Machine Vision

Image Segmentation

Instructor : PhD, Associate Professor Leyla Muradkhanli

 Image Segmentation
Image segmentation is a fundamental task in computer vision
that involves dividing an image into distinct and meaningful
regions or segments.
The goal of image segmentation is to simplify and represent
an image in a more understandable and analyzable manner,
by separating different objects, regions, or structures within
the image.
 Image Segmentation
Image segmentation process is crucial for various
applications, including object recognition, scene
understanding, medical imaging, robotics, and more.
 Types of Image Segmentation

• Semantic Segmentation
• Instance Segmentation
• Panoptic Segmentation
 Types of Image Segmentation
Semantic Segmentation
Semantic segmentation is a type of image segmentation that involves labeling
each pixel in an image with a corresponding class label with no other information
or context taken into consideration.
The goal is to assign a label to every pixel in the image, which provides a dense
labeling of the image. The algorithm takes an image as input and generates
a segmentation map where the pixel value (0,1,...255) of the image is transformed
into class labels (0,1,...n). It is useful in applications where
identifying the different classes of objects on the road is
important.
 Types of Image Segmentation
Instance Segmentation
Instance segmentation is a type of image segmentation that involves detecting
and segmenting each object in an image. It is similar to object detection but
with the added task of segmenting the object’s boundaries. The algorithm has
no idea of the class of the region, but it separates overlapping objects. Instance
segmentation is useful in applications where individual objects need to be
identified and tracked.
 Types of Image Segmentation
Panoptic Segmentation
Panoptic segmentation is a combination of semantic and instance
segmentation. It involves labeling each pixel with a class label and identifying
each object instance in the image. This mode of image segmentation provides
the maximum amount of high-quality granular information from machine
learning algorithms. It is useful in applications where the computer vision model
needs to detect and interact with different objects in its environment, like an
autonomous robot.
 Image Segmentation Techniques
1. Edge-Based Segmentation
Edge-based segmentation is a popular image processing
technique that identifies the edges of various objects in
a given image. It helps locate features of associated
objects in the image using the information from the
edges. Edge detection helps strip images of redundant
information, reducing their size and facilitating analysis.
 Image Segmentation Techniques
Edge-based segmentation algorithms identify edges based
on contrast, texture, color, and saturation variations. They
can accurately represent the borders of objects in an image
using edge chains comprising the individual edges.
 Edge Segmentation
An example of edge detection using the Canny edge detection
algorithm, one of the most popular edge detection algorithms
is shown below.
 Image Segmentation Techniques
2. Threshold-Based Segmentation
Thresholding is the simplest image segmentation method, dividing pixels based
on their intensity relative to a given value or threshold. It is suitable for
segmenting objects with higher intensity than other objects or backgrounds.
The threshold value T can work as a constant in low-noise images. In some cases,
it is possible to use dynamic thresholds. Thresholding divides a grayscale image
into two segments based on their
relationship to T, producing a binary image.
 Thresholding
Thresholding is one of the easiest methods of image
segmentation where a threshold is set for dividing pixels into
two classes. Pixels that have values greater than the threshold
value are set to 1 while pixels with values lesser than the
threshold value are set to 0.
The image is thus converted into a binary map, resulting in the
process often termed binarization. Image thresholding is very
useful in case the difference in pixel values between the two
target classes is very high, and it is easy to choose an average
value as the threshold.
 Thresholding

Thresholding is often used for image binarization so that further

algorithms like contour detection and identification that work
only on binary images can be used.
 Image Segmentation Techniques
3. Region-Based Segmentation
Region-based segmentation is a technique used in image processing to divide an
image into regions based on similarity criteria, such as color, texture, or intensity.
The method involves grouping pixels into regions or clusters based on their
similarity and then merging or splitting regions until the desired level of
segmentation is achieved.
 Image Segmentation Techniques
4. Cluster-Based Segmentation
Clustering algorithms are unsupervised classification algorithms that
help identify hidden information in images. They augment human
vision by isolating clusters, shadings, and structures.
The algorithm divides images into clusters of
pixels with similar characteristics, separating
data elements and grouping similar elements
into clusters.
 Deep Learning-based methods
Semantic segmentation models provide segment maps as outputs
corresponding to the inputs they are fed.
These segment maps are often n-channeled with n being the
number of classes the model is supposed to segment. Each of these
n-channels is binary in nature with object locations being “filled”
with ones and empty regions consisting of zeros. The ground truth
map is a single channel integer array the same size as the input and
has a range of “n”, with each segment “filled” with the index value of
the corresponding classes (classes are indexed from 0 to n-1).
 Deep Learning Techniques

The basic architecture of the neural network model for image segmentation
 Applications of Image Segmentation
Image segmentation has a wide range of applications in various fields,
including medical imaging, robotics, autonomous vehicles, and surveillance.
Here are some examples of how image segmentation is used in different
fields:
Medical imaging: Image segmentation is widely used in medical imaging for
tasks such as tumor detection, organ segmentation, and disease diagnosis.
Accurate segmentation is essential for treatment planning and monitoring
disease progression.
Robotics: Image segmentation is used in robotics for object recognition and
manipulation. For example, robots can use segmentation to recognize and
grasp specific objects, such as tools or parts, in industrial settings.
 Applications of Image Segmentation
Autonomous vehicles: Image segmentation is essential for the
development of autonomous vehicles, allowing them to detect and
classify objects in their environment, such as other vehicles, pedestrians,
and obstacles. Accurate segmentation is crucial for safe and reliable
autonomous navigation.
Surveillance: Image segmentation is used in surveillance for detecting
and tracking objects and people in real-time video streams.
Segmentation can help to identify and classify objects of interest, such
as suspicious behavior or potential threats.
 Applications of Image Segmentation
Agriculture: Image segmentation is used in agriculture for crop
monitoring, disease detection, and yield prediction. Accurate
segmentation can help farmers make informed decisions about crop
management and optimize crop yields.
Art and design: Image segmentation is used in art and design for tasks
such as image manipulation, color correction, and style transfer.
Segmentation can help to separate objects or regions of an image and
apply different effects or modifications to them.