In some implementations, a device may determine that an unknown sample is an outlier sample by using an aggregated classification model. The device may determine that one or more spectroscopic measurements are not performed accurately based on determining that the unknown sample is the outlier sample. The device may cause one or more actions based on determining the one or more spectroscopic measurements are not performed accurately.

A hybrid spectral imager apparatus has an imaging head arrangement (IHA), a control and processing unit (CPU), and a display. The IHA includes an optical imager, multiband filtering optics (MBFO), and a sensor arrangement. The optical imager collects and focuses an image of a target scene or object along an imaging path. The multiband optics includes a beam divider for generating at least two replica images of the target image, and a multiband filter (MF) interposed into the imaging path and effecting multi-bandpass filtering in the image replicas. The sensor arrangement has at least one Mosaic filter array (MFA) focal plane array (FPA) sensor onto which the multiband filtered image replicas are focused, and a focal plane array masked, in a pixelized manner, with at least three wide-band primary color-type filters, with each primary color-type response separating and capturing one single-band. The CPU is coupled to the IHA and is configured to execute program instructions for calibrating image acquisition processes, controlling and synchronizing the acquisition/capturing of the image replicas by the MFA sensor arrangement, and spectrally purifying the MFA sensor arrangement responses to compensate band cross-talking between the MFA and the MF. The display is configured to display on a user interface at least the acquired single-band images. The CPU is further configured to reconstruct and display, on the display, a set of at least three different single-band images per MFA-FPA sensor employed. The IHA is configured to capture sets of different single-band images for video snapshot spectral imaging at desired spectral bands within the FPA sensor arrangement spectral sensitivity range.

The present disclosure provides a spectral imaging chip and apparatus, an information processing method, a fingerprint living body identification device and a fingerprint module. The spectral imaging chip can obtain spectral information of a captured object without affecting the spatial resolution and imaging quality of the resulting image, which is convenient for grasping more comprehensive information of the object to be imaged. The fingerprint living body identification device and fingerprint module can realize fingerprint living body identification through the spectral imaging chip, which is advantageous to improve the stability of the component performance, while reducing the volume, weight and cost of the spectral components, greatly improving the anti-counterfeiting ability of the fingerprint identification system.

An object recognition apparatus includes a first spectrometer configured to obtain a first type of spectrum data from light scattered, emitted, or reflected from an object; a second spectrometer configured to obtain a second type of spectrum data from the light scattered, emitted, or reflected from the object, the second type of spectrum data being different from the first type of spectrum data; an image sensor configured to obtain image data of the object; and a processor configured to identify the object using data obtained from at least two from among the first spectrometer, the second spectrometer, and the image sensor and using at least two pattern recognition algorithms.

A non-contact system for the sensing of pH includes a hyperspectral imaging device configured to capture a hyperspectral image of a fluid, a flow cell configured to enable the capturing of a hyperspectral image of a fluid, a process, and a memory. The memory includes instructions stored thereon, which, when executed by the processor, cause the system to generate a hyperspectral image of the fluid in the flow cell, generate several spectral signals based on the hyperspectral image, provide the spectral signal as an input to a machine learning network, and predict by the machine learning network a pH of a fluid.

Provided is an optical system which may acquire a hyperspectral image by acquiring a spectral image of an object to be measured, which includes, to collect spectral data and train the neural network, an image forming part forming an image from an object to be measured and transmitting collimated light, a slit moving to scan the incident image and passing and outputting a part of the formed image, and a first optical part obtaining spectral data by splitting light of the image received through the slit by wavelength. Also, the system includes, to decompose overlapped spectral data and to infer hyperspectral image data through the trained neural network, an image forming part forming an image from an object to be measured and transmitting collimated light, and a first optical part obtaining spectral data by splitting light of the received image by wavelength.

A spectrometry method used with an apparatus including a spectrometry section, a spectroscopic controller, a spectroscopic image generator, and a display section, the method including generating a teaching-purpose spectroscopic image, generating and displaying a teaching-purpose visualized image, identifying a first teaching area in the teaching-purpose visualized image and generating a first teaching-purpose spectrum, displaying a first icon based on the display color of the first teaching area, accepting teacher data that teaches chromaticity in correspondence with the first icon, generating a conversion rule based on the relationship between the spectrum and the teacher data, generating a measurement-purpose spectrum, and calculating chromaticity based on the conversion rule.

An image capturing device (202) can include a sensor array (210), a lens (230) positioned at a first distance from an intermediate image (235), and apolychromat (220) positioned at a second distance from the lens (230). The polychromat (220) can diffract the intermediate image (235) according to a transform function (207) to produce a dispersed sensor image (215) onto the sensor array (210). The dispersed sensor image (215) can represent a spatial code of the intermediate image (235).

An object is to provide a filter learning device capable of optimizing recognition processing using features obtained from the characteristics of light. The filter learning device (10) according to the present disclosure includes an optical filter unit (11) for extracting a filter image from an image for learning by using a filter condition determined according to a filter parameter, a parameter updating unit (12) for updating the filter parameter with a result obtained by executing image analysis processing on the filter image, and a sensing unit (13) for sensing an input image by using a physical optical filter that satisfies a filter condition determined according to the updated filter parameter.

The disclosed embodiments include thin film multivariate optical element and detector combinations, thin film optical detectors, and downhole optical computing systems. In one embodiment, a thin film multivariate optical element and detector combination includes at least one layer of multivariate optical element having patterns that manipulate at least one spectrum of optical signals. The thin film multivariate optical element and detector combination also includes at least one layer of detector film that converts optical signals into electrical signals. The thin film optical detector further includes a substrate. The at least one layer of multivariate optical element and the at least one layer of detector film are deposited on the substrate.