A system to perform remote oculography includes light-occluding goggles configured to be worn by a patient. The light-occluding goggles include an infrared camera positioned to capture one or more first images of a first eye of the patient. The light-occluding goggles also include a display positioned such that it is viewable by a second eye of the patient. The display is configured to display a pattern for the patient to view. The light-occluding goggles also include a sensor configured to detect information regarding a position of a head of the patient. The system also includes a visible light camera configured to capture one or more second images of the patient as the patient wears the light-occluding goggles.

The present disclosure discloses a remote sensing-based extraction method for a type of a river channel, including: obtaining multi-source remote sensing data of a target area including a river channel; preprocessing the multi-source remote sensing data, and obtaining corresponding reflectance data; according to the reflectance data, analyzing a water index and a vegetation index of the target area; and according to the water index and the vegetation index, constructing first preset conditions for determining whether there is water in the river channel and second preset conditions for determining whether the river channel is a non-dry river channel to determine the type of the river channel. Types of river channels can be divided into four types: non-dry river channels, seasonal dry river channels, temporary water channels, and dry river channels.

An image acquisition apparatus includes: a multispectral image sensor configured to acquire images in at least four channels based on a second wavelength band of about 10 nm to about 1,000 nm; and a processor configured to estimate illumination information of the images by inputting the images of at least four channels to a deep learning network trained in advance, and convert colors of the acquired images using the estimated illumination information.

An image acquisition apparatus includes: a multispectral image sensor configured to acquire images in at least four channels based on a second wavelength band of about 10 nm to about 1,000 nm; and a processor configured to estimate illumination information of the images by inputting the images of at least four channels to a deep learning network trained in advance, and convert colors of the acquired images using the estimated illumination information.

An optical fingerprint recognition device includes a light-emitting diode (LED) array and a fingerprint sensing device. The LED array includes a central LED area and an edge LED area, and configured to display a light source pattern in response to a fingerprint sensing request. The light source pattern includes a central portion and a surrounding portion. During displaying the light source pattern, a plurality of red display subpixels of the central LED area are not illuminating and a plurality of red display subpixels of the edge LED area are illuminating. The fingerprint sensing device generates a first fingerprint image according to a plurality of first sensing signals obtained from a plurality of first sensing pixel area, and the first fingerprint image is adapted to be used for examining whether a finger which triggers the fingerprint sensing request is real or fake.

Methods for determining a value of an intrinsic geometrical parameter of a geometrical feature characterizing a physical object, and for classifying a scene into at least one geometrical shape, each geometrical shape modeling a luminous object. A singular light distribution characterized by a first wavelength and a position of singularity is projected onto the physical object. Light excited by the singular light distribution that has interacted with the geometrical feature and that impinges upon a detector is detected and a return energy distribution is identified and quantified at one or more positions. A deep learning or neural network layer may be employed, using the detected light as direct input of the neural network layer, adapted to classify the scene, as a plurality of shapes, static or dynamic, the shapes being part of a set of shapes predetermined or acquired by learning.

An image processing apparatus includes an acquiring unit and an assigning unit. The acquiring unit is configured to acquire first image information on a first image and second image information on a second image read by irradiating, with light at respective different wavelengths for read, a printing medium on which at least the first image and the second image to be irradiated with light at different wave lengths to read are mixed. The assigning unit is configured to assign image information on one or more color systems constituting the first image information and image information on one or more color systems constituting the second image information to any of communication paths for supplying image information to a subsequent stage image processing unit configured to perform predetermined image processing on the first image information and the second image information.

An image processing apparatus includes an acquiring unit and an assigning unit. The acquiring unit is configured to acquire first image information on a first image and second image information on a second image read by irradiating, with light at respective different wavelengths for read, a printing medium on which at least the first image and the second image to be irradiated with light at different wave lengths to read are mixed. The assigning unit is configured to assign image information on one or more color systems constituting the first image information and image information on one or more color systems constituting the second image information to any of communication paths for supplying image information to a subsequent stage image processing unit configured to perform predetermined image processing on the first image information and the second image information.

A fraud confirmation assisting apparatus includes a light source, a reading sensor, and processing circuitry. The light source irradiates an object to be read with light in at least an invisible wavelength range. The reading sensor has sensitivity at least in the invisible wavelength range. The processing circuitry performs a reading operation on the object to be read by a combination of the light source and the reading sensor, and outputs a plurality of pieces of fraud confirmation information based on read information output from the reading sensor by the reading operation.

A fraud confirmation assisting apparatus includes a light source, a reading sensor, and processing circuitry. The light source irradiates an object to be read with light in at least an invisible wavelength range. The reading sensor has sensitivity at least in the invisible wavelength range. The processing circuitry performs a reading operation on the object to be read by a combination of the light source and the reading sensor, and outputs a plurality of pieces of fraud confirmation information based on read information output from the reading sensor by the reading operation.