scholarly journals Spatial calibration and image processing requirements of an image fiber bundle based snapshot hyperspectral imaging probe: from raw data to datacube

2017 ◽  
Author(s):  
Hoong-Ta Lim ◽  
Vadakke Matham Murukeshan
Keyword(s):  
Image Processing ◽  
Hyperspectral Imaging ◽  
Fiber Bundle ◽  
Raw Data ◽  
Imaging Probe ◽  
Spatial Calibration

Correlation Image Processing of Diagnostic Raw Data Collected with NAOS-CONICA

2005 ◽  
pp. 83-85
Author(s):  
Hans Weghorn ◽  
Rainer Lenzen ◽  
Wolfgang Brandner ◽  
Markus Hartung
Keyword(s):  
Image Processing ◽  
Raw Data ◽  
Correlation Image

scholarly journals Raw data based image processing algorithm for fast detection of surface breaking cracks

2017 ◽  
Author(s):  
Sruthi Krishna K. P. ◽  
Nithin Puthiyaveetil ◽  
Renil Kidangan ◽  
Sreedhar Unnikrishnakurup ◽  
Mathias Zeigler ◽  
...  
Keyword(s):  
Image Processing ◽  
Processing Algorithm ◽  
Image Processing Algorithm ◽  
Raw Data ◽  
Fast Detection

scholarly journals ImageM: a user-friendly interface for the processing of multi-dimensional images with Matlab

F1000Research ◽  
2021 ◽  
Vol 10 ◽  
pp. 333
Author(s):  
David Legland ◽  
Marie-Françoise Devaux
Keyword(s):  
Image Processing ◽  
Image Analysis ◽  
User Interface ◽  
Image Data ◽  
Image Visualization ◽  
Image Processing Algorithms ◽  
Processing Algorithms ◽  
Spatial Calibration ◽  
User Friendly ◽  
Efficient Software

Modern imaging devices provide a wealth of data often organized as images with many dimensions, such as 2D/3D, time and channel. Matlab is an efficient software solution for image processing, but it lacks many features facilitating the interactive interpretation of image data, such as a user-friendly image visualization, or the management of image meta-data (e.g. spatial calibration), thus limiting its application to bio-image analysis. The ImageM application proposes an integrated user interface that facilitates the processing and the analysis of multi-dimensional images within the Matlab environment. It provides a user-friendly visualization of multi-dimensional images, a collection of image processing algorithms and methods for analysis of images, the management of spatial calibration, and facilities for the analysis of multi-variate images. ImageM can also be run on the open source alternative software to Matlab, Octave. ImageM is freely distributed on GitHub: https://github.com/mattools/ImageM.

scholarly journals ANALISA CITRA HIPERSPEKTRAL DAUN DARI TANAMAN KELAPA SAWIT YANG MENGALAMI KEKURANGAN AIR MENGGUNAKAN PROGRAM MATLAB

2019 ◽  
Vol 16 (2) ◽  
pp. 143
Author(s):  
JR Lessy Eka Putri ◽  
Minarni Minarni ◽  
Feri Candra ◽  
Herman Herman
Keyword(s):  
Image Processing ◽  
Hyperspectral Imaging ◽  
Oil Palm ◽  
Imaging System ◽  
Water Volume ◽  
Imaging Method ◽  
Pixel Size ◽  
Water Deficiency ◽  
Image Recording ◽  
Detached Leaves

The hyperspectral imaging method has been widely and intensively used in agriculture to find out various problems that occur in plants. Image processing is very important step in an imaging method. This research aims to create Matlab based program to be used to analyze the leaf image of oil palm plants that has experienced water deficiency. Reflectance intensity values were extracted from the process. The hyperspectral imaging system consisted of a 650 nm diode laser, a spectrograph, monochrome CMOS camera, and Matlab image processing program. The samplesused were 8 month old Tenera variety of oil palm seedlings which were treated to simulate water deficiency in the form of variations in the volume of water, namely 0 mL (without watering), 1000 mL, 2000 mL, and 3000 mL (normal), 3 duplicates for each volume. The samples were given water volume of 1000 mL and 2000 mL for every 7 days in 21 days, while the sampleswith 3000 mL of water were watered every day. Image recording was done on the 21st day for detached leaves at the the bottom part.The results showed that the Matlab program was able to separate each row from 15 images, each of which had a pixel size of 1280 × 1024 and merge each of the same lines into 1024 images with a pixel size of 1280 × 15. The reflectance intensity values were then obtained. The results showed that higher levels of water deficiency in plants produce increasing reflectance intensity values.

scholarly journals Hyperspectral imaging and artificial intelligence to detect oral malignancy – part 1 - automated tissue classification of oral muscle, fat and mucosa using a light-weight 6-layer deep neural network

2021 ◽  
Vol 17 (1) ◽  
Author(s):  
Daniel G. E. Thiem ◽  
Paul Römer ◽  
Matthias Gielisch ◽  
Bilal Al-Nawas ◽  
Martin Schlüter ◽  
...  
Keyword(s):  
Neural Network ◽  
Computer Vision ◽  
Deep Learning ◽  
Hyperspectral Imaging ◽  
Oral Mucosa ◽  
Deep Neural Network ◽  
Light Weight ◽  
Tissue Classification ◽  
Raw Data

Abstract Background Hyperspectral imaging (HSI) is a promising non-contact approach to tissue diagnostics, generating large amounts of raw data for whose processing computer vision (i.e. deep learning) is particularly suitable. Aim of this proof of principle study was the classification of hyperspectral (HS)-reflectance values into the human-oral tissue types fat, muscle and mucosa using deep learning methods. Furthermore, the tissue-specific hyperspectral signatures collected will serve as a representative reference for the future assessment of oral pathological changes in the sense of a HS-library. Methods A total of about 316 samples of healthy human-oral fat, muscle and oral mucosa was collected from 174 different patients and imaged using a HS-camera, covering the wavelength range from 500 nm to 1000 nm. HS-raw data were further labelled and processed for tissue classification using a light-weight 6-layer deep neural network (DNN). Results The reflectance values differed significantly (p < .001) for fat, muscle and oral mucosa at almost all wavelengths, with the signature of muscle differing the most. The deep neural network distinguished tissue types with an accuracy of > 80% each. Conclusion Oral fat, muscle and mucosa can be classified sufficiently and automatically by their specific HS-signature using a deep learning approach. Early detection of premalignant-mucosal-lesions using hyperspectral imaging and deep learning is so far represented rarely in in medical and computer vision research domain but has a high potential and is part of subsequent studies.

scholarly journals Spatial-scanning hyperspectral imaging probe for bio-imaging applications

2016 ◽  
Vol 87 (3) ◽  
pp. 033707 ◽  
Author(s):  
Hoong-Ta Lim ◽  
Vadakke Matham Murukeshan
Keyword(s):  
Hyperspectral Imaging ◽  
Imaging Probe ◽  
Bio Imaging

scholarly journals Towards a new image processing system at Wendelstein 7-X: From spatial calibration to characterization of thermal events

2018 ◽  
Vol 89 (12) ◽  
pp. 123503 ◽  
Author(s):  
F. Pisano ◽  
B. Cannas ◽  
M. W. Jakubowski ◽  
H. Niemann ◽  
A. Puig Sitjes ◽  
...  
Keyword(s):  
Image Processing ◽  
Processing System ◽  
Image Processing System ◽  
Spatial Calibration

scholarly journals Leaf Quality :Hyperspectral Imaging Technology

2018 ◽  
Vol 7 (3.12) ◽  
pp. 693
Author(s):  
Pramila Mary A ◽  
Thippeswamy G
Keyword(s):  
Economic Growth ◽  
Image Processing ◽  
Side Effects ◽  
Hyperspectral Imaging ◽  
Digital Image Processing ◽  
Digital Image ◽  
Food Crops ◽  
Food Crop ◽  
Leaf Quality ◽  
Fundamental Research

India is a rural nation 70% of the populace relies upon horticulture. Farmers are the backbone of our nation.70% of the economic growth depends on agriculture. The major food crops of India are wheat, corn, rice, barley, sorghum. In this project the concentration is on paddy as it is a main food crop of our state. This project helps us to find whether the leaf is diseased or not and also helps us to find the type of disease in paddy leaf. The agribusiness research of programmed leaf sickness recognition is fundamental research point as it might demonstrate benefits in checking substantial fields of products, and subsequently naturally recognize manifestations of illness when they show up on plant takes off. Digital image processing Advanced is a procedure utilized for improvement of the picture. To enhance farming items programmed recognition of side effects is useful.  

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