On the Image Processing Mechanism Based on the Fuzzy Vision

2014 ◽  
Vol 513-517 ◽  
pp. 3134-3138
Author(s):  
Tian Fang Cai ◽  
Zhong Guo Yang
Keyword(s):  
Image Processing ◽  
Image Segmentation ◽  
Fuzzy Theory ◽  
Feature Detector ◽  
Practical Application ◽  
Learning Mechanism ◽  
Different Types ◽  
Application Potential ◽  
Visual Mechanism ◽  
Processing Mechanism

Human visual system has excellent ability in image processing. Based on the visual principles and fuzzy theory, the model of W3M proposed in the paper simulates the level, two-way connectivity, feature detector and learning mechanism of the visual mechanism to a certain extent. The model accomplishes different types of image segmentation by setting different parameters and shows its application potential through the practical application of some really collected biomedical images in segmentation.

scholarly journals Parametric Comparison of K-means and Adaptive K-means Clustering Performance on Different Images

2017 ◽  
Vol 7 (2) ◽  
pp. 810
Author(s):  
Madhusmita Sahu ◽  
K. Parvathi ◽  
M. Vamsi Krishna
Keyword(s):  
Image Processing ◽  
Image Segmentation ◽  
Clustering Algorithm ◽  
Successful Result ◽  
Area Of Interest ◽  
Time Period ◽  
Different Types

<p>Image segmentation takes a major role to analyzing the area of interest in image processing. Many researchers have used different types of techniques to analyzing the image. One of the widely used techniques is K-means clustering. In this paper we use two algorithms K-means and the advance of K-means is called as adaptive K-means clustering. Both the algorithms are using in different types of image and got a successful result. By comparing the Time period, PSNR and RMSE value from the result of both algorithms we prove that the Adaptive K-means clustering algorithm gives a best result as compard to K-means clustering in image segmentation.    </p>

Split and Merge: A Region Based Image Segmentation

2018 ◽  
Vol 6 (8) ◽  
pp. 306 ◽  
Author(s):  
Anju Bala ◽  
Aman Kumar Sharma
Keyword(s):  
Image Processing ◽  
Image Segmentation ◽  
Digital Image Processing ◽  
Digital Image ◽  
Region Growing ◽  
Segmentation Algorithm ◽  
Region Segmentation ◽  
Segmentation Algorithms ◽  
Different Types ◽  
Split And Merge

Image segmentation is a very challenging task in digital image processing field. It is defined as the process of takeout objects from an image by dividing it into different regions where regions that depicts some information are called objects. There are different types of image segmentation algorithms. The segmentation process depends upon the type of description required for an application for which segmentation is to be performed. Hence, there is no universally accepted segmentation algorithm. Region segmentation is divided into three categories region growing, split and merge and watershed. But this study confines only to split and merge techniques. This paper includes split and merge approaches and their extended versions. This study highlights the main limitations and potentials of these approaches.

A Survey on Solar Image Segmentation Techniques

2014 ◽  
Vol 945-949 ◽  
pp. 1899-1902
Author(s):  
Yuan Yuan Fan ◽  
Wei Jiang Li ◽  
Feng Wang
Keyword(s):  
Image Processing ◽  
Pattern Recognition ◽  
Image Analysis ◽  
Image Segmentation ◽  
Image Retrieval ◽  
Fundamental Issue ◽  
Solar Image ◽  
Segmentation Methods

Image segmentation is one of the basic problems of image processing, also is the first essential and fundamental issue in the solar image analysis and pattern recognition. This paper summarizes systematically on the image segmentation techniques in the solar image retrieval and the recent applications of image segmentation. Then the merits and demerits of each method are discussed in this paper, in this way we can combine some methods for image segmentation to reach the better effects in astronomy. Finally, according to the characteristics of the solar image itself, the more appropriate image segmentation methods are summed up, and some remarks on the prospects and development of image segmentation are presented.

Near-Band IR Small Scene Star Image Segmentation Based on Partial Histogram Grey Level

2014 ◽  
Vol 496-500 ◽  
pp. 1834-1839
Author(s):  
Zhe Wang ◽  
Zhe Yan ◽  
Wei Tan
Keyword(s):  
Image Processing ◽  
Image Segmentation ◽  
Real Time ◽  
Grey Level ◽  
Star Image ◽  
Star Images

The near-band IR star images segmentation and recognition is key technique in day time star navigation. Due to the scene of near-band IR star imaging relative small and stellar with high star grade are limited. Pertinence and dynamic grey level threshold is necessary for image processing arithmetic. In order to enhance near-band IR star images segmentation and recognition in real-time, this paper present the process of partial histogram grey level threshold and improve for actually near-band IR star images with scene of no more than 1.5°×1.5°. It can reduce the calculation of near-band IR star images with adjustable threshold. And get rid of disturbance of small imaging square stars and noise points.

scholarly journals RECOGNIZING DIFFERENT TYPES OF STOCHASTIC PROCESSES

2006 ◽  
Vol 06 (01) ◽  
pp. L1-L6
Author(s):  
JONG U. KIM ◽  
LASZLO B. KISH
Keyword(s):  
Image Processing ◽  
Stochastic Processes ◽  
Cross Correlation ◽  
Correlation Method ◽  
Correlation Coefficients ◽  
Power Density Spectrum ◽  
Density Spectrum ◽  
Random Telegraph Signals ◽  
Different Types ◽  
Coefficient Method

We propose a new cross-correlation method that can recognize independent realizations of the same type of stochastic processes and can be used as a new kind of pattern recognition tool in biometrics, sensing, forensic, security and image processing applications. The method, which we call bispectrum correlation coefficient method, makes use of the cross-correlation of the bispectra. Three kinds of cross-correlation coefficients are introduced. To demonstrate the new method, six different random telegraph signals are tested, where four of them have the same power density spectrum. It is shown that the three coefficients can map the different stochastic processes to specific sub-volumes in a cube.

scholarly journals Effects of Motion Compensation Residual Error and Polarization Distortion on UAV-Borne PolInSAR

2021 ◽  
Vol 13 (4) ◽  
pp. 618
Author(s):  
Zexin Lv ◽  
Fangfang Li ◽  
Xiaolan Qiu ◽  
Chibiao Ding
Keyword(s):  
Motion Compensation ◽  
Residual Error ◽  
Synthetic Aperture ◽  
Interferometric Synthetic Aperture Radar ◽  
Practical Application ◽  
Aerial Vehicle ◽  
Application Potential ◽  
Large Motion ◽  
Simulation Results ◽  
Interferometric Sar

Polarimetric Interferometric Synthetic Aperture Radar (PolInSAR) can improve interferometric coherence and phase quality, which has good application potential. With the development of the Mini-SAR system, Unmanned Aerial Vehicle borne (UAV-borne) PolInSAR systems became a reality. However, UAV-borne PolInSAR is easily affected by air currents and other factors, which may cause large motion errors and polarization distortion inevitably exists. However, there are few pieces of research which are about motion compensation residual error (MCRE) and polarization distortion effects on PolInSAR. Though the effects of MCRE on Interferometric SAR (InSAR) and polarization distortion on PolInSAR were studied, respectively, these two parts are independently modeled and analyzed. In this paper, a model that simultaneously considers the effects of these two kinds of errors is proposed, and simulation results are given to validate the model. Then, a quantitative analysis based on a real Quadcopter UAV PolInSAR system is performed according to the model, which is valuable for system design and practical application of the UAV-borne PolInSAR system.

Image Segmentation Using Rough Set Theory

2014 ◽  
Vol 1 (2) ◽  
pp. 62-74 ◽  
Author(s):  
Payel Roy ◽  
Srijan Goswami ◽  
Sayan Chakraborty ◽  
Ahmad Taher Azar ◽  
Nilanjan Dey
Keyword(s):  
Image Processing ◽  
Image Segmentation ◽  
Set Theory ◽  
Rough Set ◽  
Rough Set Theory ◽  
Gray Level ◽  
Rule Generation ◽  
Local Minima ◽  
Fuzzy Correlation ◽  
Segmentation Methods

In the domain of image processing, image segmentation has become one of the key application that is involved in most of the image based operations. Image segmentation refers to the process of breaking or partitioning any image. Although, like several image processing operations, image segmentation also faces some problems and issues when segmenting process becomes much more complicated. Previously lot of work has proved that Rough-set theory can be a useful method to overcome such complications during image segmentation. The Rough-set theory helps in very fast convergence and in avoiding local minima problem, thereby enhancing the performance of the EM, better result can be achieved. During rough-set-theoretic rule generation, each band is individualized by using the fuzzy-correlation-based gray-level thresholding. Therefore, use of Rough-set in image segmentation can be very useful. In this paper, a summary of all previous Rough-set based image segmentation methods are described in detail and also categorized accordingly. Rough-set based image segmentation provides a stable and better framework for image segmentation.

scholarly journals A Hybrid Methodology for Automation the Diagnosis of Leukemia Based on Quantitative and Morphological Feature Analysis

2018 ◽  
Vol 12 (3) ◽  
pp. 56 ◽  
Author(s):  
Hussam N. Fakhouri ◽  
Saleh H. Al-Sharaeh
Keyword(s):  
Image Processing ◽  
Medical Image ◽  
White Blood Cells ◽  
Medical Image Processing ◽  
Initial Diagnosis ◽  
Feature Analysis ◽  
Microscopic Images ◽  
Different Types ◽  
The Status ◽  
Blast Cells

Recent year’s witnessed a huge revolution for developing an automated diagnosis for different disease such as cancer using medical image processing. Many researches have been dedicated to achieve this goal. Analyzing medical microscopic histology images provide us with large information about the status of patient and the progress of diseases, help to determine if the tissue have any pathological changes. Automation of the diagnosis of these images will lead to better, faster and enhanced diagnosis for different hematological and histological tissue images such as cancer. This paper propose an automated methodology for analyzing cancer histology and hematology microscopic images to detect leukemia using image processing by combining two diagnosis procedures initial and advance; the initial diagnosis depend on the percentage of the white blood cells in microscopic images affected by leukemia as indicator for the existence of leukemia in the blood smear sample. Whereas, the advance diagnosis classifying the leukemia according into different types using feature bag classifier. The experimental results showed that the proposed methodology initial diagnosis is able to detect leukemia images and differentiate it from samples that do not have leukemia. While, advance diagnosis it is able to detect and classify most leukemia types and differentiate between acute and chronic, but in some cases in the chronic leukemia where the percent of blast cells and shape are similar; it gave a diagnosis of the type of leukemia to the most similar type.

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