Image Fusion Technique Based on PCA and Fuzzy Logic Part 2 Essay

In accession to the binary images, the proposed method may be tested on discrete color images also. These type of

}\}$ be the corresponding Fuzzy sets defined by the membership function $ \{\mu _{A}^{1},\mu _{A}^{2},\mu _{A}^{3}, . . .\mu _{A}^{m}\}$. The implication of the form $\left ( A,{T_{i}^{A}} \right )\rightarrow \left ( B,{T_{j}^{B}} \right )$ or $A\epsilon {F_{i}^{A}},B\epsilon {F_{j}^{B}}$ is a Fuzzy Association Rule.\\

This report focuses on the Data combined by multi-sensors as a critical variable for acquiring solid context oriented data in keen spaces which utilize the pervasive and omnipresent registering strategies. Versatile combination enhances hearty operational framework exhibitions then settles on a solid choice by diminishing indeterminate data. Then again, these combination systems experience the ill effects of issues with respect to the exactness of estimation or derivation. There are no regularly acknowledged methodologies exist presently. In this report, the points of interest and detriments of combination procedures which might be utilized as a part of particular requisitions are presented. Secondly, well-known models, calculations, frameworks, and requisitions relying upon the proposed methodologies are classified. At long last, the related issues for combination methods inside the shrewd spaces then recommend research headings for enhancing the choice making in unverifiable circumstance are recommended.

George J. Kilr and Bo Yuan [32] Fuzzy logic is a way to formalize the human decision capacity of imprecise reasoning, or approximate reasoning. Such type of reasoning represents the human ability to find out the reason approximately and judge

This paper presents a image fusion technique based on PCA and fuzzy logic. the framework of the proposed image fusion technique is divided in the following major phases:  preprocesing phase  Feature extraction based on the principal component analysis  The image fusion based on fuzzy set  Reconstruction final image The figure (1) shows the framework of the proposed image fusion and its phases. Fig.

This article is written for an intended audience of undergraduate or graduate students, this article provides an introduction to, and an overview of what fuzzy systems are. Presented in this article is an acknowledgment of the contributions that fuzzy systems are making to the Artificial Intelligence discipline, as well as examples of fuzzy systems which are in use today.

Abstract: Bad weather, such as fog, haze significantly degrade the visibility of the scene. Haze is an atmospheric phenomenon that significantly degrades the visibility of outdoor scenes. It is due to the atmosphere particles that absorb and scatter the light. Removing haze means increases the visibility of the scene and applicable for both color and gray images. The method is a fusion based strategy that derives the inputs and weight maps only from the original degraded image. To minimize the artifacts introduce the weight maps. The information of the derived inputs to preserve the regions with good visibility, by computing three measures: luminance, chromaticity, and salience

A fuzzy multi-attribute group decision making model is reviewed. Some typos in the original paper are corrected. Some absurd parts are reconstructed to make an easy understanding. The method of TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) is detailed explained. After all, deep thoughts into the original paper are made.

Principal component analysis (PCA) is a statistical method for correlation, from a small number of independent principal components to represent the total samples. It is always applied in geochemical exploration for extraction anomalies from element variates and integration of multi-source geo-information. In a recently year, several improved PCA methods were proposed by traditional PCA. Cheng (2006) proposed spatially weighted principle component analysis (SWPCA) for images processing by spatially weighted correlation coefficient matrix by incorporating a fuzzy mask defined by sample location.

Abstract- In recent years the image processing techniques are used commonly in various medical areas for improving earlier detection and treatment stages, in which the time span or elapse is very important to discover the disease in the patient as possible as fast, especially in many tumours such as the lung cancer, breast cancer. This system generally first segments the area of interest (lung) and then analyses the separately obtained area for nodule detection in order to examine the disease. Even with several lung tumour segmentations have been presented, enhancing tumour segmentation methods are still interesting because lung tumour CT images has some complex characteristics, such as large difference in tumour appearance and uncertain tumour boundaries. To address this problem, tumour segmentation method for CT Images which separates non-enhancing lung tumours from healthy tissues has been carried out by clustering method. The proposed method uses pre-processing technique that remove unwanted artifacts using median and wiener filters. Initially, the segmentation of the CT images has been carried out by using K- Means clustering method. To the clustered result, EK-Mean clustering is applied . Further the features like entrpy, Contrast, Correlation,Homogenity and the area are extracted from the tumorous part of Fuzzy Ek- Means segmented Image. For feature extraction, statistic method called Gray Level Co-occurrence Matrix (GLCM). Classification is done by using the

Nowadays retail video analytics has moved out ahead of the traditional domain of loss prevention and security by providing retailers understanding business intelligence for instance queue data and store traffic statistics. Such information allows on behalf of optimized store performance, enhanced customer experience, ultimately higher reduced operational costs and profitability. This paper gives an overview of various camera-based applications in retail. It also presents a number of the promising technical guidelines for survey in retail video analytics.

In IT Foundations, I was assigned by Mrs. Baumaster to watch the video Fuzzy Thinking. The speaker in this video is an Astrophysicist by the name of Neil deGrasse Tyson. In the video, Fuzzy Thinking he talked about many things like what was wrong with the misspellings of the word Cat and about his choice between hiring two workers. The video did not seem very interesting at first but as I watched it, the video got more and more interesting. Tyson is a very good presenter in the video because he made it interesting and fun to listen to him. All in all, this video was a great way to get his message out on how people think.

In this chapter, a brief description about medical image fusion with its important terms is provided. This chapter analysis image fusion three algorithms performance through medical application. Analysis of performance of the algorithms is compared in three modes of fusion rules for better medical image fusion. The fusion performance metrics used to utilize the objective of getting better quality medical fused image and detection of better algorithm are PSNR and MSE. The simulation work has been done using MATLB R2011a. [30]

Step 3: The basic principles in Step 2 were then extended to calculate the degree of possibility of, Ŝi, of one criterion, being greater than all the other (n- 1) convex fuzzy numbers, Ŝj, of other criteria. This can be defined as follows,

M. Hossny and S. Nahavandi have proposed the duality between image fusion algorithm and quality metrics. The authors have proposed duality index as main function against which combination of fusion