Patents
Literature
Patsnap Copilot is an intelligent assistant for R&D personnel, combined with Patent DNA, to facilitate innovative research.
Patsnap Copilot

3597 results about "Spectral image" patented technology

Spectral Image. Spectral Image is a privately held, early stage medical device company focused on the development of imaging technology for biomedical use.

Multispectral or hyperspectral imaging system and method for tactical reconnaissance

A two-dimensional focal plane array (FPA) is divided into sub-arrays of rows and columns of pixels, each sub-array being responsive to light energy from a target object which has been separated by a spectral filter or other spectrum dividing element into a predetermined number of spectral bands. There is preferably one sub-array on the FPA for each predetermined spectral band. Each sub-array has its own read out channel to allow parallel and simultaneous readout of all sub-arrays of the array. The scene is scanned onto the array for simultaneous imaging of the terrain in many spectral bands. Time Delay and Integrate (TDI) techniques are used as a clocking mechanism within the sub-arrays to increase the signal to noise ratio (SNR) of the detected image. Additionally, the TDI length (i.e., number of rows of integration during the exposure) within each sub-array is adjustable to optimize and normalize the response of the photosensitive substrate to each spectral band. The array provides for parallel and simultaneous readout of each sub-array to increase the collection rate of the spectral imagery. All of these features serve to provide a substantial improvement in the area coverage of a hyperspectral imaging system while at the same time increasing the SNR of the detected spectral image.
Owner:THE BF GOODRICH CO

System for Multi- and Hyperspectral Imaging

The present invention relates to the production of instantaneous or non-instantaneous multi-band images, to be transformed into multi- or hyperspectral images, comprising light collecting means (11), an image sensor (12) with at least one two dimensional sensor array (121), and an instantaneous colour separating means (123), positioned before the image sensor array (121) in the optical path (OP) of the arrangement (1), and first uniform spectral filters (13) in the optical path (OP), with the purpose of restricting imaging to certain parts of the electromagnetic spectrum. The present invention specifically teaches that a filter unit (FU) comprising colour or spectral filter mosaics and / or uniform colour or spectral filters mounted on filter wheels (114) or displayed by transmissive displays (115), is either permanently or interchangeably positioned before the colour separating means (123) in the optical path (OP) in, or close to, converged light (B). Each colour or spectral filter mosaic consists of a multitude of homogeneous filtering regions. The transmission curves (TC) of the filtering regions of a colour or spectral filter mosaic can be partly overlapping, in addition to overlap between these transmission curves and those belonging to the filtering regions of the colour separating means (123). The transmission curves (TC) of the colour or spectral filter mosaics and the colour separating means (123) are suitably spread out in the intervals of a spectrum to be studied. The combination of the colour separating means (123) and the spectral or colour or spectral filter mosaics produces different sets of linearly independent transmission curves (TC). The multiple-filter image captured by the image sensor (12) is demosaicked by identifying and segmenting the image regions that are affected by the regions of the multiple filter mosaic, and after an optional interpolation step, a multi-band image is obtained. The resulting multi-band image is transformed into a multi- or hyperspectral image.
Owner:RP VENTURES TECH OFFICE

Graph-based semi-supervised high-spectral remote sensing image classification method

The invention relates to a graph-based semi-supervised high-spectral remote sensing image classification method. The method comprises the following steps: extracting the features of an input image; randomly sampling M points from an unlabeled sample, constructing a set S with L marked points, constructing a set R with the rest of the points; calculating K adjacent points of the points in the sets S and R in the set S by use of a class probability distance; constructing two sparse matrixes WSS and WSR by a linear representation method; using label propagation to obtain a label function F<*><S>, and calculating the label prediction function F<*><R> of the sample points in the set R to determine the labels of all the pixel points of the input image. According to the method, the adjacent points of the sample points can be calculated by use of the class probability distance, and the accurate classification of high-spectral images can be achieved by utilizing semi-supervised conduction, thus the calculation complexity is greatly reduced; in addition, the problem that the graph-based semi-supervised learning algorithm can not be used for large-scale data processing is solved, and the calculation efficiency can be improved by at least 20-50 times within the per unit time when the method provided by the invention is used, and the visual effects of the classified result graphs are good.
Owner:XIDIAN UNIV

Hyperspectral image classification method based on spectral-spatial cooperation of deep convolutional neural network

The present invention relates to a hyperspectral image classification method based on spectral-spatial cooperation of a deep convolutional neural network, which leads the conventional deep convolutional neural network applied to a two-dimensional image into the three-dimensional hyperspectral image classification problem. Firstly, the convolutional neural network is trained by using a small volume of label data, and a spectral-spatial feature of a hyperspectral image is autonomously extracted by using the network without carrying out any compression and dimensionality reduction processing; then, a support vector machine (SVM) classifier is trained by using the extracted spectral-spatial feature so as to classify an image; and finally, the trained neural network is combined with the trained classifier, the neural network extracts a spectral-spatial feature of a to-be-classified target and the classifier determines a specific category of the extracted spectral-spatial feature so as to acquire a structure (DCNN-SVM) that can autonomously extract the spectral-spatial feature of the hyperspectral image and carry out classification to the spectral-spatial feature, thereby forming a set of hyperspectral image classification method.
Owner:陕西令一盾信息技术有限公司

Multispectral or hyperspectral imaging system and method for tactical reconnaissance

A two-dimensional focal plane array (FPA) is divided into sub-arrays of rows and columns of pixels, each sub-array being responsive to light energy from a target object which has been separated by a spectral filter or other spectrum dividing element into a predetermined number of spectral bands. There is preferably one sub-array on the FPA for each predetermined spectral band. Each sub-array has its own read out channel to allow parallel and simultaneous readout of all sub-arrays of the array. The scene is scanned onto the array for simultaneous imaging of the terrain in many spectral bands. Time Delay and Integrate (TDI) techniques are used as a clocking mechanism within the sub-arrays to increase the signal to noise ratio (SNR) of the detected image. Additionally, the TDI length (i.e., number of rows of integration during the exposure) within each sub-array is adjustable to optimize and normalize the response of the photosensitive substrate to each spectral band. The array provides for parallel and simultaneous readout of each sub-array to increase the collection rate of the spectral imagery. All of these features serve to provide a substantial improvement in the area coverage of a hyperspectral imaging system while at the same time increasing the SNR of the detected spectral image.
Owner:THE BF GOODRICH CO

Apparatus for photodynamic therapy and photodetection

The present invention provides an apparatus for photodynamic therapy and fluorescence detection, in which a combined light source is provided to illuminate an object body and a multispectral fluorescence-reflectance image is provided to reproduce various and complex spectral images for an object tissue, thus performing effective photodynamic therapy for various diseases both outside and inside of the body.For this purpose, the present invention provides an apparatus for photodynamic therapy and photodetection, which provides illumination with light of various wavelengths and multispectral images, the apparatus including: an optical imaging system producing an image of an object tissue and transmitting the image to a naked eye or an imaging device; a combined light source including a plurality of coherent and non-coherent light sources and a light guide guiding incident light emitted from the light sources; a multispectral imaging system including at least one image sensor; and a computer system outputting an image of the object tissue to the outside. Thus, the apparatus for photodynamic therapy and photodetection of the present invention can effectively perform the photodynamic therapy and photodetection by means of the combined light source capable of irradiating light having various spectral components to an object tissue and the multispectral imaging system capable of obtaining images from several spectral portions for these various spectral ranges at the same time, thus improving the accuracy of diagnosis and efficiency of the photodynamic therapy.
Owner:KOREA ELECTROTECH RES INST
Who we serve
  • R&D Engineer
  • R&D Manager
  • IP Professional
Why Eureka
  • Industry Leading Data Capabilities
  • Powerful AI technology
  • Patent DNA Extraction
Social media
Try Eureka
PatSnap group products