The present disclosure provides an optical fingerprint identification unit, a display panel, a method for manufacturing an optical fingerprint identification unit and a method for identifying a fingerprint. The optical fingerprint identification unit includes a substrate, and a light source, a photoluminescent layer and a light sensor above the substrate. The light source is configured to emit visible light. The photoluminescent layer is configured to receive the visible light reflected by a fingerprint and convert the received visible light into non-visible light. The light sensor is configured to detect the non-visible light.

Provided herein are a method of extracting a feature from an image using a laser pattern and an identification device and a robot including the same, and the identification device for extracting a feature from an image using a laser pattern, which includes a first camera coupled to a laser filter and configured to generate a first image including a pattern of a laser which is reflected from an object, a second camera configured to capture an area overlapping an area captured by the first camera to generate a second image, and a controller configured to generate a mask for distinguishing an effective area using the pattern included in the first image and extract a feature from the second image by applying the mask to the second image.

An apparatus to detect the presence of a user includes, in one embodiment, an infrared sensor that passively detects infrared light radiated from an observation target, a heat source arranged proximate the infrared sensor, and a determinator that determines whether the observation target is present in response to information changing along with an operational situation of the heat source and infrared data detected by the infrared sensor. A method and a computer program product also perform functions of the apparatus.

A system and method for automatically (without human intervention) identifying a material in an image that comprises a building material for buildings in the image. Building side polygons which may be used to identify building sides in off-nadir imagery are generated. Off-nadir, multispectral images, building footprint data and elevation data for a geographic area are taken as input. Building heights for buildings in the geographic area are determined by clipping the elevation data using the building footprint data and then calculating building heights. A candidate set of polygons representing visible side faces of each building in the images is created from the known building heights, and based on the viewpoint direction, using vector analysis. After culling occluded polygons and polygons too small for analysis, the polygons are associated with a building footprint. Building materials for each building having visible polygons can then be identified.

In one embodiment, a method of machine learning and/or image processing for spectral object classification is described. In another embodiment, a device is described for using spectral object classification. Other embodiments are likewise described.

In one embodiment, a method of machine learning and/or image processing for spectral object classification is described. In another embodiment, a device is described for using spectral object classification. Other embodiments are likewise described.

The present invention discloses a multispectral stereo camera self-calibration algorithm based on track feature registration, and belongs to the field of image processing and computer vision. Optimal matching points are obtained by extracting and matching motion tracks of objects, and external parameters are corrected accordingly. Compared with an ordinary method, the present invention uses the tracks of moving objects as the features required for self-calibration. The advantage of using the tracks is good cross-modal robustness. In addition, direct matching of the tracks also saves the steps of extraction and matching the feature points, thereby achieving the advantages of simple operation and accurate results.

In an embodiment, digital images of agricultural fields are received at an agricultural intelligence processing system. Each digital image includes a set of pixels having pixel values, and each pixel value of a pixel includes a plurality of spectral band intensity values. Each spectral band intensity value describes a spectral band intensity of one band among several bands of electromagnetic radiation. For each of the agricultural fields, spectral band intensity values of each band are preprocessed at a field level using the digital images for that agricultural field resulting in preprocessed intensity values. The preprocessed intensity values are provided as input to a machine learning model. The model generates a predicted yield value for each field. The predicted yield value is used to update field yield maps of agricultural fields for forecasting and can be displayed via a graphical user interface (GUI) of a client computing device.

A processor device includes an image signal acquisition unit, an image processing unit, a display control unit, an image storage control unit, and a user input reception unit. The image processing unit detects a region-of-interest, superimposes and displays a highlight display, and performs a setting change from a first highlight level setting value to a second highlight level setting value for changing a shape of the highlight display to be different. The second highlight level setting value is stored together with an endoscopic image.

Embodiments of the present application disclose a fingerprint identification apparatus and an electronic device. The fingerprint identification apparatus is used to be disposed under a display screen and includes a first filter layer and a fingerprint sensor, and the first filter layer is disposed above the fingerprint sensor, the first filter layer includes a plurality of first filter units, and the plurality of first filter units are disposed in a region of the first filter layer corresponding to an edge region of the fingerprint sensor; sensing units of the edge region of the fingerprint sensor are configured to receive a light signal returned by a finger above the display screen and filtered by the plurality of first filter units; and sensing units of a middle region of the fingerprint sensor are configured to receive a light signal returned by the finger, to generate a fingerprint image of the finger.