Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for image segmentation and chemical analysis using machine learning. In some implementations, a system obtains a hyperspectral image that includes a representation of an object. The system segments the hyperspectral image to identify regions of a particular type on the object. The system generates a set of feature values derived from image data for different wavelength bands that is located in the hyperspectral image in the identified regions of the particular type. The system generates a prediction of a level of one or more chemicals in the object based on an output produced by a machine learning model in response to the set of feature values being provided as input to the machine learning model. The system provides data indicating the prediction of the level of the one or more chemicals in the object.

This application provides a method for detecting images of testing object using hyperspectral imaging. Firstly, obtaining a hyperspectral imaging information according to a reference image, hereby, obtaining corresponded hyperspectral image from an input image and obtaining corresponded feature values for operating Principal components analysis to simplify feature values. Then, obtaining feature images by Convolution kernel, and then positioning an image of an object under detected by a default box and a boundary box from the feature image. By Comparing with the esophageal cancer sample image, the image of the object under detected is classifying to an esophageal cancer image or a non-esophageal cancer image. Thus, detecting an input image from the image capturing device by the convolutional neural network to judge if the input image is the esophageal cancer image for helping the doctor to interpret the image of the object under detected.

A computer-implemented system and method of image cross-correlation improves the sub-pixel accuracy of the correlation surface and subsequent processing thereof. One or both of the template or search windows are resampled using the fractional portions of the correlation offsets X and Y produced by the initial image cross-correlation. The resampled window is then correlated with the other original window to produce a resampled cross-correlation surface. Removing the fractional or sub-pixel offsets between the template and search windows improves the “sameness” of the represented imagery thereby improving the quality and accuracy of the correlation surface, which in turn improves the quality and accuracy of the FOM or other processing of the correlation surface. The process may be iterated to improve accuracy or modified to generate resampled cross-correlation surfaces for multiple possible offsets and to accept the one with the most certainty.

Methods for calculating a MetaKG signal are provided. The method including illuminating a region of interest in a sample with a near-infrared (NIR) light source and/or a visible light source. The region of interest includes a sample portion and background portion, each having a different set of optical characteristics. Images of the region of interest are acquired and processed to obtain metadata associated with the acquired images. MetaKG signals are calculated for the region of interest and for the background. The MetaKG signal for the background is used to adjust the MetaKG signal for the region of interest to provide a final adjusted MetaKG signal for the region of interest.

The invention relates to a method for online quality control of decorative prints on substrate materials, including similarity comparisons of actual and target images and adjusting decorative prints if deviations of color values are detected. The method may include the steps of: a) producing a hyperspectral digital image of a print decoration; b) calibrating the print decoration via a hyperspectral digital image; c) producing and storing a digital target image of the print decoration; d) creating a first print decoration on a first substrate material; e) producing and storing a digital actual image of the printed decoration on the first substrate material; f) determining color deviations between the digital target image and the digital actual image via a computer program; and g) printing on at least one side of substrate materials so as to form a decorative layer. The invention also relates to a device for carrying out the method.

The present invention relates to the technical field of field crop cultivation, more particularly to a training method, an evaluation method, an electronic device and a storage medium. According to the present invention, a multispectral three-dimensional point cloud map is obtained through depth information and multispectral information, and the multispectral three-dimensional point cloud map is analyzed by utilizing an FVNet three-dimensional target detection algorithm, thereby acquiring crop feature information. Thus, more comprehensive crop information can be obtained, and a crop state evaluation model constructed based on an artificial neural network can be further trained with the crop feature information.

A method for optimizing image data for generating orthorectified image(s) related to an area of interest in an environment. The method includes receiving a first image dataset of the area of interest captured therein, identifying each of multiple objects in the area of interest, receiving attribute information related to each of the multiple identified objects, determining if one or more of the multiple identified objects satisfy at least one of a risk criteria based on the attribute information therefor, identifying a maximum relevant second area including at least the area of interest and each of the one or more of the multiple identified objects satisfying the at least one of risk criteria, and processing the first image dataset to either discard or down-sample areas other than the maximum relevant second area captured therein.

A hyperspectral infrared imaging system includes optical components, multi-color focal plane array or arrays, readout electronics, control electronics, and a computing system. The system measures a limited number of spatial and spectral points during image capture and the full dataset is computationally generated.

A system and method are disclosed for design of a suite of multispectral (MS) sensors and processing of enhanced data streams produced by the sensors for autonomous aircraft flight. The onboard suite of MS sensors is specifically configured to sense and use a MS variety of sensor-tuned objects, either strategically placed objects and/or surveyed and sensor significant existing objects to determine a position and verify position accuracy. The received MS sensor data enables an autonomous aircraft object identification and positioning system to correlate MS sensor data output with a-priori information stored onboard to determine and verify position and trajectory of the autonomous aircraft. Once position and trajectory are known, the object identification and positioning system commands the autonomous aircraft flight management system and autopilot control of the autonomous aircraft.

A hyperspectral stereoscopic CubeSat with computer vision and artificial intelligence capabilities consists of a device and a data processing methodology. The device comprises a number of VIS-NIR-TIR hyperspectral sensors, a central processor with memory, a supervisor system running independently of the imager system, radios, a solar panel and battery system, and an active attitude control system. The device is launched into low earth orbit to capture, process, and transmit stereoscopic hyperspectral imagery in the visible and infrared portions of the electromagnetic spectrum. The processing methodology therein comprises computer vision and convolutional neural network algorithms to perform spectral feature identification and data transformations.