Lately, there has been an increased buzz about the potential applications of computer vision. For the most part, it’s a technology that mimics human vision and is able to perform advanced analysis from imagery. However, many people seem to be confused about the scope of computer vision vs machine learning. That’s understandable because the scope of both technologies overlaps considerably.
In essence, both machine learning and computer vision are part of artificial intelligence (AI), which itself is an umbrella term that covers various other technologies as well. In this specific post, we will be restricting our focus on discussing machine learning vs computer vision, both of which involve the interpretation of visual inputs. We will be looking at the differences, similarities, and applications of the technologies to better understand what each of these technology brings to the table.
Computer vision is a relatively new technology as compared to machine learning. While the researchers started working on the development of computer vision technology back in the 1950s, it wasn’t until a few years back that the technology was matured enough to be used in scientific and everyday use. The earliest application of computer vision was simple two-dimensional imaging, performed by scientists to recognize statistical patterns. It was in 1978, that the MIT AI Lab was able to develop a bottom-up approach for the use of computer vision, which allows the technology to be used in practical everyday applications. Ever since the image recognition technologies have continuously being evolved and are segregated into various categories by use case.
In simple terms, machine learning is about creating intelligent machines that are able to observe, analyze and learn from datasets. The concept behind the technology is the use of statistical learning and optimization methods to enable machines to observe, process, and identify patterns within a dataset. Just like computer vision, it’s a sub-branch of artificial intelligence and is used widely across various industries and applications; including visual interpretation. Machine learning technology relies on data mining to identify complex patterns in the dataset and learn them for future models. Today, machine learning technology is widely used for image recognition, where various supervised, and unsupervised models used the technology to analyze images and identify elements of interest from within the image.
Computer Vision Vs. Machine Learning – The Overlap!
As we have described above, both computer vision and machine learning are branches of artificial intelligence that use intelligent algorithms to observe, analyze, process, and identify patterns from visual data with high speed and accuracy. This also means that the better approach to differentiate the two technologies would be through their use case and not through their differences, since both of these technologies share many commonalities.
To give you an idea, a typical system for both machine learning and computer vision will include much of the same components and requirements including;
- A imaging device – this could be a camera lens or any imaging sensor
- Appropriate lighting for the specific application
- Software with the capabilities to process images and use computer vision or machine learning algorithms
Computer Vision vs. Machine Learning Global Trend – Past 5 Years
As said earlier, machine learning is a much mature and widely implemented technology as compared to computer vision. This also means that more people are aware of the use-case and applications of machine learning technology than computer vision. The same is deduced in the graph below highlighting the past trends of computer vision vs machine learning searches on Google search engine.
Different Computer Vision Applications Using Machine Learning Models
Today, machine learning and computer vision technology are frequently used in conjunction to create strong systems and algorithms capable of fast, and accurate results. Support Vector Machine (SVM), Neural Networks (NN), and Probabilistic graphical models are some examples of machine learning models for computer vision applications. Support vector machine is a supervised classification method that uses machine learning models to observe, analyze, and process datasets. Similarly, the Neural Network method includes layered networks of interconnected processing nodes. The advanced version of Neural Networks (Convolution Neural Network) is specifically used in the image recognition and classification processes.
Below we will be looking at some computer vision applications using machine learning models;
- AI Image Processing
Image processing involved manipulating or transform the image data to either improve the quality of the image or identify required information from it. The field of image processing has advanced considerably and today involved the use of complex machine learning, and computer vision algorithms that enable fast, and accurate processing of large datasets for the identification of hidden patterns. The AI image processing technique is used in various industries including remote sensing, agriculture, 3D mapping, forestry, water management, and others.
Some of the functionalities of AI image processing includes;
– Identifying objects & Patterns
Using machine learning and computer vision algorithm, the AI image processing technique is able to identify patterns, and objects of interest, which are otherwise unrecognizable to the naked eye.
– Image Restoration
Image restoration functionality is meant to enhance the quality of image through transformation techniques for object identification
– Image Tagging & Database Creation
AI image processing can also be used to tag images to facilitate the development of a dataset for easy retrieval and use at a later stage
– Analyze and Alter Images
Automatically measuring, analyzing, and counting image objects through predefined rules
AI Image Processing Services for Enterprises and Businesses
Today, AI image processing technique has become invaluable for cross-industries, both private and public. Since AI image processing techniques can be used to identify patterns that otherwise go undetected through the naked eye, it’s widely implemented in medical, mining, petroleum, security, and other industries.
Some of the industries that greatly relies upon AI image processing includes;
- Life sciences research
- Planning software
- Manufacturing and assembly
- Enterprise resource
- Operations and logistics
- Surveillance and monitoring
- AI-driven Software for Drones
AI-driven software for drones is another high-utility computer vision application that’s powered by machine learning models. AI drones software is a robust and powerful technology with wide-scale application in various industries from aerial mapping, to modeling, and analytics.
Applications of AI drones in real world
AI drones have quickly made inroads into various industries, automating the legacy systems for better efficiency and precision. The computer vision technology powered by robust machine learning algorithms makes it possible for the software to observe, process, analyze, and interpret drone imagery in real-time to identify and extract the required information.
AI drones powered by computer vision and machine learning technology are able to gather high-quality imagery, which is subsequently processed by AI-driven software. This near-real-time image acquisition and processing enable businesses across-industries to improve their operational performance and boost their productivity. These AI drones with the software are perfect tools to streamline operations in various fields including agriculture, terrain mapping, and others.
– Livestock Management
Livestock management is a tedious and resource extensive industry, which requires high input from farmers/ranchers. However, advanced AI drones aided by powerful processing software can help streamline the processes involved in the industry. With AI drones, it’s easier than ever to count cattle and other livestock in real-time, even from remote places.
The technology has helped farmers to significantly improve their operational efficiencies, as well as, lower the cost of managing farms. The technology is also being used to identify unhealthy animals, and thereby, take timely actions to avoid further harm to healthy animals.
– Terrain Mapping
Apart from livestock management, AI drones have also made significant inroads into civil engineering projects. Today, AI drones are excessively used across various civil engineering projects for faster, and precise terrain mapping, which is a prerequisite for projects.
AI drones are equipped with powerful sensors (LiDAR) and navigation systems to surveil the desired terrain and collect required data. The data is then processed using computer vision and machine learning models to create 3D models.
– Precision Agriculture
Precision agriculture is yet another advanced application of AI drones. The agriculture industry is one of the most critical sectors for our survival. However, the sector has been facing various issues due to inefficient processes and legacy systems. To make it worse, the rapid increase in the world’s population is making it hard for the traditional agriculture industry to keep up with the food demand.
AI drones in recent years have become an indispensable tool for the agriculture sector, where the technology is used to automate various processes for increased efficiency, higher productivity, and lower costs. The technology today is used for crop planning, crop harvesting, soil monitoring, livestock management, crop health monitoring, and various other tasks. AI drones with powerful imaging systems are used to collect real-time visual data across vast cultivated areas, which is subsequently processed using computer vision and machine learning-based algorithms. This enables real-time data analysis and processing for farmers, improving efficiencies, and productivity; while lowering the cost of the practice.
- Image Segmentation Services
Image segmentation is the next evolutionary stage of image processing technique, powered by computer vision. The technique is already transforming the industry, paving the way for a high-tech future. The technique is also assisting the tech work to experiment in more challenging industries, making possible things that were once considered miracles.
Today, the image segmentation technique is already used in various futuristic applications including autonomous vehicles, robotics, drones, etc. Autonomous cars for once are the most realistic prospect of image segmentation. The technology has already matured considerably and has been rigorously tested by multiple companies. Once rolled-out for the public, this would significantly change the way humans commute.
- Image Annotation
Lastly, image annotation is yet another advance and highly in-demand application of computer vision with machine learning. The computer vision and machine learning algorithms enable image annotation software to visualize, process, analyze, and segment various objects in visual data (videos, and images). This subsequently helps the user to quickly and accurately annotate images on a massive scale.
Image annotation is also a highly useful technique that’s used for training AI and machine learning algorithms. This subsequently improves the accuracy of pattern recognition of the algorithms and thus helps in improving the quality of results through machine learning or AI algorithms.
Some common types of image annotations used in industry today include;
- Bounding box
- 3D cuboid
- Polygon annotation
- Semantic segmentation
Components Requirement in Computer vision and Machine Vision
Now that we are done with the machine learning vs computer vision, while also looking at the potential applications of machine learning models to power computer vision applications, now let’s move on to discuss some of the components requirements for computer vision and machine vision.
As we have seen above for machine learning vs computer vision comparison, the core concept behind both technologies is quite similar. The key is to develop intelligent machines that are able to imitate human eyes (perhaps perform better than human eyes).
Comparing machine learning vs computer vision, we know that it was the machine vision that first makes its way through scientific development. The technology for machine vision enables efficient mechanical “eyes” that can keep track of proceedings along the production line. The technology was an instant hit, as it enables industries to detect flawed products in real-time, thus improving the quality of the products, as well as, accurate labeling.
The development of computer vision was the next step forward for machine vision. For easy understanding, if the machine vision represents the body system, the computer vision was the retina, central nervous system, and optic nerve for the body. Both technologies work in conjunction to produce effective results. For instance, machine vision uses a camera/lens to capture visual data, which subsequently is processed by computer vision algorithms for interpretation, which then alter other components in the system to take necessary actions based on the interpreted results.
An interesting pattern to note between machine learning vs computer vision is that, while computer vision can work as a standalone solution, machine vision needs software at its core to carry out required functions. That’s because for computer vision image doesn’t necessarily have to be a photo or video, rather it can be data from any sensor like infrared sensors, thermal sensors, motion detectors, or others.
Also, when comparing computer vision vs machine learning, it’s worthy to note that computer vision is better equipped to process 3D data or moving images (videos). The latest computer vision algorithms are specially optimized to handle complex 3D data or videos, which makes them the near-perfect solution for data processing and interpretation requirements across industries.
However, the advancement of computer vision also improves enhances the applications for machine vision. Both of these technologies are making possible things like braking system in autonomous vehicles, or face recognition gates, or even robotic surgeries, which were once considered impossible.
What is the future for vision systems?
To sum up the computer vision vs machine learning comparison, it’s safe to say that the future holds great prospects for both of these visual systems. The technology behind both of these vision systems is improving every day with scientists making breakthroughs to improve the quality and accuracy of the systems.
And just like the other technologies, the vision systems will surely get even more interesting and will adapt to the new requirement of industries. To be specific, computer vision will see greater use of deep learning, cloud computing, and data integration services in the future. Whereas, for machine learning the development of advanced imaging systems to capture high-quality imagery, and intricate robotics will be some prospects of development in future.