Machine Learning vs Computer Vision

Introduction

Imagine this. You have a DSLR camera with you and you went on a trip to a beautiful island. Of course, you would want to click some pictures.

Now, you upload those memories on to your desktop and your computer automatically understands what’s in those pictures!

That’s where Computer Vision comes in!

Now you want to categorize your pictures according to the objects (beaches, trees, animals) in them.

Your smart desktop uses Machine Learning algorithms to identify, interpret and make decisions based on the information.

Wait, how is this possible?

Read along.

Computer Vision and Machine Learning are two important branches of Artificial Intelligence (AI) which are changing the way we live and work.

Computer Vision aims to make computers understand visual data the way humans do.

On the other hand, Machine Learning trains computers to learn from data and make decisions based on the information.

Both fields might be related to AI. But they are vastly different in their goals and applications in various sectors.

In this blog, we will dig deeper into these two fields of AI. We will also explore the similarities and differences between Computer Vision and Machine Learning.

But first, let’s define AI.

What is Artificial Intelligence?

Artificial Intelligence is a technology built with the objective of developing intelligent systems.

These systems can perform operations which would normally require human intelligence.

This includes

a. decision making,

b. speech recognition,

c. image recognition,

d. language translation

and much more.

The advent of AI dates to 1956, when John McCarthy first coined the term.

In the decades since, AI has made tremendous strides. It has become a driving force behind many industries, led by the data revolution.

Not only the industries, even our homes are powered with smart IoT (Internet of Things) devices.

With the power of AI, you can control various devices in your home just by using your voice.

For example, you can turn off the lights, adjust the temperature, or even start the coffee maker all without getting up from your couch.

How does this work?

AI-powered smart homes use machine learning algorithms and natural language processing to understand and respond to your commands.

Let’s have a look at some industry wide AI technologies in application.

1. Natural Language Processing (NLP): NLP enables computers to understand, interpret, and generate human language in a way that is both accurate and natural.

Now you can automate responses to customer queries easily!

2. Predictive Maintenance: Using ML algorithms, this technology analyses data from sensors and can predict machine failures beforehand, so that you can schedule maintenance. Say goodbye to downtimes!

3. Deep Learning: Analyzing enormous amounts of data using neural networks helps immensely in industries such as finance, marketing and healthcare in fraud detection, customer segmentation and disease diagnosis respectively.

4. Computer Vision: Computer Vision is being used in retail, transportation for object detection, image classification and video analysis.

5. Generative Adversarial Networks (GANs): Used for generating images or text, GANs are being used in gaming, entertainment and marketing to create realistic and immersive gaming environments, movie trailers, and personalized ads.

Now let’s dig into the exciting field of machine learning and learn how does it work.

What is Machine Learning?

Machine Learning is a branch of AI which aims to teach computers to work with data.

It does so by

a. understanding,

b. analyzing, and

c. interpreting

data without the explicit need for programming.

To achieve this goal, various algorithms and statistical models are used for data analysis.

The goal of machine learning is that systems not only learn from data but are also able to make predictions about the future outcomes with a high degree of accuracy.

Types of Machine Learning Algorithms

There are several types of machine learning algorithms. They are based on the level of human intervention needed.

1. Supervised Learning: The machine learns about data under the supervision of a human. The human supplies labeled examples of input/output pairs. It is then applied to new data by the machine.

2. Unsupervised Learning: Unsupervised ML leaves it to the system to find patterns and structures in the unlabeled dataset.

3. Semi-supervised learning: The machine is provided with both labeled and unlabeled datasets. It uses the labeled ones to make decisions about the unlabeled data.

4. Reinforcement Learning: The machine learns to make decisions by interacting with an environment. It receives feedback in the form of rewards or penalties. This, in turn, helps it to make better decisions.

5. Deep Learning: Deep Learning involves the usage of neural networks to analyze and interpret data.

Apart from the ones mentioned above, there are other types of machine learning. Some of them are:

a. transfer learning,

b. active learning,

c. generative models, and

d. online learning.

Each can be expressed as a combination of one or more types of ML.

Applications of Machine Learning

Machine Learning finds ample applications in various tasks and industries.

Few major applications include,

1. Customer segmentation: Customer data can be analyzed using algorithms. The model then divides the data into similar groups based on various characteristics. They could include behavior, demographics, or purchase history.

2. Fraud detection: Algorithms can be used to analyze patterns in transaction data. They could identify unusual behavior that might indicate fraud.

3. Predictive maintenance: Sensor data analysis could be done from equipment and predict when maintenance will be needed.

4. Personalized recommendations: Algorithms are used to analyze data on customer preferences and behavior. This helps make personalized product or content recommendations.

Understanding Deep Learning

Deep learning uses neural networks to analyze and interpret data.

Neural Networks are based on the structure and function of the human brain and simulate the neural networks in the brain.

They are made up of multiple interconnected layers starting from the input layer, hidden layers, and an output layer.

In deep learning, neural networks are mathematical functions. These functions receive an input, perform computations, and produce an output.

Complexity increases as we proceed from the lower layers of a neural network to higher ones.

The lower layers learn simple features and patterns. The higher layers learn the complex representations of data.

Due to the extensive complexity of the deep learning model, it finds uses in

a. image and speech recognition,

b. natural language processing,

c. decision making, and

d. computer vision

to name a few.

There are several types of neural networks used in deep learning, namely

1. Convolutional Neural Networks

2. Recurrent Neural Networks

3. Feedforward Neural Networks

4. Generative Adversarial Networks

5. Autoencoders 

Now let’s proceed to another exciting subfield of AI, that is, Computer Vision.

What is Computer Vision?

Computer Vision (CV) is another subfield of artificial intelligence which enables computers to understand visual data.

Visual Data consists of

a. 2D images and videos

b. 3D images and videos

c. Sensor Data

For ease of understanding, if we consider machine learning to mimic the human brain, computer vision mimics the human eyes.

It helps the computer recognize people, living beings and objects. It also detects motion and understands the context of images and videos.

In this branch of AI, techniques like

a. image processing,

b. pattern recognition,

c. object detection,

d. object tracking

are used amongst others. These help analyze and interpret visual data accurately.

Techniques used in Computer Vision

Computer vision relies on techniques to extract features from images.

1. Image processing: Filtering, thresholding, edge detection and segmentation are some of the techniques used to extract low level features from images.

2. Feature-based techniques: They help extract features like edges, corners and blobs from an image which are then used for object tracking and recognition.

3. Structure-based techniques: These analyze the 3D structure of an image or a motion scene to create a 3D representation of the scene.

4. Template Matching: As the name suggests, the target image is compared with a set of templates to find the best match.

5. Optical Flow: It estimates the movement or flow of objects in an image or video.

Applications of Computer Vision

Computer Vision has found applications in traffic control, security, and surveillance, augmented reality (AR) and virtual reality (VR), self-driving cars to name a few.

1. Traffic Control: CV algorithms can detect and track vehicles, pedestrians, and other objects. They then use this information to control traffic signals and make real-time adjustments to improve traffic flow and safety.

2. Security and Surveillance: Algorithms can be trained to identify and track objects, recognize faces, and detect unusual behavior, such as attempts to breach a perimeter.

3. AR/VR: Computer vision is used to track and integrate virtual objects with the real-world environment.

4. Self-driving cars: Computer vision is used to perceive and understand the driving environment. Algorithms analyze data from cameras, LiDAR, Radar, and other sensors to detect and track objects such as other vehicles, pedestrians, and road signs.

The Role of Deep Learning in Computer Vision

Deep Learning is generally used in Computer Vision through Convolutional Neural Networks (CNNs) to automatically learn features and representations from visual data.

This process is as follows:

1. An image or any other visual data is fed into the network.

2. The network applies certain layers to extract features from the visual data.

3. These features are then passed on through the interconnected layers which make the final decision or prediction based on the same.

The above process is part of the training given to the CNNs using a labelled dataset of images.

Owing to their complexity, deep learning models are inherently able to learn rich representations of images.

This leads to better performance when compared to traditional methods. Traditional methods tend to have lower accuracy and require a lot of fine-tuning of parameters for specific tasks.

Deep Learning in Computer Vision is used in object recognition, image classification, object detection, semantic segmentation, and medical image analysis.

Machine Learning vs Computer Vision: The overlap

Now that we have studied Machine Learning and Computer Vision in detail, let us focus on the differences in both the fields of artificial intelligence.

Computer Vision is a field of artificial intelligence that aims to make computers understand and interpret the world as human eyes perceive the same.

The area of focus is extensively on deducing meaningful information from any kind of visual data – images and videos.

Machine Learning, on the other hand, is a subset of artificial intelligence whose intent is to train computers to learn from data, without being explicitly programmed.

This includes identifying patterns from a given dataset and making decisions based on the same.

The fundamental difference arises in the input data of both fields.

Machine Learning focusses on making predictions or decisions by learning from structured or unstructured data while computer vision enables systems to analyze visual information.

Below is a table which paints a better picture with respect to the differences between Machine Learning and Computer Vision.

The Relationship between Computer Vision and Machine Learning 

Machine learning models are used to enhance computer vision techniques and improve the performance of computer vision systems.

Machine learning is based on automation. This allows for greater accuracy in extracting features from visual data.

This, in turn, helps develop models which are more robust than the traditional techniques.

One such example is Object Detection and Tracking.

ML can be used to train models to detect objects in images and videos, such as cars, people, traffic, and obstacles, even when they are partially visible, rotated or viewed from a different angle.

Another example could be Image Segmentation.

Through supervised learning techniques, models can be trained to identify the labels on images, such as ‘cars,’ ‘road,’ ‘sky’ etc.

This helps in generating environment maps with detail. It also improves the performance of other CV tasks like object detection.

Machine Learning Powered Computer Vision in Action

Machine Learning techniques are revolutionizing the field of Computer Vision. They are playing a significant role in shaping the future of technology and advancing the field of Artificial Intelligence.

The techniques discussed in the last section are being applied in real world scenarios, such as:

1. Self-driving cars: ML-powered Computer Vision enables the car to understand its environment, such as detecting other vehicles, pedestrians, traffic lights and road signs.

For example, Waymo, Alphabet's autonomous vehicle company, uses machine learning techniques to train their cars to recognize and respond to different road conditions and situations.

The automotive industry, especially makers such as Tesla, Volvo, BMW, and Audi, use computer vision in their self-driving cars.

Computer vision enables these self-driven vehicles to spot objects, identify lane markings, and understand traffic signals for safe driving.

2. Security and Surveillance: Object and face detection using ML powered Computer Vision can be used to track the motion of people and detect any suspicious behavior.

Mega conglomerates like Fujitsu, Walmart, etc. have set up independent research labs to facilitate AI in behavioral analytics in their retail stores. 

3. Medical Imaging: CT scans, X-rays and MRI images can be analyzed using ML powered Computer Vision.

This can ease the detection of tumors, blood vessels to improve accuracy in diagnosis.

4. Virtual Reality and Augmented Reality: Real time object detection and tracking comes into play while detecting motions and gestures of the user.

For example, Microsoft HoloLens uses machine learning to understand the user's gaze and gestures, allowing them to interact with the virtual objects in a more natural way.

The future of machine learning powered computer vision might foresee the combination of various other technologies.

Transformers, a deep learning architecture for computer vision algorithms is emerging.

Technologies like AutoML, or automated Machine Learning could be used in computer vision.

Source: The Future of Computer Vision | NVIDIA Technical Blog

Conclusion 

Computer Vision and Machine Learning are important aspects of Artificial Intelligence.

Leveraging these technologies by using the competencies of each other can result in increase in efficiency and robustness.

This could drive results with more precision and accuracy and save time and resources.

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