A New Method for Edge Image Analysis

1979 ◽  
Vol 26 (3) ◽  
pp. 349-356 ◽  
Author(s):  
A. Basuray ◽  
S.K. Sarkar ◽  
M. De
Keyword(s):  
Image Analysis ◽  
New Method ◽  
Edge Image

scholarly journals Digital Imaging as a Possible Approach in Evaluation of Islet Yield

2003 ◽  
Vol 12 (2) ◽  
pp. 129-133 ◽  
Author(s):  
Peter Girman ◽  
Jan Kříž ◽  
Jozef Friedmanský ◽  
FrantišEk Saudek
Keyword(s):  
Image Analysis ◽  
Digital Image ◽  
Digital Imaging ◽  
Digital Image Analysis ◽  
Three Dimensional ◽  
Two Dimensions ◽  
New Method ◽  
Important Method ◽  
Three Dimensional Objects ◽  
Significant Difference

Digital image analysis (DIA) is a new method in assessment of islet amount, which is expected to provide reliable and consistent results. We compared this method with conventional counting of small numbers of rat islets. Islets were isolated from 8 pancreases and counted in 24 samples in duplicate, first routinely by sizing according to estimated diameters under a calibrated reticule and then by processing of islets pictures taken by camera. As presumed, no significant difference was found in absolute numbers of islets per sample between DIA and conventional assessment. Volumes of islets per sample measured by DIA were on average more than 10% higher than amounts evaluated conventionally, which was statistically significant. DIA has been shown to be an important method to remove operator bias and provide consistent results. Evaluation of only two dimensions of three-dimensional objects still represents a certain limitation of this technique. With lowering of computer prices the system could become easily available for islet laboratories.

scholarly journals Recognition the Parameters of Slub-yarn Based on Image Analysis

2011 ◽  
Vol 6 (1) ◽  
pp. 155892501100600 ◽  
Author(s):  
Ruru Pan ◽  
Weidong Gao ◽  
Jihong Liu ◽  
Hongbo Wang
Keyword(s):  
Image Analysis ◽  
Fourier Transform ◽  
High Resolution ◽  
New Method ◽  
Slub Yarn

In this study, a new method for recognizing the parameters of slub-yarn based on image analysis has been proposed. The slub yarn was wrapped on the surface of the black board by YG381 Yarn Evenness Tester. A high resolution scanner was used to acquire the yarn image. Gray stretching and thresholding were carried out to preprocess the image of slub yarn. By separating the slubs from base yarn with different widths, the slub length, slub distance and slub amplitude can be obtained. With the lists of slub length and slub distance, the periodicity rule of slub yarn can be determined. The period of the slubs then will be identified by 1D-Fourier transform. The experiment indicated that the method can identify the parameters of slub yarn with satisfactory results.

scholarly journals Spectrogram Image Analysis of Error Signals for Minimizing Impulse Noise

2016 ◽  
Vol 2016 ◽  
pp. 1-9 ◽  
Author(s):  
Jeakwan Kim ◽  
Yunseon Choi ◽  
Young-Sup Lee
Keyword(s):  
Image Analysis ◽  
Impulse Noise ◽  
Active Noise Control ◽  
Convergence Speed ◽  
New Method ◽  
Least Mean Square ◽  
Real Time Control ◽  
Step Size ◽  
Time Control ◽  
Analysis Of Error

This paper presents the theoretical and experimental study on the spectrogram image analysis of error signals for minimizing the impulse input noises in the active suppression of noise. Impulse inputs of some specific wave patterns as primary noises to a one-dimensional duct with the length of 1800 mm are shown. The convergence speed of the adaptive feedforward algorithm based on the least mean square approach was controlled by a normalized step size which was incorporated into the algorithm. The variations of the step size govern the stability as well as the convergence speed. Because of this reason, a normalized step size is introduced as a new method for the control of impulse noise. The spectrogram images which indicate the degree of the attenuation of the impulse input noises are considered to represent the attenuation with the new method. The algorithm is extensively investigated in both simulation and real-time control experiment. It is demonstrated that the suggested algorithm worked with a nice stability and performance against impulse noises. The results in this study can be used for practical active noise control systems.

Noisy Image Segmentation Based on Artificial Bee Colony Algorithm

2014 ◽  
Vol 687-691 ◽  
pp. 3652-3655
Author(s):  
Yong Hao Xiao ◽  
Zhuo Bin He ◽  
Yao Hu ◽  
Wei Yu Yu
Keyword(s):  
Image Analysis ◽  
Image Segmentation ◽  
Artificial Bee Colony Algorithm ◽  
Artificial Bee Colony ◽  
Experimental Results ◽  
New Method ◽  
Noisy Image ◽  
Noisy Images ◽  
Bee Colony

Segmentation of noisy images is one of the most challenging problems in image analysis. It hasn’t yet been solved very well. In this paper, we propose a new method for image segmentation, which is able to segment two kinds of noisy images. The experimental results prove that Artificial Bee Colony Algorithm performs better for two types of noisy images.

A new method for robust feature extraction of otolith growth marks using fingerprint recognition methods

2005 ◽  
Vol 56 (5) ◽  
pp. 791 ◽  
Author(s):  
M. Palmer ◽  
A. Álvarez ◽  
J. Tomás ◽  
B. Morales-Nin
Keyword(s):  
Image Analysis ◽  
Feature Extraction ◽  
Age Determination ◽  
Fingerprint Recognition ◽  
New Method ◽  
Otolith Growth ◽  
Contour Analysis ◽  
Recent Developments ◽  
Robust Feature Extraction ◽  
Essential Knowledge

Individual and population age structures constitute essential knowledge for proper management of commercial fisheries. Despite the important advances made in age determination using otolith growth structures, there is still a need to improve both precision and accuracy. The problem of increasing precision in age estimations has been addressed via increasing automation in the identification of growth marks. However, approaches based on otolith size, weight, perimeter, and related measurements (including contour analysis) have moderate success in age prediction. Likewise, early attempts of image analysis have reported poor results, both in cases of 1D (grey-intensity profiles) or 2D images. Recent developments in image analysis have broken this trend, and fully automatic techniques could be an alternative for routine ageing in the near future. Here, we propose a new method for 2D feature extraction that provides robust numerical descriptors of the growth structures of otoliths.

scholarly journals Image analysis: a new method for estimating triploidy in commercial bivalves

1994 ◽  
Vol 25 (7) ◽  
pp. 697-708 ◽  
Author(s):  
A. GERARD ◽  
Y. NACIRI ◽  
J.-M. PEIGNON ◽  
C. LEDU ◽  
P. PHELIPOT ◽  
...  
Keyword(s):  
Image Analysis ◽  
New Method

scholarly journals A new method of image analysis to visualize troponin structure in thin filaments

2000 ◽  
Vol 40 (supplement) ◽  
pp. S138
Author(s):  
A. Narita ◽  
T. Yasunaga ◽  
T. Ishikawa ◽  
K. Mayanagi ◽  
T. Wakabayashi
Keyword(s):  
Image Analysis ◽  
New Method ◽  
Thin Filaments

scholarly journals Methods for image noise level estimation

2021 ◽  
Vol 5 (45) ◽  
pp. 713-720
Author(s):  
A.I. Novikov ◽  
A.V. Pronkin
Keyword(s):  
Image Analysis ◽  
Computational Complexity ◽  
White Noise ◽  
Noise Level ◽  
Image Noise ◽  
New Method ◽  
Difference Operators ◽  
Noise Level Estimation ◽  
Block Methods

The article presents a method for estimating the level of discrete white noise in an image, based on the use of linear difference operators with a vector mask. Two variants of a new method for estimating the noise level are proposed, which differ in the accuracy of the obtained estimates and computational complexity. The first version of the method can be attributed to the class of block methods, whereas the second one is intended for the rapid image analysis and is based on processing a small number of rows or columns of an image.

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