Hi !
I need to extract the edge of any image using fuzzy inference rules, pls send some sample coding for us ....etc

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Image Processing for Edge Detection

Edge detection is a fundamental tool used in most image processing applications to obtain information from the frames as a precursor step to feature extraction and object segmentation. This process detects outlines of an object and boundaries between objects and the background in the image. An edge-detection filter can also be used to improve the appearance of blurred or anti-aliased image streams. The basic edge-detection operator is a matrix area gradient operation that determines the level of var ....etc

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EDGE Compact and EDGE Classic Packet Data Performance
Abstract
Even though cellular radio services have been extremely successful in providing untethered voice communications, wireless data services have captured only a limited market share so far. One obstacle for wireless data services is their limited peak bit rates. Existing wireless data rates, up to several tens of kb/s, may be over one order of magnitude short of what is required to make popular applications user-friendly. To accomplish these necessities we go for ....etc

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EDGE Compact and EDGE Classic Packet Data Performance
Abstract
Even though cellular radio services have been extremely successful in providing untethered voice communications, wireless data services have captured only a limited market share so far. One obstacle for wireless data services is their limited peak bit rates. Existing wireless data rates, up to several tens of kb/s, may be over one order of magnitude short o ....etc

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Fuzzy Logic and Fuzzy Sets 


History


Lotfi Zadeh, at the University of California at Berkeley, first presented fuzzy logic in the mid-1960's.

Zadeh developed fuzzy logic as a way of processing data. Instead of requiring a data element to be either a member or non-member of a set, he introduced the idea of partial set membership.

In 1974 Mamdani and Assilian used fuzzy logic to regulate a steam engine.

In 1985 researchers at Bell laboratories ....etc

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HARDWARE BASED EDGE DETECTION

INTRODUCTION:
Edge detection is fundamental tool used in most image processing applications to obtain information from the frames before feature extraction and object segmentation. This process detects outlines of an object and boundaries between objects and the background in the image. Beyond that, Edge Detection refers to the process of identifying and locating sharp discontinuities in intensities in an image. The discontinuities are abrupt changes in pixels intensity which characterize boundaries of objects ....etc

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Abstract
In this paper, a novel method of hybrid filter for denoising digital images corrupted
by mixed noise has been presented. The proposed design of hybrid filter utilizes the
concept of neuro fuzzy network and spatial domain filtering. This method incorporates
improved adaptive wiener filter and adaptive median filter to reduce white Gaussian noise
and impulse noise respectively. Selection of filters depends upon the performance of the
impulse noise detection process. The edge detector is capable of extracting edges from
fil ....etc

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ABSTRACT

Edge detection is one of the most important steps leading to the analysis of processed image data. Image texture segmentation is an important problem and occurs frequently in many image-processing applications. The image can be segmented by detecting there boundaries. This paper presents some algorithms that are required for Edge detection of textured image. Algorithms for Edge detection are presented that works directly for non-textured image. These algorithm can be carried out for edge detecti ....etc

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ABSTRACT: The normal direction of the bone contour in computed tomography (CT) images provides important anatomical information and can guide segmentation algorithms. Since various bones in CT images have different sizes, and the intensity values of bone pixels are generally nonuniform and noisy, estimation of the normal direction using a single scale is not reliable. We propose a multiscale approach to estimate the normal direction of bone edges. The reliability of the estimation is calculated from the estimated results and, after re-scaling, ....etc

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ABSTRACT: The normal direction of the bone contour in computed tomography (CT) images provides important anatomical information and can guide segmentation algorithms. Since various bones in CT images have different sizes, and the intensity values of bone pixels are generally nonuniform and noisy, estimation of the normal direction using a single scale is not reliable. We propose a multiscale approach to estimate the normal direction of bone edges. The reliability of the estimation is calculated from the estimated results and, after re-scaling, ....etc

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