An infrared sensing gesture recognition method, characterized in that the recognition method adopts multiple infrared light emitting units, and each infrared light emitting unit is loaded with pulse signals on different frequencies, then, one or more infrared receiving units for receiving signals are used to receive infrared reflected signals loaded with corresponding frequencies emitted from different infrared light emitting units, and the infrared reflected signal corresponds to the infrared light emitting unit with the corresponding frequency by identifying the frequency of pulse signal loaded in the infrared reflected signal, so as to determine the physical location of infrared light emitting unit, and to judge the movement trajectory of gesture.

The present disclosure relates to an advanced driving assistance system (ADAS), and more particularly, to a camera system for an ADAS. According to the present disclosure, there are provided a voltage logic and a memory logic that may be used in a front-view camera system for an ADAS. According to the present disclosure, there is also provided a scheme capable of coupling a lens barrel and a lens holder in a front-view camera system for an ADAS. The camera system according to the present disclosure includes a lens configured to capture a region ahead of a vehicle, a lens barrel configured to accommodate the lens in an internal to space thereof, a lens holder coupled to the lens barrel, an image sensor configured to sense an image captured by the lens, an image processor configured to receive image data from the image sensor and process the received image data, and a camera micro-control unit (MCU) configured to communicate with the image processor and receive the data processed by the image processor.

In some arrangements, product packaging is digitally watermarked over most of its extent to facilitate high-throughput item identification at retail checkouts. Imagery captured by conventional or plenoptic cameras can be processed (e.g., by GPUs) to derive several different perspective-transformed views—further minimizing the need to manually reposition items for identification. Crinkles and other deformations in product packaging can be optically sensed, allowing such surfaces to be virtually flattened to aid identification. Piles of items can be 3D-modelled and virtually segmented into geometric primitives to aid identification, and to discover locations of obscured items. Other data (e.g., including data from sensors in aisles, shelves and carts, and gaze tracking for clues about visual saliency) can be used in assessing identification hypotheses about an item. Logos may be identified and used—or ignored—in product identification. A great variety of other features and arrangements are also detailed.

There is provided an image capturing apparatus. A live view shooting unit iteratively captures live view images in accordance with a shooting parameter for live view shooting. A main shooting unit performs main shooting in accordance with a shooting parameter for main shooting. A training unit performs, after the main shooting has been performed, training of a training model that outputs a shooting parameter in accordance with an input image, based on the shooting parameter for the main shooting and a first live view image captured by the live view shooting unit.

A vehicle includes a camera unit disposed in the vehicle to have a plurality of channels and configured to obtain an image around the vehicle, the camera unit including one or more cameras, a sensing device including an ultrasonic sensor, the sensing device configured to obtain distance information between an object and the vehicle, and a controller configured to match a part of the image around the vehicle with at least one mask, form map information based on the at least one mask and the distance information, determine at least one control point based on the map information, and obtain the image around the vehicle based on a priority of the camera unit corresponding to a surrounding type of the vehicle determined based on the control point.

A fingerprint sensing system. The fingerprint sensing system includes: at least one sensor; at least one display device; at least one application processor; and at least one secure enclave processor. The application processor(s) receives fingerprint data from the sensor(s) and provides the fingerprint data to the secure enclave processor(s). The secure enclave processor(s) decodes the fingerprint data and provides a signal indicative of at least one matched node. The application processor(s), responsive to receipt of the signal indicative of the matched node(s), presents at least a portion of a synthetic fingerprint image via at least one display device corresponding to the matched node(s).

Methods and apparatuses for tracking objects comprise one or more optical sensors for capturing one or more images of a scene, wherein the one or more optical sensors capture a wide field of view and corresponding narrow field of view for the one or more images of a scene, a localization module, coupled to the one or more optical sensors for determining the location of the apparatus, and determining the location of one more objects in the one or more images based on the location of the apparatus and an augmented reality module, coupled to the localization module, for enhancing a view of the scene on a display based on the determined location of the one or more objects.

Systems and methods are disclosed to objectively identify sub-second behavioral modules in the three-dimensional (3D) video data that represents the motion of a subject. Defining behavioral modules based upon structure in the 3D video data itself—rather than using a priori definitions for what should constitute a measurable unit of action—identifies a previously-unexplored sub-second regularity that defines a timescale upon which behavior is organized, yields important information about the components and structure of behavior, offers insight into the nature of behavioral change in the subject, and enables objective discovery of subtle alterations in patterned action. The systems and methods of the invention can be applied to drug or gene therapy classification, drug or gene therapy screening, disease study including early detection of the onset of a disease, toxicology research, side-effect study, learning and memory process study, anxiety study, and analysis in consumer behavior.

A three-dimensional finger vein recognition method and system, comprising the following steps: three cameras taking finger vein images from three angles to obtain three images; constructing a three-dimensional finger model according to finger contour lines; mapping two-dimensional image textures photographed by the three cameras into the three-dimensional finger model, respectively performing different processes on an overlapping region and a non-overlapping region; obtaining a three-dimensional finger vein image; and finally, performing feature extraction and matching on the three-dimensional finger vein image, to complete recognition. The method can acquire a better finger vein recognition effect, and has a higher robustness for a plurality of postures, such as finger rotation and inclination.

An array substrate is disclosed. The array substrate may include a base substrate, gate lines and data lines intersecting the gate lines on the base substrate. The gate lines and the data lines may define a plurality of pixel regions. Each of at least some of the plurality of the pixel regions may be provided with an image sensor. The image sensor may include a sensitive element, a first electrode at one end of the sensitive element, and a second electrode at the other end of the sensitive element. The image sensor may be configured to sense light having image information.