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DIGITAL IMAGE PROCESSINGCHAPTER5 - MOTION DETECTION,
SEGMENTATION AND WAVELETS TMM2443
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Motion DetectionMotion detection is the process of detecting a
change in position of an object relative to its surroundings or the
change in the surroundings relative to an object.Motion detection:
Often from a static camera. Common in surveillance systems. Often
performed on the pixel level only (due to speed constraints).
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Motion DetectionMotion detection plays a fundamental role in any
object tracking or video surveillance algorithm, to the extent that
nearly all such algorithms start with motion detection.Actually,
the reliability with which potential foreground objects in movement
can be identified, directly impacts on the efficiency and
performance level achievable by subsequent processing stages of
tracking and/or recognition.However, detecting regions of change in
images of the same scene is not a straightforward task since it
does not only depend on the features of the foreground elements,
but also on the characteristics of the background such as, for
instance, the presence of vacillating elements.From this starting
point, any detected changed pixel will be considered as part of a
foreground object.
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Motion Detection
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Motion Detection (SAD)SAD is an algorithm for measuring the
similarity between two video frames. It finds the motion by firstly
subtracting the two frames. Secondly, the absolute value of the
latter result is obtained. Thirdly, these differences are summed to
create a simple metric of image motion.Let's take an example,
sequences of frames are employed, the current frame and the next
frame are taken into consideration at every computation. Then, the
frames are changed (the next frame becomes present frame and the
frame that comes after it becomes the next frame). The SAD
algorithm is reliable because: it's fast, takes less memory, time,
and number of steps to achieve the calculation.
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Motion Detection (SAD)
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Motion DetectionThe applications of motion detection are:
Detection of unauthorized entry.Detection of ending of area
occupancy to switch off the lights. Detection of a moving object
which triggers a camera to record subsequent events.
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BinarizationImage binarization: converts a gray-level or a
colored image to a black and white image. Frequently, binarization
is used as a pre-processoing step before Optical Character
Recognition (OCR). In fact, most OCR packages on the market work
only on bi-level (black & white) images. The simplest way to
use image binarization is to choose a threshold value, and classify
all pixels with values above this threshold as white, and all other
pixels as black. The problem then is how to select the correct
threshold. In many cases, finding one threshold compatible to the
entire image is very difficult, and in many cases even
impossible.
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ThresholdingThresholding produces a binary image from a
grey-scale or colour image by setting pixel values to 1 or 0
depending on whether they are above or below the threshold value.
This is commonly used to separate or segment a region or object
within the image based upon its pixel values, as shown in following
Figure :Thresholding for object identication
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ThresholdingIn its basic operation, thresholding operates on an
image I as follows:
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ThresholdingIn Matlab, this can be carried out using the
function im2bw and a threshold in the range 0 to 1.The im2bw
function automatically converts colour images (such as the input in
the example) to grayscale and scales the threshold value supplied
(from 0 to 1) according to the given range of the image being
processed. For grey-scale images, whose pixels contain a single
intensity value, a single threshold must be chosen. For colour
images, a separate threshold can be dened for each channel (to
correspond to a particular colour or to isolate different parts of
each channel).
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ThresholdingIn many applications, colour images are converted to
grey scale prior to thresholding for simplicity.Thresholding is the
work-horse operator for the separation of image foreground from
background. One question that remains is how to select a good
threshold. This topic is addressed on image
segmentation.Thresholding of a complex image
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SegmentationImage segmentation is the process of partitioning a
digital image into multiple segments (sets of pixels, also known as
superpixels). The goal of segmentation is to simplify and/or change
the representation of an image into something that is more
meaningful and easier to analyze.Image segmentation is typically
used to locate objects and boundaries (lines, curves, etc.) in
images. More precisely, image segmentation is the process of
assigning a label to every pixel in an image such that pixels with
the same label share certain visual characteristics.The result of
image segmentation is a set of segments that collectively cover the
entire image, or a set of contours extracted from the image.Each of
the pixels in a region is similar with respect to some
characteristic or computed property, such as color, intensity, or
texture.
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SegmentationThe Applications of Image Segmentation are:
Medical imaging: Locate tumors, Measure tissue volumes.Object
detection: Locate objects in satellite images (roads,
forests).Recognition Tasks: Fingerprint recognition, Iris
recognition.Traffic control systems.Content-based image
retrieval.
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SegmentationIn general, completely independent segmentation is
one of the most difcult tasks in the design of computer vision
systems and remains an active eld of image processing and machine
vision research. Segmentation occupies a very important role in
image processing because it is so often the vital rst step which
must be successfully taken before subsequent tasks such as feature
extraction, classication, description, etc. can be sensibly
attempted. After all, if you cannot identify the objects in the rst
place, how can you classify or describe them?The basic goal of
segmentation, then, is to partition the image into mutually
exclusive regions to which we can subsequently attach meaningful
labels. The segmented objects are often termed the foreground and
the rest of the image is the background.
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SegmentationNote that, for any given image, we cannot generally
speak of a single, correct segmentation. Rather, the correct
segmentation of the image depends strongly on the types of object
or region we are interested in identifying. What relationship must
a given pixel have with respect to its neighbours and other pixels
in the image in order that it be assigned to one region or
another?This really is the central question in image segmentation
and is usually approached through one of two basic
routes:Edge/boundary methods This approach is based on the
detection of edges as a means to identifying the boundary between
regions. As such, it looks for sharp differences between groups of
pixels.Region-based methods This approach assigns pixels to a given
region based on their degree of mutual similarity.
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Use of image properties and features in segmentationIn the most
basic of segmentation techniques (intensity thresholding), the
segmentation is used only on the absolute intensity of the
individual pixels. However, more sophisticated properties and
features of the image are usually required for successful
segmentation. There are three basic properties/qualities in images
which we can exploit in our attempts to segment images :Colour is,
in certain cases, the simplest and most obvious way of
discriminating between objects and background. Objects which are
characterized by certain colour properties (i.e. are conned to a
certain region of a colour space) may be separated from the
background. For example, segmenting an orange from a background
comprising a blue tablecloth is a trivial task.
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Use of image properties and features in segmentationTexture is a
somewhat loose concept in image processing. It does not have a
single denition but, nonetheless, accords reasonably well with our
everyday notions of a rough or smooth object. Thus, texture refers
to the typical spatial variation in intensity or colour values in
the image over a certain spatial scale. A number of texture metrics
are based on calculation of the variance or other statistical
moments of the intensity over a certain neighbourhood / spatial
scale in the image. Motion of an object in a sequence of image
frames can be a powerful cue. When it takes place against a
stationary background, simple frame-by-frame subtraction techniques
are often sufcient to yield an accurate outline of the moving
object.In summary, most segmentation procedures will use and
combine information on one of more of the properties colour,
texture and motion.
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Problems with thresholdingThere are several serious limitations
to simple thresholding:there is no guarantee that the thresholded
pixels will be contiguous (thresholding does not consider the
spatial relationships between pixels);it is sensitive to accidental
and uncontrolled variations in the illumination eld;it is only
really applicable to those simple cases in which the entire image
is divisible into a foreground of objects of similar intensity and
a background of distinct intensity to the objects.
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Region growing and region splittingRegion growing is an approach
to segmentation in which pixels are grouped into larger regions
based on their similarity according to predened similarity
criteria. It should be apparent that specifying similarity criteria
alone is not an effective basis for segmentation and it is
necessary to consider the adjacency spatial relationships between
pixels. In region growing, we typically start from a number of seed
pixels randomly distributed over the image and append pixels in the
neighbourhood to the same region if they satisfy similarity
criteria relating to their intensity, colour or related statistical
properties of their own neighbourhood.
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Region growing and region splittingSimple examples of similarity
criteria might be:(1) the absolute intensity difference between a
candidate pixel and the seed pixel must lie within a specied
range;(2) the absolute intensity difference between a candidate
pixel and the running average intensity of the growing region must
lie within a specied range;(3) the difference between the standard
deviation in intensity over a specied local neighbourhood of the
candidate pixel and that over a local neighbourhood of the
candidate pixel must (or must not) exceed a certain threshold this
is a basic roughness/smoothness criterion.
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Region growing and region splitting
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Edge DetectionEdge detection is one of the most important and
widely studied aspects of image processing.If we can nd the
boundary of an object by locating all its edges, then we have
effectively segmented it. Supercially, edge detection seems a
relatively straightforward affair. After all, edges are simply
regions of intensity transition between one object and another.
However, despite its conceptual simplicity, edge detection remains
an active eld of research. Most edge detectors are fundamentally
based on the use of gradient differential lters.
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Edge DetectionTrying to actually nd an edge, several factors may
complicate the situation. The rst relates to edge strength or, if
you prefer, the context how large does the gradient have to be for
the point to be designated part of an edge? The second is the
effect of noise differential lters are very sensitive to noise and
can return a large response at noisy points which do not actually
belong to the edge. Third, where exactly does the edge occur? Most
real edges are not discontinuous; they are smooth, in the sense
that the gradient gradually increases and then decreases over a
nite region.
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Edge DetectionThe Canny edge detector is an edge detection
operator that uses a multi-stage algorithm to detect a wide range
of edges in images. It was developed by John F. Canny in 1986.
Canny's aim was to discover the optimal edge detection algorithm.
In this situation, an "optimal" edge detector means:Good detection
the algorithm should mark as many real edges in the image as
possible.Good localization edges marked should be as close as
possible to the edge in the real image.Minimal response a given
edge in the image should only be marked once, and where possible,
image noise should not create false edges.
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Edge DetectionThe Canny edge detector
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Edge DetectionThe Canny edge detector
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Edge DetectionTypes of the detected edges:A viewpoint
independent edge typically reflects inherent properties of the
three-dimensional objects, such as surface markings and surface
shape.A viewpoint dependent edge may change as the viewpoint
changes, and typically reflects the geometry of the scene, such as
objects occluding one another.
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Edge Detection The purpose of using edge detection methods:The
purpose of detecting sharp changes in image brightness is to
capture important events and changes in properties of the world.In
the ideal case, the result of applying an edge detector to an image
may lead to a set of connected curves that indicate the boundaries
of objects, the boundaries of surface markings as well as curves
that correspond to discontinuities in surface orientation. Thus,
applying an edge detection algorithm to an image may significantly
reduce the amount of data to be processed and may therefore filter
out information that may be regarded as less relevant, while
preserving the important structural properties of an image.
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THE END