Description: 1.an fpga implementation of the image space reconstruction algorithm for hyperspectral imaging analysis
2. fpga implemention of a median filter
3. fpga implementation of digital filters
4.hardware acceleration of edge detection algorithm on fpgas
5.implementation and evaluation of image processing algorithms on reconfigurable architecture using C-based hardware descriptive languages
6. implementing 2D median filter in fpgas
7.视频图像处理与分析的网络资源 Platform: |
Size: 1969152 |
Author:carol |
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Description: 通过演化聚类算法对高光谱图像进行超平面分割，有成像功能，对应高光谱图像文件为HD5格式-Through the evolutionary clustering algorithm for high-spectral image hyper-plane partition, there are imaging features, the corresponding hyperspectral image file format for the HD5 Platform: |
Size: 467968 |
Author:mouse |
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Description: 一种新型的成像技术应用于苹果的表面缺损和水溶性检测。改新技术是超光谱成像技术，有望成为下一代无损光学检测技术。-Development of hyperspectral imaging technique for the detection of apple surface defects and contaminations Platform: |
Size: 1482752 |
Author:任杰 |
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Description: 处理环境一号卫星CCD和HSI高光谱影像的工具，可以转换为ENVI可识别的格式在ENVI软件中打开，目前是3.0版本-The tools can convert HJ1A/1B CCD and HSI hyperspectral imaging to a format recognizable by ENVI software, the current version is 3.0 Platform: |
Size: 389120 |
Author:kennedy_wan |
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Description: 非负约束最小二乘法丰度反演 NCLS 矩阵运算 EA=X
参考文献：[1]R. Bro, S. D. Jong,"A FAST NON-NEGATIVITY-CONSTRAINED LEAST SQUARES algorithm"
[2]Charles L. Lawson, Richard J. Hanson ,Solving Least Squares Problems (Classics in Applied Mathematics) (Society for Industrial Mathematics,1987)
[3]C.I. Chang, Hyperspectral Imaging: Techniques for Spectral Detection
and Classification, chaper 3.5- 非负约束最小二乘法丰度反演 NCLS 矩阵运算 EA=X
参考文献：[1]R. Bro, S. D. Jong,"A FAST NON-NEGATIVITY-CONSTRAINED LEAST SQUARES algorithm"
[2]Charles L. Lawson, Richard J. Hanson ,Solving Least Squares Problems (Classics in Applied Mathematics) (Society for Industrial Mathematics,1987)
[3]C.I. Chang, Hyperspectral Imaging: Techniques for Spectral Detection
and Classification, chaper 3.5 Platform: |
Size: 1024 |
Author:庄丽 |
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Description: For the first time In Croatia an airborne application of hyperspectral imaging was introduce under recent technologic project "System for the Multisensor Airborne Reconnaissance and Surveillance in the Crisis Situations and the Environment protetion" Platform: |
Size: 539648 |
Author:Linh |
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Description: The use of near infrared (NIR) hyperspectral imaging and hyperspectral image analysis for distinguishing
between hard, intermediate and soft maize kernels from inbred lines was evaluated. NIR hyperspectral
images of two sets (12 and 24 kernels) of whole maize kernels were acquired using a Spectral Dimensions
MatrixNIR camera with a spectral range of 960–1662nm and a sisuChema SWIR Platform: |
Size: 2922496 |
Author:吴仪 |
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Description: This booklet is intended only as a general introduction to
hyperspectral imaging. In TNTmips, hyperspectral images can be processed and
analyzed using the Hyperspectral Analysis process (choose Image / Hyperspectral
Analysis the TNTmips menu). For an introduction to this process, consult
the tutorial booklet entitled Analyzing Hyperspectral Images. Additional background information can be found in the booklet Introduction to Remote Sensing
of Environment (RSE).-This booklet is intended only as a general introduction to
hyperspectral imaging. In TNTmips, hyperspectral images can be processed and
analyzed using the Hyperspectral Analysis process (choose Image / Hyperspectral
Analysis the TNTmips menu). For an introduction to this process, consult
the tutorial booklet entitled Analyzing Hyperspectral Images. Additional background information can be found in the booklet Introduction to Remote Sensing
of Environment (RSE). Platform: |
Size: 1980416 |
Author:ngocan |
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Description: Compressive sensing (CS) is a new approach to simultaneous sensing and compression
that enables a potentially large reduction in the sampling and computation
costs for acquisition of signals having a sparse or compressible representation in some
basis. The CS literature has focused almost exclusively on problems involving single
signals in one or two dimensions. However, many important applications involve distributed
networks or arrays of sensors. In other applications, the signal is inherently
multidimensional and sensed progressively along a subset of its dimensions examples
include hyperspectral imaging and video acquisition. Initial work proposed joint sparsity
models for signal ensembles that exploit both intra- and inter-signal correlation
structures. Joint sparsity models enable a reduction in the total number of compressive
mea-Compressive sensing (CS) is a new approach to simultaneous sensing and compression
that enables a potentially large reduction in the sampling and computation
costs for acquisition of signals having a sparse or compressible representation in some
basis. The CS literature has focused almost exclusively on problems involving single
signals in one or two dimensions. However, many important applications involve distributed
networks or arrays of sensors. In other applications, the signal is inherently
multidimensional and sensed progressively along a subset of its dimensions examples
include hyperspectral imaging and video acquisition. Initial work proposed joint sparsity
models for signal ensembles that exploit both intra- and inter-signal correlation
structures. Joint sparsity models enable a reduction in the total number of compressive
mea Platform: |
Size: 6544384 |
Author:muhammedalijalil |
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Search - hyperspectral imaging