Low-cost design of 35 mm drum camera for high-resolution, high-speed image analysis

1998 ◽  
Vol 69 (12) ◽  
pp. 4195-4197 ◽  
Author(s):  
N. J. Lawson ◽  
J.-L. Liow
Keyword(s):  
Image Analysis ◽  
High Resolution ◽  
High Speed ◽  
Low Cost

Electron Image Analysis

1971 ◽  
Vol 29 ◽  
pp. 80-81
Author(s):  
D. W. Gibbard ◽  
J. A. Crawley ◽  
M. J. Cowham
Keyword(s):  
Image Analysis ◽  
High Resolution ◽  
High Speed ◽  
Signal To Noise Ratio ◽  
High Signal ◽  
Modular Construction ◽  
Wide Range ◽  
Electron Microscopes ◽  
Digital Circuitry ◽  
Accuracy And Repeatability

The history of automatic image analysis is a short one, the techniques being limited until recent years due to the “state of the art” of technology in electronics. A new third generation image analysis machine, the Quantimet 720 was introduced in 1969 designed with a modular construction for application to many fields of interest. It was the first equipment of its type to depart from T.V. standards to scan standards required for optimising the conflicting requirements of high resolution, high speed and high signal to noise ratio. It features high resolution and digital circuitry for accuracy and repeatability and a large and growing range of modules for high computing power. It has also been applied to a wide range of image producing devices including light microscopes, epidiascope (for analysis of photo-micrographs), transmission and scanning electron microscopes.

scholarly journals Using UAV imagery to perform fine-scale mapping of wetland vegetation

2021 ◽  
Author(s):  
◽  
Patrick Hipgrave
Keyword(s):  
Remote Sensing ◽  
Image Analysis ◽  
High Resolution ◽  
Low Cost ◽  
Digital Camera ◽  
Aerial Photographs ◽  
Fine Scale ◽  
Ancillary Data ◽  
Strong Argument ◽  
Object Based

<p>Differentiating between species of plants in aerial imagery is often challenging and, in some cases, can be impossible without significant field data collection. However, remote sensing technology is developing to the point where it is increasingly possible to eliminate the need for extensive fieldwork entirely and conduct non-disruptive monitoring of fragile environments. The increasing availability of UAV platforms with integrated high-resolution cameras and low-cost image processing software is also making remote sensing operations accessible to those outside the scientific community with an interest in environmental monitoring. This project trialled an emerging set of image analysis techniques called ‘object-based image analysis’ to create fine scale maps of a recovering wetland area, based on aerial photographs collected using a consumer-grade UAV (unmanned aerial vehicle). The effects of including additional ancillary data (such as digital surface models (DSMs) and multispectral imagery) in the classification process were also assessed to compare the ability of a standard digital camera to produce high-accuracy classifications to that of a more specialised multispectral sensor. The inclusion of this extra information was found to significantly improve classification accuracy in almost all cases, making a strong argument for the inclusion of ancillary data whenever possible, especially when considering the ease with which ancillary datasets can be produced. The high-resolution (between 2 and 4cm/pixel) imagery provided sufficient detail to observe 28 distinct land cover classes in total, with around 20 classes per image. While the number of classes in the classification scheme may have imposed limits on the overall accuracy of the classified maps, several classes were classified with a high (70% or greater) level of accuracy, including two invasive species, showing that the object-based school of image classification has potential to be a powerful tool for detecting and tracking individual vegetation types.</p>

scholarly journals Using UAV imagery to perform fine-scale mapping of wetland vegetation

2021 ◽  
Author(s):  
◽  
Patrick Hipgrave
Keyword(s):  
Remote Sensing ◽  
Image Analysis ◽  
High Resolution ◽  
Low Cost ◽  
Digital Camera ◽  
Aerial Photographs ◽  
Fine Scale ◽  
Ancillary Data ◽  
Strong Argument ◽  
Object Based

<p>Differentiating between species of plants in aerial imagery is often challenging and, in some cases, can be impossible without significant field data collection. However, remote sensing technology is developing to the point where it is increasingly possible to eliminate the need for extensive fieldwork entirely and conduct non-disruptive monitoring of fragile environments. The increasing availability of UAV platforms with integrated high-resolution cameras and low-cost image processing software is also making remote sensing operations accessible to those outside the scientific community with an interest in environmental monitoring. This project trialled an emerging set of image analysis techniques called ‘object-based image analysis’ to create fine scale maps of a recovering wetland area, based on aerial photographs collected using a consumer-grade UAV (unmanned aerial vehicle). The effects of including additional ancillary data (such as digital surface models (DSMs) and multispectral imagery) in the classification process were also assessed to compare the ability of a standard digital camera to produce high-accuracy classifications to that of a more specialised multispectral sensor. The inclusion of this extra information was found to significantly improve classification accuracy in almost all cases, making a strong argument for the inclusion of ancillary data whenever possible, especially when considering the ease with which ancillary datasets can be produced. The high-resolution (between 2 and 4cm/pixel) imagery provided sufficient detail to observe 28 distinct land cover classes in total, with around 20 classes per image. While the number of classes in the classification scheme may have imposed limits on the overall accuracy of the classified maps, several classes were classified with a high (70% or greater) level of accuracy, including two invasive species, showing that the object-based school of image classification has potential to be a powerful tool for detecting and tracking individual vegetation types.</p>

Large Area Microcontact Printing Presses for Plastic Electronics

2004 ◽  
Vol 846 ◽  
Author(s):  
Hee Hyun Lee ◽  
Etienne Menard ◽  
Nancy G. Tassi ◽  
John A. Rogers ◽  
Graciela B. Blanchet
Keyword(s):  
High Resolution ◽  
High Speed ◽  
Microcontact Printing ◽  
Low Cost ◽  
Manufacturing Processes ◽  
Flexible Substrates ◽  
Large Area ◽  
Plastic Electronics ◽  
Roll To Roll ◽  
Micron Size

ABSTRACTLow cost fabrication is key to the successful introduction of organic electronics and roll to roll manufacturing processes. We propose here that extending flexography into the micron size resolution regime may provide an economical commercialization path for plastic devices. Flexography is a high-speed technique commonly used for printing onto very large area flexible substrates.[1] Although low resolution and poor registration are characteristics of today's flexographic process, it has many similarities with soft lithographic techniques. This work shows that large, (12”×12”) high-resolution printing plates appropriate for use on small tag and label flexographic presses can be prepared using simple and inexpensive flexographic compatible processes. We illustrate the use of these plates for three representative soft lithographic processes: microcontact printing, replica molding, and phase shift lithography.

Algorithms for automated montage synthesis of images from laser-scanning confocal microscopes

1995 ◽  
Vol 53 ◽  
pp. 650-651
Author(s):  
D. E. Becker
Keyword(s):  
Image Analysis ◽  
High Resolution ◽  
Laser Scanning ◽  
Cell Counting ◽  
Wide Area ◽  
Data Set ◽  
Computational Synthesis ◽  
Automated Cell Counting ◽  
Partial View ◽  
The Individual

An efficient, robust, and widely-applicable technique is presented for computational synthesis of high-resolution, wide-area images of a specimen from a series of overlapping partial views. This technique can also be used to combine the results of various forms of image analysis, such as segmentation, automated cell counting, deblurring, and neuron tracing, to generate representations that are equivalent to processing the large wide-area image, rather than the individual partial views. This can be a first step towards quantitation of the higher-level tissue architecture. The computational approach overcomes mechanical limitations, such as hysterisis and backlash, of microscope stages. It also automates a procedure that is currently done manually. One application is the high-resolution visualization and/or quantitation of large batches of specimens that are much wider than the field of view of the microscope.The automated montage synthesis begins by computing a concise set of landmark points for each partial view. The type of landmarks used can vary greatly depending on the images of interest. In many cases, image analysis performed on each data set can provide useful landmarks. Even when no such “natural” landmarks are available, image processing can often provide useful landmarks.

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