function v = gmean(A)
%GMEAN Geometric mean of columns.
%
V = GMEAN(A) computes the geometric mean of the columns of A.
%
is a row vector with size(A,2) elements.
%
%
Sample M-file used in Chapter 3.
function [dir, x0 y0] = boundarydir(x, y, orderout)
%BOUNDARYDIR Determine the direction of a sequence of planar points.
%
[DIR] = BOUNDARYDIR(X, Y) determines the direction of travel of
%
a closed, n
function [VG, A, PPG]= colorgrad(f, T)
%COLORGRAD Computes the vector gradient of an RGB image.
%
[VG, VA, PPG] = COLORGRAD(F, T) computes the vector gradient, VG,
%
and corresponding angle array, VA,
function g = fuzzyfilt(f)
%FUZZYFILT Fuzzy edge detector.
%
G = FUZZYFILT(F) implements the rule-based fuzzy filter
%
discussed in the "Using Fuzzy Sets for Spatial Filtering"
%
section of Digital Ima
function mu = bellmf(z, a, b)
%BELLMF Bell-shaped membership function.
%
MU = BELLMF(Z, A, B) computes the bell-shaped fuzzy membership
%
function. Z is the input variable and can be a vector of any
%
function rc_new = bound2eight(rc)
%BOUND2EIGHT Convert 4-connected boundary to 8-connected boundary.
%
RC_NEW = BOUND2EIGHT(RC) converts a four-connected boundary to an
%
eight-connected boundary. RC
function H = bandfilter(type, band, M, N, D0, W, n)
%BANDFILTER Computes frequency domain band filters.
%
%
Parameters used in the filter definitions (see Table 4.3 in
%
DIPUM 2e for more details abou
function [s, sUnit] = bsubsamp(b, gridsep)
%BSUBSAMP Subsample a boundary.
%
[S, SUNIT] = BSUBSAMP(B, GRIDSEP) subsamples the boundary B by
%
assigning each of its points to the grid node to which it
function P = i2percentile(h, I)
%I2PERCENTILE Computes a percentile given an intensity value.
%
P = I2PERCENTILE(H, I) Given an intensity value, I, and a
%
histogram, H, this function computes the per
function f = adpmedian(g, Smax)
%ADPMEDIAN Perform adaptive median filtering.
%
F = ADPMEDIAN(G, SMAX) performs adaptive median filtering of
%
image G. The median filter starts at size 3-by-3 and iter
function varargout = ice(varargin)
%ICE M-file for ice.fig
%
ICE, by itself, creates a new ICE or raises the existing
%
singleton*.
%
%
H = ICE returns the handle to a new ICE or the handle to
%
the e
function cp = cornerprocess(c, T, q)
%CORNERPROCESS Processes the output of function cornermetric.
%
CP = CORNERPROCESS(C, T, Q) postprocesses C, the output of
%
function CORNERMETRIC, with the object
Digital Image Processing
/
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/
Two Categories of Image Enhancement
Spatial domain methods
Direct manipulation of pixels in an image
Frequency domain methods
Modifying the
Digital Image Processing
Image Enhancement
The objective of image enhancement is to process an
image so that the result is more suitable than the
original image for a specific application.
There a
Digital Image Processing
Fourier Transform: Concept
A signal can be
represented as a weighted
sum of sinusoids.
Fourier Transform is a
change of basis, where the
basis functions consist of
sines a
Digital Image Processing
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Color is a powerful descriptor
Simplify object identification and extraction
from scene.
Humans can discern thousands of color shades
and in
Digital Image Processing
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Image Processing : Process images by means
of digital computers
Image : photograph/picture scanned/produced
Digital image : An electronic pho
Digital Image Processing
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Segmentation based on Similarity
Thresholding
Thresholding
Global Thresholding
Suppose that an image, f(x,y), is composed of lig
Digital Image Processing
20071119
Multiresolution Processing
Background
Simple statistical modeling
over the entire image
is impossible
Low resolution
High resolution
Gaussian pyramid
Laplacian p
Digital Image Processing
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Shift Invariant Linear Systems
What is a system?
A system is anything that accepts an input and
produces an output in response to the input
Tradi
Digital Image Processing
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1.
2.
3.
()()
Preliminaries
Binary Images
Binary image processing
Images only consist of two colors (tones):
white or black
Binary images
Digital Image Processing
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Hough
Introduction
The aim of segmentation
Divide an image into constituent regions or objects
Partition an image into regions
T
function rc_new = bound2four(rc)
%BOUND2FOUR Convert 8-connected boundary to 4-connected boundary.
%
RC_NEW = BOUND2FOUR(RC) converts an eight-connected boundary to a
%
four-connected boundary. RC is
function out = conwaylaws(nhood)
%CONWAYLAWS Applies Conway's genetic laws to a single pixel.
%
OUT = CONWAYLAWS(NHOOD) applies Conway's genetic laws to a single
%
pixel and its 3-by-3 neighborhood, N
function image = bound2im(b, M, N)
%BOUND2IM Converts a boundary to an image.
%
IMAGE = BOUND2IM(b) converts b, an np-by-2 array containing the
%
integer coordinates of a boundary, into a binary image