Determination of size and shape features of walnut (Juglans regia L.) cultivars using image processing

2012 ◽  
Vol 133 ◽  
pp. 47-55 ◽  
Author(s):  
S. Ercisli ◽  
B. Sayinci ◽  
M. Kara ◽  
C. Yildiz ◽  
I. Ozturk
Keyword(s):  
Image Processing ◽  
Juglans Regia ◽  
Shape Features ◽  
Size And Shape

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

scholarly journals Particle Morphology Analysis of Biomass Material Based on Improved Image Processing Method

2017 ◽  
Vol 2017 ◽  
pp. 1-9 ◽  
Author(s):  
Zhaolin Lu ◽  
Xiaojuan Hu ◽  
Yao Lu
Keyword(s):  
Image Processing ◽  
Particle Size ◽  
Particle Morphology ◽  
Processing Method ◽  
Image Resolution ◽  
Ultrasonic Dispersion ◽  
Image Processing Method ◽  
Size And Shape ◽  
Nominal Size ◽  
Particle Size And Shape

Particle morphology, including size and shape, is an important factor that significantly influences the physical and chemical properties of biomass material. Based on image processing technology, a method was developed to process sample images, measure particle dimensions, and analyse the particle size and shape distributions of knife-milled wheat straw, which had been preclassified into five nominal size groups using mechanical sieving approach. Considering the great variation of particle size from micrometer to millimeter, the powders greater than 250 μm were photographed by a flatbed scanner without zoom function, and the others were photographed using a scanning electron microscopy (SEM) with high-image resolution. Actual imaging tests confirmed the excellent effect of backscattered electron (BSE) imaging mode of SEM. Particle aggregation is an important factor that affects the recognition accuracy of the image processing method. In sample preparation, the singulated arrangement and ultrasonic dispersion methods were used to separate powders into particles that were larger and smaller than the nominal size of 250 μm. In addition, an image segmentation algorithm based on particle geometrical information was proposed to recognise the finer clustered powders. Experimental results demonstrated that the improved image processing method was suitable to analyse the particle size and shape distributions of ground biomass materials and solve the size inconsistencies in sieving analysis.

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