identification of abnormal masses in digital mammography images. The identification technique is divided into two distinct parts i.e. Formation of Homogeneous Blocks and Color Quantization after preprocessing. The type of masses, orientation of masses, shape and distribution of masses, size of masses, position of masses, density of masses, symmetry between two pair etc. are clearly sited after proposed method is executed on raw mammogram, for easy and early detection of abnormalities.
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Chapter 1: Introduction – Defining the Role of Statistics in Business • Statistical Analysis: helps extract information from data and provides an indication of the quality of that information • Data mining: combines statistical methods with computer science & optimization in order to help businesses make the best use of the information contained in large data sets • Probability: helps you understand risky and random events and provides a way of evaluating the likelihood of various potential
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Learning OpenCV Gary Bradski and Adrian Kaehler Beijing · Cambridge · Farnham · Köln · Sebastopol · Taipei · Tokyo Learning OpenCV by Gary Bradski and Adrian Kaehler Copyright © 2008 Gary Bradski and Adrian Kaehler. All rights reserved. Printed in the United States of America. Published by O’Reilly Media, Inc., 1005 Gravenstein Highway North, Sebastopol, CA 95472. O’Reilly books may be purchased for educational, business, or sales promotional use. Online editions are also available
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Measures of Location The average is also referred to as the arithmetic mean or simply the mean. The mean is a measure of the center of the data. Average and mean are used interchangeably to label the result of the sum of all measurements divided by the number of measurements. In mathematical notation the formula for calculating the sample mean is given below. x=x1+x2+…+xnn=i=1nxin If the value given represents the mean of all values in a population it is denoted μ. When the data are from a sample
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Quant: numerical and math operations make sense (Interval if can find interval b/w the data) Shapes of Histograms Symmetry: draw vertical line in the middle= will be same on both sides Skew: Long tail on one side (If long on left it is skewed:Pos/Right if long side right then Neg/Left Modality Unimodal: single peak Bimodal: two peaks (two obvious peaks) Finding # Classes & Length of Histogram and round up for length Stem & Leaf Plot The value of the leaf unit is the unit
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Transformations . . . . . . . . . . . . . . . . . . . . . . . 5 1.5 Position . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6 1.6 Dispersion Percentages . . . . . . . . . . . . . . . . . . . . . . . . 7 2 Graphs and Displays 2.1 9 Histograms . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9 2.1.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . 9 2.1.2 Medians, Modes, and Means Revisited . . . . . . . . . . . 10 2.1.3 z-Scores and Percentile Ranks
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which you live. Click on the tab that reads “Daily.” 1. Prepare a spreadsheet with three columns: Date, High Temperature, and Low Temperature. List the past 60 days for which data is available. 2. Prepare a histogram for the data on high temperatures and comment on the shape of the distribution as observed from these graphs. 3. Calculate and S. mean 40.7483 Standard deviation 1.905878 4. What percentage of the high temperatures
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AIUB 1 Master of Business Administration Course Instructor: Dr. Swapan Kumar Dhar Definition of Statistics Statistics is the science of collecting, organizing, presenting, analyzing and interpreting data for the purpose of making intelligent statements and drawing appropriate conclusions. So, according to this definition, there are four stages: (1) Collection of data
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is available under Start->Programs->Palisade Decision Tools->@Risk 4.5 for Excel). Excel should come up with 2 additional toolbars. For this problem we will need the following distributions: Productive area: discrete distribution, described by a histogram, represented by RISKHISTOGRM(8000, 14000, {0.05, 0.10, 0.15, 0.35, 0.25, 0.10}) Pay thickness: triangular distribution, with a minimum value of 15ft, maximum value of 120ft, and most likely value of 50ft, represented in @Risk by RISKTRIANG(15,
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implementation of Successes Mean Quantization Transform and Spare Network of Winnow with the assistance of Eigenface computation. After having limited the frame of the input image or images from Web-Cam, the image is cropped into an oval or eclipse shape. Then the image is transformed into greyscale color and is normalized in order to reduce color complexities. We also focus on the special characteristics of human facial aspects such as nostril areas and oral areas. After every essential aspectsarescrutinized
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