lab data

The maketform function is used to define the desired 2D spatial transformation.
Zero-order (or nearest-neighbor) interpolation is simple and computationally fast, but produces
low-quality results, with artifacts such as blockiness effects—which are more pronounced at largescale factors.
First-order (or bilinear) interpolation calculates the gray value of the interpolated pixel (at
coordinates (x’,y’)) as a weighted function of the gray values of the four pixels surrounding the
reference pixel in the input image.
Bicubic interpolation it takes into account the 4×4 neighborhood around the reference pixel and
computes the resulting gray level of the interpolated pixel by performing the convolution of the
4×4 neighborhood with a cubic function.
The imresize function allows the user to specify the interpolation method used.
Translation of an input image f(x,y) with respect to its Cartesian origin to produce an output image
g(x’,y’) where each pixel is displaced by [Δx,Δy] (i.e., x’ = x+Δx and y’ = y+Δy) consists of a
special case of affine transform. You will use maketform and imtransform to perform image
Image registration is the process of aligning two or more images of the same scene. First, each
input image is compared with a reference image—also known as base image. Next, a spatial
transformation is applied to the input image in order to align it with the base image. The key step
in image registration is determining the parameters of the spatial transformation required to bring
the images into alignment.
Image Enhancement techniques usually have one of these two goals:
1. To improve the subjective quality of an image for human viewing.
2. To modify the image in such a way as to make it more suitable for further analysis and automatic
extraction of its contents.
Contrast manipulation: Pixel values of f<m are compressed toward darker values in the output
image, whereas values of f>m are mapped to brighter pixel values in the resulting image. function
imadjust to perform contrast adjustments.
The imadjust function in the IPT can be used to perform gamma correction.
The histogram of a monochrome image is a graphical representation of the frequency of occurrence
of each gray level in the image. imhist function to calculate and display the histogram of a
monochrome image. Histograms provide an easy, practical, and straightforward way of evaluating
image attributes, such as overall contrast and average brightness.
Histogram equalization is a technique by which the gray-level distribution of an image is changed
in such a way as to obtain a uniform (flat) resulting histogram, in which the percentage of pixels
of every gray level is the same. This method usually increases the global contrast of many images,
especially when the usable data of the image is represented by close contrast values.
convolution and correlation in one dimension can be achieved by means of the imfilter.
averaging filter automatically through the fspecial function, also use for generating mask.
Gaussian filter is similar to the non-uniform averaging filter in that the coefficients are not
We are required to convert the image to doubles because a Laplacian filtered image can result in
negative values.
if the mask center is negative, we subtract the filtered image from the original, and if it is positive,
we add.
Un-sharp masking is a simple process of subtracting a blurred image from its original to generate
a sharper image.
For shrinking imadjust function is used.
High-boost filtering is a sharpening technique that involves creating a sharpening image and
adding it to the original image.
freqz2 used to Obtain the frequency-domain filter response.
fft2 obtain 2d fourier transform and ifft2 to obtain inverse transform.
Low-pass filters attenuate the high-frequency components of the Fourier transform of an image,
while leaving the low-frequency components unchanged. The typical overall effect of applying a
low-pass filter (LPF) to an image is a controlled degree of blurring.
An ideal low-pass filter enhances all frequency components within a specified radius (from the
center of the FT), while attenuating all others.
A Gaussian low pass filter attenuates high frequencies using a transfer function whose shape is
based on a Gaussian curve. The width of the bell-shaped curve can be controlled by specifying the
parameter σ, which is functionally equivalent to the cutoff frequency D0 defined previously: lower
values of σ correspond to more strict filtering, resulting in blurrier results. The smooth transition
between passband and stopband of the Gaussian LPF guarantees that there will be no noticeable
ringing artifacts in the output image.
The Butterworth family of filters—widely used in 1D analog signal processing— provides an
alternative filtering strategy whose behavior is a function of the cutoff frequency, D0, and the order
of the filter, n. The shape of the filter’s frequency response, particularly the steepness of the
transition between passband and stopband, is also controlled by the value of n: higher values of n
correspond to steeper transitions, approaching the ideal filter behavior.
High-pass filters attenuate the low-frequency components of the Fourier transform of an image,
while enhancing the high-frequency components (or leaving them unchanged). The typical overall
effect of applying a high-pass filter to an image is a controlled degree of sharpening.
An ideal high-pass filter attenuates all frequency components with in a specified radius (from the
center of the FT), while enhancing all others.
A Gaussian high pass filter attenuates low frequencies using a transfer function whose shape is
based on a Gaussian curve. The behavior of the filter can be controlled by specifying the parameter
distmatrix function returns a two dimensional array, which should be of the same size as the image
being processed. The values in the array represent the distance from each pixel to the center of the
Edge detection methods usually rely on calculations of the first or second derivative along the
intensity profi
u\\le. The first derivative has the desirable property of being directly proportional to the difference
in intensity across the edge; consequently, the magnitude of the first derivative can be used to
detect the presence of an edge at a certain point in the image. The sign of second derivative can be
used to determine whether a pixel lies on the dark or on the bright side of an edge. Moreover, the
zero crossing between its positive and negative peaks can be used to locate the center of thick
Laplacian operator, Laplacian of gaussian, canny edge detector.
Edge, this function is used to get Robert, sobel, prewitt, canny, Laplacian of gaussian edges.
Segmentation is defined as the process of partitioning an image into a set of non-overlapping
regions whose union is the entire image. These regions should ideally correspond to objects and
their meaningful parts, and background. [similarity, discontinuity]
INTENSITY-BASED SEGMENTATION rely on pixel statistics—usually expressed in the form
of a histogram to determine which pixels belong to foreground objects and which pixels should be
labeled as background.
Thresholding an image is a process in which there are relatively few objects of interest whose
shape is more important than surface properties (such as texture) and whose average brightness is
relatively higher or lower than the other elements in the image.
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