The Improved Membership Matrix Initialization Method of FCM for Image Segmentation

2014 ◽  
Vol 989-994 ◽  
pp. 3743-3746
Author(s):  
Zong Jia Wu ◽  
Li Kun Liu
Keyword(s):  
Image Processing ◽  
Image Segmentation ◽  
Objective Function ◽  
Membership Function ◽  
Original Image ◽  
Optimum Point ◽  
Blurred Image ◽  
Membership Matrix

The performance of FCM for image segmentation directly subjects to the initialized membership matrix. This paper proposed twice FCM method to solve the membership matrix initiation problem. The image is spared to a blurred image at first, and then uses the FCM for the blurred image to obtain an iterative result, in which the membership matrix is taken as the initialized membership function of the FCM for the original image processing. This method overcomes the random membership initialization method cannot convergence to the optimum point of the objective function of FCM for the image segmentation at some extend, furthermore, it can obtain better results than the traditional FCM method.

Watershed Image Segmentation Based on PSO and FCM

2014 ◽  
Vol 1070-1072 ◽  
pp. 2041-2044 ◽  
Author(s):  
Gui Shui Yu ◽  
Ke Li
Keyword(s):  
Image Processing ◽  
Image Segmentation ◽  
Target Function ◽  
Watershed Algorithm ◽  
Original Image ◽  
Watershed Segmentation ◽  
Swarm Optimization ◽  
Sonar Image ◽  
Segmented Image ◽  
Fuzzy C Means Clustering

A watershed segmentation algorithm based on fuzzy C-means clustering () was proposed in this paper , which can solve the problem of over-segmentation in sonar image processing. Firstly, the original image was transformed to be gray image and then segmented by watershed algorithm. Secondly, the improved particle swarm optimization () was used to find the accurate original clustering centers of . Finally, with the accurate centers and the improved target function, the small regions of the initial segmented image was clustered by . The iterating number was controlled to increase segmenting speed. Additionally, the high sonar image segmentation efficiency is testified in the experiment and the problem of over-segmentation is restricted.

scholarly journals Design a model of Image Restoration using AI in Digital Image Processing

2021 ◽  
Vol 12 (5) ◽  
pp. 862-865
Author(s):  
Boosi Shyamala, Dr. Chetana Tukkoji, Archana S Nadhan, Dioline Sara
Keyword(s):  
Image Processing ◽  
Image Restoration ◽  
Digital Image Processing ◽  
Digital Image ◽  
Motion Blur ◽  
Original Image ◽  
Source Imaging ◽  
Point Extension ◽  
Noise Image ◽  
Blurred Image

Image restoration is the process of obtaining a distorted/noise image and giving an approximate clear image of the original image. False focus, motion blur and noise are forms of distortion. Image restoration can be done by reversing the process called Point Extension Function (PSF). In this process, the blurred image is generated by point source imaging and can be used to restore the image lost due to the blur process. Like to form. Modern artificial intelligence (AI) applied to image processing includes facial recognition, object recognition and detection, video, image action, and visual search. It helps to develop smart applications in digital image processing.

scholarly journals Comparison among operators for detecting and/or extracting or roads using the matlab software and the cartomorph software

2015 ◽  
Vol XL-7/W3 ◽  
pp. 703-706
Author(s):  
E. A. Silva ◽  
C. D. Chaves ◽  
A. F. Santos
Keyword(s):  
Image Segmentation ◽  
Information Systems ◽  
Mathematical Morphology ◽  
Digital Image ◽  
Public Domain ◽  
The Other ◽  
Original Image ◽  
Blurred Image ◽  
Public Domain Software ◽  
Private Domain

FCT / UNESP has been developing CARTOMORPH to be a public domain software which can be operated by users needing to extract and / or detect features from digital images. In this work, two methods were applied for the extraction and / or detection of the contours of the feature of interest (the highway) using a digital image containing part of a highway. One method was applied through the use of operators contained in the Mathematical Morphology toolbox, which is a private domain, SDC Information Systems, and the other was applied using the routine contained in the CARTOMORPH software. In the toolbox, the operators used were mmreadgray, responsible for opening the original image, mmhisteq, which softens the image, mmneg which can reverse the grayscale of pixels, and mmareaclose, which aims to remove image segmentation. This set of operators has resulted in a routine capable of extracting and / or detecting the contour of a road. The Blur and Minimum (Gblur) operator was used in the process using the CARTOMORPH software. This can detect the edges of objects present in the original blurred image, and transform them into an ideal edge ramp. Results from both methods were compared and the conclusion is that the blur and minimum (GBlur) operator gave a better performance. This finding indicates that the set of operators implemented in CARTOMORPH will be able to be operated by users in the field of cartography and related fields, thus enabling the use of a public domain software with efficient results.

APLIKASI PENAJAMAN CITRAA STREET VIEW MENGGUNAKAN METODE GRAMD SCHMIDT SPECTRAL SHARPENING DAN HIGH PASS FILTERING

2019 ◽  
Vol 3 (1) ◽  
Author(s):  
Erna Verawati ◽  
Surya Darma Nasution ◽  
Imam Saputra
Keyword(s):  
Image Processing ◽  
Visual Information ◽  
Intensity Difference ◽  
Original Image ◽  
Filtering Method ◽  
The Road ◽  
Street View ◽  
Result Show ◽  
Filter Process ◽  
High Pass

Sharpening the image of the road display requies a degree of brightness in the process of sharpening the image from the original image result of the improved image. One of the sharpening of the street view image is image processing. Image processing is one of the multimedia components that plays an important role as a form of visual information. There are many image processing methods that are used in sharpening the image of street views, one of them is the gram schmidt spectral sharpening method and high pass filtering. Gram schmidt spectral sharpening method is method that has another name for intensity modulation based on a refinement fillter. While the high pass filtering method is a filter process that btakes image with high intensity gradients and low intensity difference that will be reduced or discarded. Researce result show that the gram schmidt spectral sharpening method and high pass filtering can be implemented properly so that the sharpening of the street view image can be guaranteed sharpening by making changes frome the original image to the image using the gram schmidt spectral sharpening method and high pass filtering.Keywords: Image processing, gram schmidt spectral sharpening and high pass filtering.

A Hard C-Means Clustering Algorithm Incorporating Membership KL Divergence and Local Data Information for Noisy Image Segmentation

2017 ◽  
Vol 32 (04) ◽  
pp. 1850012 ◽  
Author(s):  
R. R. Gharieb ◽  
G. Gendy ◽  
H. Selim
Keyword(s):  
Image Segmentation ◽  
Membership Function ◽  
Clustering Algorithm ◽  
Clustering Algorithms ◽  
Cluster Center ◽  
Local Data ◽  
Cluster Membership ◽  
Kl Divergence ◽  
Clustering Approach ◽  
Center Distance

In this paper, the standard hard C-means (HCM) clustering approach to image segmentation is modified by incorporating weighted membership Kullback–Leibler (KL) divergence and local data information into the HCM objective function. The membership KL divergence, used for fuzzification, measures the proximity between each cluster membership function of a pixel and the locally-smoothed value of the membership in the pixel vicinity. The fuzzification weight is a function of the pixel to cluster-centers distances. The used pixel to a cluster-center distance is composed of the original pixel data distance plus a fraction of the distance generated from the locally-smoothed pixel data. It is shown that the obtained membership function of a pixel is proportional to the locally-smoothed membership function of this pixel multiplied by an exponentially distributed function of the minus pixel distance relative to the minimum distance provided by the nearest cluster-center to the pixel. Therefore, since incorporating the locally-smoothed membership and data information in addition to the relative distance, which is more tolerant to additive noise than the absolute distance, the proposed algorithm has a threefold noise-handling process. The presented algorithm, named local data and membership KL divergence based fuzzy C-means (LDMKLFCM), is tested by synthetic and real-world noisy images and its results are compared with those of several FCM-based clustering algorithms.

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.

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.

An Image Segmentation Method Based on Adaptability Threshold

2013 ◽  
Vol 734-737 ◽  
pp. 2912-2916
Author(s):  
Hui Li ◽  
Ping He
Keyword(s):  
Image Segmentation ◽  
Strain Measurement ◽  
Maximum Entropy Method ◽  
Entropy Method ◽  
Important Application ◽  
Original Image ◽  
Segmentation Method ◽  
One Dimensional ◽  
The One ◽  
Application Fields

Automation strain measurement of the sheet metal deforming becomes one of the important application fields of computer vision. The algorithm of image segmentation based on adaptability threshold was presented for image segmentation of metal steel. In order to validate the proposed method, it is tested and compared with Ostu method and the one-dimensional maximum entropy method. Experiment results indicate that the method is simple and effective, and has an advantage of reservation of the main features of the original image.

Sign in / Sign up

Export Citation Format

Share Document