Disclosed is a method for diagnosing a neurodegenerative disease in a subject. The method comprises obtaining from the subject a sample comprising at least one live blood cell, and optionally isolating at least one live blood cell from the sample. The method further comprises generating one or more multispectral or hyperspectral images of the at least one cell, and analysing spectral characteristics of autofluorescence from the at least one cell. Also disclosed is a system configured to aid in the detection or diagnosis of a neurodegenerative disease. Also disclosed is a method for selecting a subject for treatment for a neurodegenerative disease. Also disclosed is a method for monitoring the response of a subject to a therapeutic treatment for a neurodegenerative disease. Also disclosed is a protocol for monitoring the efficacy of a therapeutic treatment for a neurodegenerative disease.

A system and method of analysing drilling cuttings using image data output from a hyperspectral imaging device and at least one optical camera, includes generating a hyperspectral imaging data set including a plurality of lines of hyperspectral data derived from line images taken by the hyperspectral imaging device positioned along a drilling fluid cuttings path, obtaining tracking information in respect of particles of interest from the output of the at least one optical camera, correcting the position of pixels associated with particles of interest in the plurality of lines of hyperspectral imaging data based on the obtained tracking information to generate corrected hyperspectral imaging data, and analysing the corrected hyperspectral imaging data to characterise the cuttings.

A system consists of a front group, a slit member, and a rear group arranged in order from an object side to an image side, wherein the slit member is provided with an aperture long in a first direction, wherein the front group does not form an image of an object on the aperture in a first cross section parallel to the first direction, and forms an intermediate image of the object on the aperture in a second cross section perpendicular to the first direction, wherein the rear group includes a diffractive surface configured to collects a plurality of rays at positions different from each other in the second cross section, wherein the front group includes a first reflective surface and a second reflective surface that satisfies a predetermined inequality.

A ground material density measurement system is disclosed. The ground material density measurement system may receive a moisture measurement associated with an amount of moisture on a ground surface of a section of ground material. The ground material density measurement system may determine a GPR measurement associated with the section of ground material. The ground material density measurement system may process the GPR measurement based on the moisture measurement to account for the amount of moisture. The ground material density measurement system may provide density information associated with the section of ground material based on the processed GPR measurement.

A method and system may survey a property using aerial images captured from an unmanned aerial vehicle (UAV), a manned aerial vehicle (MAV) or from a satellite device. The method may include identifying a commercial property for a UAV to perform surveillance, and directing the UAV to hover over the commercial property and capture aerial images at predetermined time intervals. Furthermore, the method may include receiving the aerial images of the commercial property captured at the predetermined time intervals, detecting a surveillance event at the commercial property, generating a surveillance alert, and transmitting the surveillance alert to an electronic device associated with an owner of the commercial property.

Various embodiments relate generally to computer vision and automation to autonomously identify and deliver for application a treatment to an object among other objects, data science and data analysis, including machine learning, deep learning, and other disciplines of computer-based artificial intelligence to facilitate identification and treatment of objects, and robotics and mobility technologies to navigate a delivery system, more specifically, to an agricultural delivery system configured to identify and apply, for example, an agricultural treatment to an identified agricultural object. In some examples, a method may include receiving data representing an image, detecting a portion of the image associated with a unit of spatial translation relative to a reference, identifying a subset of pixels to be formed on the surface, and causing emission of a subset of pixel projectiles directed to impact a portion of a surface to form a replica of a portion of the image.

A signal processing method is performed by a computer. The signal processing method includes: obtaining first compressed image data including hyperspectral information and indicating a two-dimensional image in which the hyperspectral information is compressed, the hyperspectral information being luminance information on each of at least four wavelength bands included in a target wavelength range; extracting partial image data from the first compressed image data; and generating first two-dimensional image data corresponding to a first wavelength band and second two-dimensional image data corresponding to a second wavelength band from the partial image data.

Systems, methods and apparatus for image processing for reconstructing a super resolution image from multispectral (MS) images. Receive image data and initialize a fused image using a panchromatic (PAN) image, and estimate a blur kernel between the PAN image and the MS images as an initialization function. Iteratively, fuse a MS image with an associated PAN image of a scene using a fusing algorithm. Each iteration includes: update the blur kernel based on a Second-Order Total Generalized Variation function to regularize a kernel shape; fuse the PAN image and MS images with the updated blur kernel based on a local Laplacian prior function to regularize the high-resolution information to obtain an estimated fused image; compute a relative error between the estimated fused image of the current iteration and a previous estimated fused image from a previous iteration, to a predetermined threshold, to stop iterations stop, to obtain a PAN-sharpened image.

According to some embodiments, a system, method and non-transitory computer-readable medium are provided comprising an image data source storing image data from a plurality of images; a height map source storing height maps for an area of interest (AOI); a building footprint module; a memory; and a building footprint processor, operative to execute the program instructions to: receive image data for an AOI; receive a height map for the AOI; execute a building segmentation module to generate a building mask that indicates a presence of one or more buildings in the AOI; apply at least one clean mask process to the generated building mask to generate a clean mask; receive the clean mask at an instance building segmentation module; and execute the instance building segmentation module to generate at least one building footprint based on the clean mask and the received image data. Numerous other aspects are provided.

In one aspect, a method of hyperspectral image correction includes the step of generating one or more lookup tables with a radiative transfer model for converting an at-sensor digital number image from a hyperspectral satellite to a bottom of atmosphere radiance and reflectance value image. The intermediate method includes conversion of at-sensor image DN values to TOA radiance and then to TOA reflectance. Later, the method include creating a pre-classification layer using the TOA reflectance image to mask the TOA radiance image. Further, the method includes performing aerosol correction on the masked at-sensors radiance image by applying a pixel-wise albedo estimation using the one or more lookup tables to generate an aerosol corrected radiance image. The method includes performing a water vapor correction on the aerosol corrected radiance image to generate a BOA radiance image. At last, the method includes converting the BOA radiance image to a BOA reflectance.