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Tags: SEGMENTATION, IMAGE, systems fuzzy, fuzzy systems, fuzzy neural, fuzzy networks, fuzzy logic in expert system, fuzzy logic algorithms, introduction to fuzzy, fuzzy search, FUSION, INFORMATION, BASED, FUZZYLOGIC, FUZZYLOGIC BASED INFORMATION FUSION FOR IMAGE SEGMENTATION,
 
 
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FUZZY-LOGIC BASED INFORMATION FUSION FOR IMAGE SEGMENTATION
Post: #1


.doc  Fuzzy Logic.doc (Size: 149 KB / Downloads: 137)
ABSTRACT
It is required to cluster the images for finding groups of data together in many fields such as Marketing, Biology, Libraries, Insurance, City-Planning. The project involves the implementation of Fuzzy based information fusion for image segmentation. Here we first perform image segmentation on different cues. The Fuzzy-c-means clustering algorithm is used for segmentation of each cue space. In any field, where finding a group of similar region in an image is the task then the clustering technique should be used for identifying the similar regions into single group. This will have different inputs, outputs and other parameters, which are dealt with in the coming section of the report.
KEYWORDS: Image Segmentation, Fuzzy Clustering
I. INTRODUCTION
The goal of image segmentation is to cluster pixels into salient image regions, i.e., regions corresponding to individual surfaces, objects, or natural parts of objects. Segmentation could be used for object recognition, occlusion boundary estimation within motion or stereo systems, image compression, image editing, or image database look-up. For intensity images (ie, those represented by point-wise intensity levels) four popular approaches are: threshold techniques, edge-based methods, region-based techniques, and connectivity-preserving relaxation methods. Threshold techniques, which make decisions based on local pixel information, are effective when the intensity levels of the objects fall squarely outside the range of levels in the background. Because spatial information is ignored, however, blurred region boundaries can create havoc.Edge-based methods center around contour detection: their weakness in connecting together broken contour lines make them, too, prone to failure in the presence of blurring. A region-based method usually proceeds as follows: the image is partitioned into connected regions by grouping neighboring pixels of similar intensity levels. Adjacent regions are then merged under some criterion involving perhaps homogeneity or sharpness of region boundaries. Overstringent criteria create fragmentation; lenient ones overlook blurred boundaries and over merge. Hybrid techniques using a mix of the methods above are also popular. A connectivity-preserving relaxation-based segmentation method, usually referred to as the active contour model, was proposed recently. The main idea is to start with some initial boundary shapes represented in the form of spline curves, and iteratively modify it by applying various shrink/expansion operations according to some energy function. Although the energy-minimizing model is not new, coupling it with the maintenance of an ``elastic'' contour model gives it an interesting new twist. As usual with such methods, getting trapped into a local minimum is a risk against which one must guard; this is no easy task.
II. FUZZY LOGIC
Fuzzy Logic was initiated in 1965 by Lotfi A. Zadeh, professor for computer science at the University of California in Berkeley. Basically, Fuzzy Logic (FL) is a multivalued logic that allows intermediate values to be defined between conventional evaluations like true/false, yes/no, high/low, etc. Notions like rather tall or very fast can be formulated mathematically and processed by computers, in order to apply a more human-like way of thinking in the programming of computers. Fuzzy systems are an alternative to traditional notions of set membership and logic that has its origins in ancient Greek philosophy. The precision of mathematics owes its success in large part to the efforts of Aristotle and the philosophers who preceded him. In their efforts to devise a concise theory of logic, and later mathematics, the so-called "Laws of Thought" were posited. One of these, the "Law of the Excluded Middle," states that every proposition must either be True or False. Even when Parminedes proposed the first version of this law there were strong and immediate objections: for example, Heraclitus proposed that things could be simultaneously true and not True. It was Plato who laid the foundation for what would become fuzzy logic, indicating that there was a third region (beyond True and False) where these opposites "tumbled about." Other, more modern philosophers echoed his sentiments, notably Hegel, Marx, and Engels. But it was Lukasiewicz who first proposed a systematic alternative to the bi-valued logic of Aristotle
Post: #2
to get information about the topic fuzzy logic in image processing full report ,ppt and related topic refer the link bellow

http://project-seminars.com/Thread-fuzzy...fuzzy-sets

http://project-seminars.com/Thread-fuzzy...-stability

http://project-seminars.com/Thread-fuzzy...gmentation

http://project-seminars.com/Thread-noise...ring--7175

http://project-seminars.com/Thread-neuro-fuzzy-logic

http://project-seminars.com/Thread-fuzzy...ull-report
 

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