What is Image Processing and Why is it Important?
Share On
Trajectus
5 Minute read
5 Minute read
Image Processing (IP) is a type of computer technology that
allows us to process, analyse, and extract information from
images.
It's one of the fastest-growing technologies, but it's changed
dramatically over time. Today, image processing is used by a
variety of businesses and organisations for a variety of
purposes, including visualisation, image information extraction,
pattern recognition, classification, segmentation, and more.
Analogue and digital image processing are the two main types of
image processing. The analogue IP approach is used to process
hard copies such as scanned photos and prints, with the outputs
often being images. The Digital IP, on the other hand, is used
to manipulate digital images using computers; the outputs are
usually image-related information, such as data on features,
attributes, bounding boxes, or masks.
Here are a few examples of common use cases for machine learning image processing techniques:
- Medical Imaging / Visualization: Assist medical practitioners in more quickly interpreting medical imaging and diagnosing irregularities.
- Law Enforcement & Security: Aid in surveillance and biometric authentication for law enforcement and security.
- Self-Driving Technology: Self-driving technology can help with object detection and replicating human visual cues and interactions.
- Gaming: Improving game experiences in augmented reality and virtual reality.
- Image Restoration & Sharpening: Enhance image quality or apply popular filters, for example.
- Pattern Recognition: Classify and recognise objects/patterns in photographs, as well as comprehend contextual information.
- Image Retrieval: Recognize images for faster image retrieval from vast datasets.
Working of Machine Learning Image Processing
To begin, ML algorithms require a large amount of high-quality
data in order to learn and anticipate extremely accurate
outcomes. As a result, we'll need to make sure the images are
well-processed, annotated, and generic enough to be used in
machine learning image processing. This is where Computer Vision
(CV) comes in; it's a field concerned with machines' ability to
comprehend image data. We can analyse, load, transform, and
modify photos with CV to create a perfect dataset for the
machine learning algorithm.
- All of the images are being converted to the same format.
- Cropping out the parts of an image that aren't needed.
- They are converted into numbers so that algorithms can learn from them (array of numbers).
An input image is seen by computers as an array of pixels, with
the number of pixels varying depending on the image resolution.
It will see height * width * dimension based on the image
resolution. An image of a 6 × 6 x 3 array of an RGB matrix (3
refers to RGB values) and a 4 x 4 x 1 array of a grayscale
image, for example.
These features (processed data) are then employed in the next
phase, which involves selecting and developing a
machine-learning algorithm to classify unknown feature vectors
from a large library of feature vectors with known
classifications. We'll need to pick a good algorithm for this;
some of the most common ones include Bayesian Nets, Decision
Trees, Genetic Algorithms, Nearest Neighbors, and Neural Nets,
among others.
Libraries and Frameworks for Machine Learning Image Processing
-
OpenCV-Python:OpenCV-Python is a Python bindings library forsolving computer vision challenges. It's straightforward and simple to useFeatures:
- Huge library of image processing algorithms
- Open Source + Great Community
- Works on both images and videos
- Java API Extension
- Works with GPUs
- Cross-Platform
-
Tensorflow:Tensorflow is a prominent end-to-end machine learning programming framework created by Google.Features:
- Wide range of ML, NN Algorithms
- Open Source + Great Community
- Work on multiple parallel processors
- GPU Configured
- Cross-Platform
-
PyTorch:One of the most popular neural network frameworks for researchers is PyTorch (by Facebook). When compared to other ML libraries, it is more pythonic.Features:
- Distribution Training
- Cloud Support
- Open Source + Great Community
- Works with GPUs
- Production Ready
-
Caffe:Caffe is a deep learning framework that focuses on expression, speed, and modularity. Berkeley AI Research (BAIR) and community collaborators are working on it.Features:
- Open Source + Great Community
- C++ Based
- Expressive Architecture
- Easy and Faster Execution
-
EmguCV:EmguCV is a cross-platform CV editor. The OpenCV image processing library is wrapped in a Net wrapper.Features:
- Open Source and Cross-Platform
- Working with .NET compatible languages – C #, VB, VC ++, IronPython, etc.
- Compatible with Visual Studio, Xamarin Studio and Unity
-
MATLAB Image Processing Toolbox:Image Processing Toolbox programmes from MATLAB allow you to automate standard image processing procedures. You can segment picture data interactively, compare image registration algorithms, and handle big data sets in batches.Features:
- Wide range of Deep Learning Image Processing Techniques
- CUDA Enabled
- 3D Image Processing Workflows
-
WebGazer:WebGrazer is a JS-based eye tracking framework that uses regular webcams to infer online visitors' eye-gaze locations on a page in real-time.Features:
- Multiple gaze prediction models
- Continually supported and Open Source for 4+ years
- No special hardware; WebGazer.js uses your webcam
-
Apache Marvin-AI:Marvin-AI is an open-source AI platform that enables the delivery of complicated solutions with a high-scale, low-latency, language-agnostic, and standardised architecture while simplifying exploitation and modelling.Features:
- Open Source and Well documented
- Easy to use CLI
- Multi-threaded image processing
-
MIScnn:MIScnn is a deep-learning-based open-source system for medical image segmentation.Features:
- Open Source and Well Documented
- Creation of segmentation pipelines
- Decently pre-processing and post-processing tools
- CNN Implementation
-
Kornia:Kornia is an open-source differentiable computer vision library built on PyTorch.Features:
- Rich and low-level image processing techniques
- Open Source and Great Community
- Differentiable programming for large applications
- Production Ready, JIT Enabled
-
VXL:VXL (the Vision-something-Libraries) is a set of C++ libraries aimed towards computer vision research and development.Features:
- Open Source
- 3D Image Processing Workflows
- Designing a graphical user interface
Categories
Deep Learning Image Processing
Deep learning networks are currently used in a number of machine
learning image processing approaches. These are a type of framework
that learns from data and builds models in the same way that the
human brain does. Convolution Neural Networks, or CNNs, are a
well-known neural network architecture that has made substantial
advances in picture data analysis. Let's take a look at how CNNs are
used to generate state-of-the-art models from photos with various
image processing jobs.
Three basic layers make up the convolutional neural network:
- Convolutional Layer
- Pooling Layer
- Fully Connected Layer
Convolutional layer:
The convolutional layer is the brains of CNNs; it handles the
majority of the work in detecting the characteristics in a given
image. Then, in the convolution layer, we take square blocks of the
input image of a random size and use the dot product with the filter
(random filter size). The convolution layer output will be high if
the two matrices (the patch and the filter) have high values in the
same places (which gives the bright side of the image). It will be
low if they don't (the dark side of the image). In this method, we
can tell whether the pixel pattern in the underlying image matches
the pixel pattern given by our filter based on a single value of the
dot product output.
Pooling Layer:
We have many feature maps when we use the convolutional layers to
detect the features. When the convolutional operation is applied
between the input image and the filter, these feature maps appear.
As a result, we'll need another procedure to downsample the image.
As a result, the "pooling" technique is used to minimise the pixel
values in the arrays, making the learning process easier for the
network. They operate independently on each depth slice of the input
and spatially resize it using two separate operations:
Max Pooling
returns the largest value from the picture covered by the Kernel's
array.
Average Pooling
Returns the average of all the values in the picture covered by
the Kernel's array.
Fully Connected Layer (FC):
The fully connected layer (FC) works with a flattened input, meaning
that each input is connected to all neurons. These are typically
employed at the network's end to connect the hidden layers to the
output layer, which aids in class score optimization.
What is a deep neural network, and how does it work?
A deep neural network (DNN), or deep net for short, is a neural
network with a certain amount of complexity, usually at least two
layers. Deep nets use advanced math modelling to process data in
complex ways.
To properly grasp deep neural networks, though, it's best to think
of them as a process. Before deep nets, a few things had to be
built.
Machine learning had to be built first. ML is a framework for
automating (through algorithms) statistical models, such as linear
regression models, in order to improve prediction accuracy. A single
model that makes predictions about something is referred to as a
model. Those forecasts are reasonably accurate. A learning model
(machine learning) takes all of its incorrect predictions and
adjusts the weights within the model to develop a model that makes
fewer errors.
Artificial neural networks arose from the learning component of the
modelling process. The hidden layer is used by ANNs to store and
evaluate how important each of the inputs is to the output. The
hidden layer retains information about the relevance of inputs and
forms links between the importance of different combinations of
inputs.
Deep neural networks, on the other hand, make use of the ANN
component. They argue that if this improves a model so well—because
each node in the hidden layer creates both associations and grades
the value of the input in determining the output—then why not stack
more and more of these on top of each other to get even more benefit
from the hidden layer?
As a result, there are several hidden levels in the deep net. A
model's layers are said to be 'deep' if they are numerous layers
deep.
Neural Network architecture
Deep convolutional neural networks can be thought of as layers of
neurons, each of which can be classified into distinct categories
based on its connectivity structure. Convolutional, pooling, and
fully linked layers are the most prevalent. In general, each layer
is associated with a unique set of parameters, which together make
up the network's full collection of connection weights and biases.
Hyperparameters are the parameters that are employed particularly
during the training method.
The numerous layer types, each with its own set of parameters, as
well as the hyperparameters and their impact on training quality and
speed, make selecting a high-performing architecture difficult. As a
result, prior empirical evidence (i.e., the performance of
previously reported designs on structurally similar issues) as well
as domain expertise and insight into the nature of the challenge at
hand are critical in guiding design decisions.
Convolutional blocks are the building blocks of the network
architecture we built. With the exception of the amount of kernels,
every convolutional block has the identical hyperparameters and is
made up of two sets of convolutional layers, batch normalisation,
and rectified linear unit activation. A max-pooling procedure is
used after a convolutional block to minimise the dimensionality of
the modified input.
The final design consists of five convolutional blocks in a row and
max-pooling pairings. With the exception of the last layer, the
number of filters is doubled after each pooling layer. The output of
the last pooling layer is flattened and processed by three fully
connected layers of 4096 neurons, followed by an 83-output soft-max
output layer. Except for the final output, dropout is applied to all
fully connected layers. A Gaussian distribution is used to generate
the initial values for the weights and biases.
Deep learning models must be trained (such as neural networks)
To create an ML model that can forecast customer churn, for example,
data scientists must first define the input features (problem
attributes) the model will take into account when predicting a
result. If we used feature engineering to build a deep learning
model to recognise the difference between a dog and a cat... Imagine
compiling data on the characteristics of billions of cats and dogs
on the earth. We can't build precise features that will work for
every potential image while taking into account issues like
viewpoint-dependent object variability, background clutter, lighting
conditions, and image deformation. There should be another way, and
owing to the nature of neural networks, there is.
Neural networks learn features directly from the data with which
they are trained, eliminating the need for professionals to manually
extract characteristics.
So, the more layers the network has, the greater its predictive
capability.
The black box problem: Improving accuracy
Deep nets improve the accuracy of a model's performance. They enable
a model to accept a collection of inputs and produce a result.
Copying and pasting a line of code for each layer is all it takes to
use a deep net. It makes no difference which machine learning
platform you use; telling the model to use two or 2,000 nodes in
each layer is as easy as typing the numbers 2 or 2000.
However, adopting deep nets raises a question: how do these models
make decisions? The explanation is the ability of a model is greatly
diminished when these simple tools are used.
The Deep Net allows a model to develop its own generalisations and
then store them in a hidden layer called the black box. It's
difficult to investigate the black box. Even if the values in the
black box are known, there is no context through which to understand
them.
Apps for recognising works of art using Neural Networks
Magnus is an app that uses picture recognition to help art
collectors and lovers "navigate the art jungle." When a user takes a
photo of a work of art, the app displays information such as the
creator, title, year of production, size, material, and, most
crucially, the current and historical price. The app also includes a
map containing galleries, museums, and auctions, as well as artworks
that are currently on display.
Magnus gathers information from a database of over 10 million
photographs of artworks, as well as crowdsourced information about
pieces and pricing. Magnus claims on the app's Apple Store page that
Leonardo DiCaprio invested in it.
Apps like Smartify can satiate museum visitors' thirst for
knowledge. Smartify is a museum guide that can be used at a variety
of locations around the world, including the Metropolitan Museum of
Art in New York, the Smithsonian National Portrait Gallery in
Washington, DC, the Louvre in Paris, Amsterdam's Rijksmuseum, the
Royal Academy of Arts in London, and the State Hermitage Museum in
Saint Petersburg, among others.
Conclusion
We've seen how machine learning and deep learning image processing
techniques aid in the development of high-performing models at scale
in this blog. We've gone through some of the most well-known Python,
C++, and C# open source tools that can be used to develop Ml Image
Processing pipelines that pre-process, analyse, and extract data
from photos. Finally, we looked at CNNs, one of the most popular
deep learning image processing architectures, to see how they may be
used to create cutting-edge models on picture data. These had a wide
range of uses, including classification, segmentation, information
extraction, and style transfer, among others.
Related Blogs
Data Science Central
Data Science Central is a platform for people who are interested in both Artificial Intelligence and Data Science. Many top-notch researchers and data practitioners are part of this community.
Towards Data Science
It is a blog powered by the community of data science professionals. They use it to share their ideas and code examples with us.
Reduction in Human Error:
The phrase “human error” was born because humans make mistakes from time to time. Computers, however, do not make these mistakes if they are programmed properly. With Artificial intelligence, the decisions are taken from the previously gathered information applying a certain set of algorithms.