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Essay heading: Adaptive Thresholding
 
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Issue: Technology
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Date added: December 8, 2006
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No of pages / words: 3 / 786
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We consider each individual row at a time; calculate the average brightness value for that row based on the brightness values of all the pixels in that row. We then use this average value to binarise that row. We then proceed to the next row and so on. In this way we binarise the whole image. For the second part of the assignment, we make a window of user defined size around the centre pixel under consideration, calculate the average value for all the pixels in this window and then binarise that centre pixel using this average value as the threshold value...
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We then use this average value to binarise that row. We then proceed to the next row and so on. In this way we binarise the whole image. For the second part of the assignment, we make a window of user defined size around the centre pixel under consideration, calculate the average value for all the pixels in this window and then binarise that centre pixel using this average value as the threshold value...
displayed next 300 characters

 
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Introduction Digital Image Definitions Common Values Characteristics of Image Operations Types of operations Types of neighborhoods Video Parameters Tools Convolution Properties of Convolution Fourier Transforms Properties of Fourier Transforms Importance of phase and magnitude Circularly symmetric signals Examples of 2D signals and transforms Statistics Probability distribution function of the brightnesses Probability density function of the brightnesses Average Standard deviation Coefficient-of-variation Percentiles Mode SignaltoNoise ratio Contour Representations Chain code Chain code properties Crack code Run codes Perception Brightness Sensitivity Wavelength sensitivity Stimulus sensitivity Spatial Frequency Sensitivity Color Sensitivity Standard observer CIE chromaticity coordinates Optical Illusions Image Sampling Sampling Density for Image Processing Sampling aperture Sampling Density for Image Analysis Sampling for area measurements Sampling for length measurements Conclusions on sampling Noise Photon Noise Thermal Noise On-chip Electronic Noise KTC Noise Amplifier Noise Quantization Noise Cameras Linearity Sensitivity Absolute sensitivity Relative sensitivity SNR Thermal noise (Dark current) Photon noise Shading Pixel Form Square pixels Fill factor Spectral Sensitivity Shutter Speeds (Integration Time) Video cameras Scientific cameras Readout Rate Displays Refresh Rate Interlacing Resolution Algorithms Histogram-based Operations Contrast stretching Equalization Other histogram-based operations Mathematics-based Operations Binary operations Arithmetic-based operations Convolution-based Operations Background Convolution in the spatial domain Convolution in the frequency domain Smoothing Operations Linear Filters Non-Linear Filters Summary of Smoothing Algorithms Derivative-based Operations First Derivatives Second Derivatives Other Filters Morphology-based Operations Fundamental definitions Dilation and Erosion Boolean Convolution Opening and Closing itandMiss operation Summary of the basic operations Skeleton Propagation Summary of skeleton and propagation Gray-value morphological processing Morphological smoothing Morphological gradient Morphological Laplacian Summary of morphological filters Techniques Shading Correction Model of shading Estimate of shading Basic Enhancement and Restoration Techniques Unsharp masking Noise suppression Distortion suppression Segmentation Thresholding Edge finding Binary mathematical morphology Gray-value mathematical morphology Acknowledgments References
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