Matlab Feature Extraction Using Segmentation And Edge Detection

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This document discusses several image processing techniques in Matlab: 1) Edge detection using the edge function and Sobel and Canny edge detection algorithms. 2) The radon transform which computes projections of an image along specified directions and the inverse radon transform used to reconstruct images from projections. 3) Marker-controlled watershed segmentation which separates touching objects in an image using morphological operations like gradients and markers.

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Matlab:Feature Extraction Using Segmentation and Edge Detection

Detecting Edges Using the edge FunctionIn an image, an edge is a curve that follows a path of rapid change in image intensity.

Detecting Edges Using the edge FunctionI = imread(‘dmt.jpg');

figure, imshow(BW2)Detecting Edges Using the edge FunctionI = imread(‘bird.png');

figure, imshow(BW2)Detecting Edges Using the edge Function

Radon TransformThe radon function computes projections of an image matrix along specified directions.

Radon Transform>> I=zeros(100,100);>> I(40:60, 40:60)=1;>> imshow(I);>> [R,xp] = radon(I,0);>> figure,plot(xp,R);

Radon Transform>> I=zeros(100,100);>> I(40:60, 40:60)=1;>> imshow(I); >> [R,xp] = radon(I,45);>> figure,plot(xp,R);

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Market basket analysis examines customer purchasing patterns to determine which items are commonly bought together. This can help retailers with marketing strategies like product bundling and complementary product placement. Association rule mining is a two-step process that first finds frequent item sets that occur together above a minimum support threshold, and then generates strong association rules from these frequent item sets that satisfy minimum support and confidence. Various techniques can improve the efficiency of the Apriori algorithm for mining association rules, such as hashing, transaction reduction, partitioning, sampling, and dynamic item-set counting. Pruning strategies like item merging, sub-item-set pruning, and item skipping can also enhance efficiency. Constraint-based mining allows users to specify constraints on the type of

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Data Mining: Data processingDataminingTools Inc

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Terminology Machine LearningDataminingTools Inc

Techniques Machine LearningDataminingTools Inc

Machine learning IntroductionDataminingTools Inc

Areas of machine leanringDataminingTools Inc

AI: Planning and AIDataminingTools Inc

AI: Logic in AI 2DataminingTools Inc

AI: Logic in AIDataminingTools Inc

AI: Learning in AI 2DataminingTools Inc

AI: Learning in AI DataminingTools Inc

AI: Introduction to artificial intelligenceDataminingTools Inc

AI: Belief NetworksDataminingTools Inc

AI: AI & SearchingDataminingTools Inc

AI: AI & Problem SolvingDataminingTools Inc

Data Mining: Text and web miningDataminingTools Inc

Data Mining: Outlier analysisDataminingTools Inc

Data Mining: Mining stream time series and sequence dataDataminingTools Inc

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Data warehouse and olap technologyDataminingTools Inc

Data Mining: Data processingDataminingTools Inc

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