Integration of wavelet network and image processing for determination of total pigments in bitter orange ( Citrus aurantium L.) peel during ripening

2019 ◽  
Vol 42 (5) ◽  
Author(s):  
Saeedeh Taghadomi‐Saberi ◽  
Amin A. Masoumi ◽  
Morteza Sadeghi ◽  
Maryam Zekri
Keyword(s):  
Image Processing ◽  
Citrus Aurantium ◽  
Wavelet Network ◽  
Bitter Orange

Mass Spectrometric Determination of the Predominant Adrenergic Protoalkaloids in Bitter Orange (Citrus aurantium)

2007 ◽  
Vol 55 (24) ◽  
pp. 9769-9775 ◽  
Author(s):  
Bryant C. Nelson ◽  
Karsten Putzbach ◽  
Katherine E. Sharpless ◽  
Lane C. Sander
Keyword(s):  
Mass Spectrometric ◽  
Citrus Aurantium ◽  
Bitter Orange ◽  
Mass Spectrometric Determination

Information and estimation in image processing

1987 ◽  
Vol 45 ◽  
pp. 14-17
Author(s):  
B. Roy Frieden
Keyword(s):  
Image Processing ◽  
Optical System ◽  
Large Amplitude ◽  
Communication Theory ◽  
Image Data ◽  
Direct Approach ◽  
Ill Posed

Despite the skill and determination of electro-optical system designers, the images acquired using their best designs often suffer from blur and noise. The aim of an “image enhancer” such as myself is to improve these poor images, usually by digital means, such that they better resemble the true, “optical object,” input to the system. This problem is notoriously “ill-posed,” i.e. any direct approach at inversion of the image data suffers strongly from the presence of even a small amount of noise in the data. In fact, the fluctuations engendered in neighboring output values tend to be strongly negative-correlated, so that the output spatially oscillates up and down, with large amplitude, about the true object. What can be done about this situation? As we shall see, various concepts taken from statistical communication theory have proven to be of real use in attacking this problem. We offer below a brief summary of these concepts.

The quantitative comparison of experimental and simulated diffraction contrast images

1995 ◽  
Vol 53 ◽  
pp. 102-103
Author(s):  
Stuart McKernan
Keyword(s):  
Image Processing ◽  
Personal Computer ◽  
Close Agreement ◽  
Quantitative Comparison ◽  
Image Simulation ◽  
Computing Power ◽  
Diffraction Contrast ◽  
Simulated Images ◽  
Made In

For many years the concept of quantitative diffraction contrast experiments might have consisted of the determination of dislocation Burgers vectors using a g.b = 0 criterion from several different 2-beam images. Since the advent of the personal computer revolution, the available computing power for performing image-processing and image-simulation calculations is enormous and ubiquitous. Several programs now exist to perform simulations of diffraction contrast images using various approximations. The most common approximations are the use of only 2-beams or a single systematic row to calculate the image contrast, or calculating the image using a column approximation. The increasing amount of literature showing comparisons of experimental and simulated images shows that it is possible to obtain very close agreement between the two images; although the choice of parameters used, and the assumptions made, in performing the calculation must be properly dealt with. The simulation of the images of defects in materials has, in many cases, therefore become a tractable problem.

DETERMINATION OF POROSITY CONTENT IN COMPOSITES BY MICROGRAPH IMAGE PROCESSING

2008 ◽  
Author(s):  
A. H. Kite ◽  
D. K. Hsu ◽  
D. J. Barnard ◽  
Donald O. Thompson ◽  
Dale E. Chimenti
Keyword(s):  
Image Processing
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