurality of first sub-parts, wherein each one of first sub-parts is composed of a plurality of first portions, each one associated with a respective brightness value; defining a first threshold for the brightness values; making a first comparison between the brightness values of the first portions and the first threshold; as a function of the first comparison, determining for each one of the first sub-parts a first operative parameter representative of a total area occupied in each first sub-part by the first portions associated with a brightness value smaller or greater than the first threshold; making a second comparison between each one of the first operative parameters and a second threshold; and causing the generation of a first notification signal as a functio

Abstract: A method for registration of partially-overlapped images, comprises (a) performing noise reduction and feature extraction in a reference image and an unregistered image; (b) determining a template size using a phase transition methodology for a sufficiently-sampled finite data set; (c) identifying a template region in the reference image; (d) performing a wide angle estimation of the reference image and the unregistered image; (e) performing orientation and translation of the reference image and the unregistered image; (f) performing a search space reduction of the reference image and the unregistered image; (g) performing a coarse angle estimation of the reference image and the unregistered image; (h) performing orientation of the reference image and the unregistered image of the coarse angle estimation; and (i) performing a fine angle estimation and registration of the reference image and the unregistered image.

Abstract: In a method of generating a three dimensional representation of a human body shape from a depth image of a clothed human subject is disclosed.

Abstract: Some embodiments of the invention provide a novel method for training a multi-layer node network to reliably determine depth based on a plurality of input sources (e.g., cameras, microphones, etc.) that may be arranged with deviations from an ideal alignment or placement. Some embodiments train the multi-layer network using a set of inputs generated with random misalignments incorporated into the training set. In some embodiments, the training set includes (i) a synthetically generated training set based on a three-dimensional ground truth model as it would be sensed by a sensor array from different positions and with different deviations from ideal alignment and placement, and/or (ii) a training set generated by a set of actual sensor arrays augmented with an additional sensor (e.g., additional camera or time of flight measurement device such as lidar) to collect ground truth data.

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for object detection and representation in images. In one aspect, a method includes detecting occurrences of objects of a particular type in images captured within a first duration of time, and iteratively training an image embedding function to produce as output representations of features of the input images depicting occurrences of objects of the particular type, where similar representations of features are generated for images that depict the same instance of an object of a particular type captured within a specified duration of time, and dissimilar representations of features are generated for images that depict different instances of objects of the particular type.

Abstract: A non-contact 3D measuring system is configured to provide a combined three-dimensional shape of an object to be imaged based on a frame image, which has been captured by a camera while a measuring head is being scanned in an optical axis (Z-axis) direction, and the information on the position at which the image has been acquired. The system includes: a position detector for detecting a scanning position of the measuring head while a plurality of raw images are captured; and a computer for generating an interpolation image by linear interpolation for the captured raw image using the information of the scanning position as well as generating a combined frame image using the interpolation image.

Abstract: Techniques include systems, computerized methods, and computer readable media for choosing placement of three-dimensional (3D) probes used for evaluating a 3D alignment pose of a runtime 3D image inside a 3D alignment system for estimating the pose of a trained 3D model image in that 3D runtime image. A plurality of features associated with a first plurality of points of interest from a 3D image are generated, wherein each feature includes data indicative of 3D properties of an associated point from the plurality of points of interest. A second plurality of points of interest are selected from among the first plurality of points of interest, based at least in part on the plurality of features associated with the first plurality of points of interest. Placements of a plurality of 3D probes are determined based at least in part on the second plurality of points of interest.

Abstract: The disclosure relates to an image recognition algorithm implemented by a hardware control system which operates directly on data from a compressed sensing camera. A computationally expensive image reconstruction step can be avoided, allowing faster operation and reducing the computing requirements of the system. The method may implement an algorithm that can operate at speeds comparable to an equivalent approach operating on a conventional camera's output. In addition, at high compression ratios, the algorithm can outperform approaches in which an image is first reconstructed and then classified.

Abstract: Human Computer Interfaces (HCI) may allow a user to interact with a computer via a variety of mechanisms, such as hand, head, and body gestures. Various of the disclosed embodiments allow information captured from a depth camera on an HCI system to be used to recognize such gestures. Particularly, by training a classifier using vectors having both base and extended components, more accurate classification results may be subsequently obtained. The base vector may include a leaf-based assessment of the classification results from a forest for a given depth value candidate pixel. The extended vector may include additional information, such as the leaf-based assessment of the classification results for one or more pixels related to the candidate pixel. Various embodiments employ this improved structure with various optimization methods and structure to provide more efficient in-situ operation.

Abstract: Provided herein is a control method of an electronic apparatus. The control method of an electronic apparatus includes: generating signal type information using an image data of a signal region portion of a signal lamp in a driving-related image data of a vehicle; and performing a driving-related guide of the vehicle using the generated signal type information.

Abstract: A device includes an input to sequential data associated to a face; a predictor configured to predict facial parameters; and a corrector configured to correct the predicted facial parameters on the basis of input data, the input data containing geometric measurements and other information. A related method and a related computer program are also disclosed.

Abstract: Some implementations provide a computer-implemented method that include accessing image frames from a video sequence; detecting face of a subject in each image frame from the video sequence; detecting facial landmark features in the detected face; determining whether a sufficient number of facial landmark features and image frames have been obtained; in response to determining that a sufficient number of facial landmark features and image frames have been obtained, classifying detected facial landmark features from various detected image frames; quantifying variations of the classified facial landmark features across the detected image frames; correlating the quantified variations to a pre-determined threshold; and in response to determining that the quantified variations meet the pre-determined threshold, determining that the video sequence is from a live session.

Abstract: Provided herein is a control method of an electronic apparatus. The control method of an electronic apparatus includes: generating signal type information using an image data of a signal region portion of a signal lamp in a driving-related image data of a vehicle; and performing a driving-related guide of the vehicle using the generated signal type information.

Abstract: As to N images captured at different focus positions, which are arrayed in order of focus positions, N/2 images are set as feature point extraction images so that at least one of two adjacent images becomes a feature point extraction image; corresponding points corresponding to a feature point, extracted from the feature point extraction image, are searched for from the other images; a correction parameter, with which corresponding positions of the N images are matched, is calculated from a positional relation of the feature point and the corresponding points; and, with an image whose view angle is the smallest among the N images as a reference, the other images are corrected.