First of all, you might be wondering
- What exactly is image segmentation and how does it work? Well, long
story short, Image segmentation is a prime domain of computer vision, backed by
a huge amount of research involving both image processing-based algorithms and
learning-based techniques. In other words, image segmentation is the process of
partitioning a digital image into multiple image segments, also known as
image regions or image objects (sets of pixels). 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.
Here is an example of how Image Segmentation works on a sample picture:
As you can see, the machine automatically
finds a lot of things that can be distinguished by the human eye, things such
as cars, traffic lights, or even pedestrians. This example is, if you will, the
most primitive way to show this technology’s true power and capabilities.
Because this kind of technology has been
used for a while now, engineers and programmers have managed to use this
algorithm in multiple ways and uses. Some notable examples are:
- Self-driving cars: Image
segmentation can be used in self-driving cars for giving easy distinctions
between various objects. Be it traffic signals, signboards, humans, and cars.
It can help the driving instruction algorithm to better assess the surrounding
before generating the next instruction.
- Circuit Board Defect Detection: A
company has to bear the responsibility of defected devices. If a camera backed
with an Image Segmentation model keeps scanning for defects produced in the
final product, a lot of money and time can be saved in fixing a defective
device.
- Face detection: Nowadays, we have
observed that the majority of cameras in phones support portrait mode. Portrait
mode is technically an outcome of Image Segmentation. Apart from this, security
surveillance will be much more effective when the faces are distinguishable
from noisy objects.
- Medical Imaging: Image segmentation can be used to extract clinically relevant information from medical reports. For example, image segmentation can be used to segment tumors.
For our project, we want to test and see
the efficacy of the first example that we have shown above, the standard object
detection system. For this, we will use Python, along with some
specialized libraries (numpy, scipy, pillow, cython, matplotlib,
scikit-image, tensorflow, keras, opencv, h5py, imgaug and jpython).
The output given after the running will
consist of the image that we put to the test, modified so that every object
found is highlighted with a specific colour, and named accordingly. Also, the
exact number of objects found is going to be output as well.
The main technology used for this to be
working is called Mask R-CNN (a Region-Based Convolutional Neural Network),
which is a type of artificial neural network used in image recognition and
processing that is optimized to process pixel data. Therefore, Convolutional
Neural Networks are the fundamental and basic building blocks for the computer
vision task of image segmentation (CNN segmentation).
More details will be presented in the
upcoming weeks. Thank you for your time!
Bibliography:
https://data-flair.training/blogs/image-segmentation-machine-learning/
https://viso.ai/deep-learning/mask-r-cnn/
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