Abstract: A texture recognition device and an operation method of a texture recognition device are provided. The texture recognition device includes a light source array and an image sensor array. The light source array includes a plurality of light sources; the image sensor array is at a side of the light source array and includes a plurality of image sensors, and the plurality of image sensors are configured to receive light emitted from the plurality of light sources and reflected to the image sensors by a texture for a texture image collection; each of the image sensors includes a plurality of signal switches, and a signal of each of the image sensors is read through the plurality of signal switches for forming one image pixel of the texture image.

Abstract: A sensor pixel according to a feature of the present invention includes: a detection electrode that forms capacitance with a recognition target; and a sensor pixel circuit that is connected to the detection electrode, generates a detection signal by using the detection electrode, and to which a DC voltage for resetting is supplied, wherein a coupling pulse is periodically applied to the recognition target that forms the capacitance with the detection electrode, and the detection signal may be changed according to the coupling pulse.

Abstract: An optical fingerprint sensing apparatus is provided. A control circuit controls a first selection circuit to output at least one of fingerprint sensing signals on sensing signal lines, and controls a second selection circuit to output at least one of background noise signals on dummy sensing signal lines, so that the first selection circuit and the second selection circuit simultaneously output the fingerprint sensing signal and the background noise signal, respectively. The signal processing circuit cancels background noise of the fingerprint sensing signals according to the background noise signal.

Abstract: An image analyzer repeatedly obtains a captured image including a subject to be detected, and detects, in the captured image, a first area likely to include the subject without a cover, detects, in the captured image, a second area likely to include the subject with a cover, and determines whether the subject is covered by the cover based on a detection result of the first area and a detection result of the second area and a determination result for the captured image obtained previously.

Abstract: A biometric identification apparatus, an unlocking method of biometric identification, and a terminal device are provided. The biometric identification apparatus includes: a fingerprint receiving module, configured to sense approaching of a finger to generate a triggering instruction and collect a fingerprint pattern of a user based on the triggering instruction; and a pulse receiving module, coupled with the fingerprint receiving module and configured to receive the triggering instruction and collect pulse information of the user based on the triggering instruction.

Abstract: The invention relates to a device for the biometric acquisition and processing of an image of a part of the human body with dermatoglyphs, comprising a contact surface (3) configured so that the part of the human body is affixed to this contact surface (3), a light source (4) configured to project onto the part of the human body a light pattern having a sinusoidal modulation of light intensity in a main direction with a target frequency, an imager (2) configured to acquire an image, and an automated data processing system (7) configured to implement a fraud detection method and a biometric identity recognition method using a periodic component parameter representative of an amplitude of a sinusoidal oscillation of light intensity in the acquired image in accordance with the main direction at the target frequency.

Abstract: According to one embodiment, an inspection device includes a stage on which a substrate having a protrusion portion on a surface thereof is mountable. A ring member presses an outer periphery of the substrate on the stage. A liquid supply unit supplies a liquid on the surface of the substrate from the surface thereof to a first height. An imaging unit captures an image of a surface of the liquid and the protrusion portion from above the surface of the substrate. An arithmetic operation unit determines a size of an exposed portion of the protrusion portion which is exposed from the surface of the liquid by using the image obtained from the imaging unit, and determines a height of the protrusion portion on the basis of the size of the exposed portion.

Abstract: An image processing device includes multiple image processing units, each trained to accommodate a different feature possibly contained in an image, a decision unit that decides a sequence of the multiple image processing units according to the features contained in an input image, and an application unit that applies the image processing units to the input image in the sequence decided by the decision unit.

Abstract: The disclosure relates to a user equipment (UE) and a method and apparatus for identifying a handprint. The user equipment includes a housing including a front housing, a rear housing, and a middle frame located between the front housing and the rear housing; at least one ultrasound handprint sensor located on an inner wall of the middle frame, and configured to emit an ultrasound capable of passing through the middle frame, receive an ultrasound reflected by a hand of a user, and generate an electric signal of handprint information based on the ultrasound reflected by the hand of the user; and a controller located inside the housing, electrically connected to the at least one ultrasound handprint sensor, and configured to control the at least one ultrasound handprint sensor, and convert the electric signal of the handprint information collected by the at least one ultrasound handprint sensor into a handprint image.

Abstract: In one aspect, a sensor includes an image acquisition controller and a pixel array. The pixel array includes a first set of pixels electrically coupled to the controller and a second set of pixels electrically coupled to the controller. The sensor is configured to operate in a first mode and a second mode. When operating in the first mode, the controller is configured to acquire signals from only the first set of pixels for generating a low-resolution image. When operating in the second mode, the controller is configured to acquire signals from both the first set of pixels and the second set of pixels for generating a high-resolution image.

Abstract: An extraneous-matter detecting apparatus includes a region setting unit, a computing unit, a change-value setting unit, an updating unit, and a detection unit. The region setting unit sets divided regions for a captured image captured by an image capturing device. The computing unit computes, for a corresponding divided region, a first difference between a past representative value of luminance and a present representative value of luminance and a second difference between a past dispersion amount of luminance and a present dispersion amount of luminance. The change-value setting unit sets, based on the first and the second differences, a change value of a counter value that indicates an adhesion state of an extraneous matter in the divided region. The updating unit updates the counter value based on the change value. The detection unit detects, based on the counter value, an adhesion state of an extraneous matter in the divided region.

Abstract: According to the embodiments of the present application, there are proposed a method and electronic device for retrieving an image, and computer readable storage medium. The method includes: processing an image to be retrieved using a first neural network to determine a local feature vector of the image to be retrieved; processing the image to be retrieved using a second neural network to determine a global feature vector of the image to be retrieved; and determining, based on the local feature vector and the global feature vector, an image having a similarity to the image to be retrieved which is higher than a similarity threshold.

Abstract: A computer-implemented method for generating a training set of images and labels for a native environment includes receiving physical coordinate sets, retrieving environmental model data corresponding to a georeferenced model of the environment, and creating a plurality of two-dimensional (2-D) rendered images each corresponding to a view from one of the physical coordinate sets. The 2-D rendered images include one or more of the environmental features. The method also includes generating linking data associating each of the 2-D rendered images with (i) labels for the one or more included environmental features and (ii) a corresponding native image. Additionally, the method includes storing the training set including the 2-D rendered images, labels, corresponding native images, and linking data.

Abstract: A lane line processing method and device are provided. The method can includes obtaining distances between lane line points in a first image, determining direction densities of the individual lane line points by using the distances between the lane line points, dividing lane line points into groups corresponding to lane lines by using the direction densities of the individual lane line points, and obtaining representation information of the lane lines corresponding to the groups by using the lane line points in the groups. By using the direction densities of the individual lane line points, lane line points in an image are divided into groups, the obtained groups are then more accurate, and thus the lane lines obtained by fitting are accurate, are less susceptible to image quality issues, and have high robustness.

Abstract: An information processing device according to an embodiment includes a determination unit and a first training unit. The determination unit determines whether an unlabeled data point whose class label is unknown is a non-targeted data point that is not targeted for pattern recognition. The first training unit trains a first classifier for use in the pattern recognition through semi-supervised learning using a first training dataset including unlabeled data determined not to be the non-targeted data and not including unlabeled data determined to be the non-targeted data.

Abstract: This application discloses a method and an apparatus for determining a posture of a target object in an image, a device, and a non-transitory storage medium. In the method, a training model parameter of a convolutional neural network of the target object is obtained from a server. A real-time image of the target object is obtained. At least one first image block from the real-time image is identified. The at least one first image block is a local image of the real-time image. According to the training model parameter, a label image block matching the at least one first image block is determined. The label image block is a local image of a standard image of the target object. Furthermore, the posture of the target object is determined, by processing circuitry of a terminal device, according to the at least one first image block and the determined label image block.

Abstract: Computer-implemented systems and methods for video content analysis are described herein. A video feed comprising at least one image captured by a camera is received. The at least one image has a plurality of regions associated with a different level of precision required for each region. An adjustable image processing algorithm is applied to each region of the at least one image to obtain for each region the different level of precision. The image processing algorithm is adjusted based of the different level of precision associated with each region. Meta data indicative of content of the video feed is generating by the image processing algorithm.

Abstract: Optical sensing is provided to detect two-dimensional spoof objects using bright-dark reversal imaging. For example, embodiments can operate in context of an under-display optical fingerprint sensor integrated into an electronic device, such as a smartphone. An optical scanning system is configured so that direct illumination incident on a defined sensing region is redirected, by internal specular reflection off of a contact surface, onto an optical sensor in a bright-dark pattern corresponding to the pattern of contact an object and the contact surface. Masking of direct illumination in a certain region of the contact surface inhibits specular reflection in that region, such that optical information is directed to the optical sensor from that region only by non-specular reflection. Three-dimensional (real) and two-dimensional (spoof) objects tend to manifest different optical responses to the lack of specular reflection in the masked region, which can be exploited to detect such spoofs.

Abstract: An extraneous-matter detecting apparatus according to an embodiment includes a region setting unit, a computing unit, and a state setting unit. The region setting unit sets, for a captured image captured by an image capturing device provided in a vehicle, a plurality of divided regions. The computing unit computes a representative value of luminance in a target region for detecting an adhesion of an extraneous matter. The target region includes the plurality of divided regions. The state setting unit sets, based on a vehicle speed and the representative value of luminance in the target region, a transition state of information that indicates an adhesion state of an extraneous matter in the divided region.

Abstract: Provided are an apparatus and a method for converting an image. The apparatus includes an image sensor configured to obtain an image of a road, and a processor. The processor is configured to segment an input image into a plurality of region images, determine a vanishing point corresponding to each of the plurality of region images, obtain a translation relation for converting two-dimensional (2D) coordinates of a point in a region image among the plurality of region images into three-dimensional (3D) coordinates, based on a vanishing point of the region image, and generate road profile data based on translation relations of the plurality of region images.