Artificial intelligence (AI) has been a hot topic for over a decade. It is predicted that the global AI market will be almost $267 billion by 2027, up from just $1.4 billion in 2016. Applications span across several industries, from performing a Google search to Netflix recommendations, Amazon understanding the products we want to buy, and predictive maintenance, to name a few.
Computer vision is another AI subset destined to have a revolutionary impact in a post-COVID-19 world, where the digital ecosystem continues to expand exponentially.
This article discusses how computer vision helps to understand this visual content - and how advances in technology, computing power, and connectivity make computer vision attainable to every SME.
What is computer vision?
Computer vision (CV) uses algorithms to understand digital visual data the same way as the human eye does. Models can translate what they see into data and take meaningful actions or recommendations based on the information.
By 2022, the computer vision market is expected to reach $48.6 billion as new developments like autonomous vehicles, delivery drones, and spotting a 'deep fake' rely on technology.
Compute vision comes in two forms. The first requires a significant amount of logical and mathematical thinking from field experts such as data scientists and data engineers. Unfortunately, such resource is hard to come by and expensive and often requires investment in technology and IT infrastructure.
The second form, which is becoming popular amongst SMEs, is “no-code” computer vision. Computer vision without code platforms don’t require a solution to be built internally, meaning no new resource investment and loss of time.
In addition, these new platforms (such as Levity, our own platform which performs computer vision tasks) make AI less intimidating for non-technical people.. They also allow businesses to deploy models with minimal coding knowledge while remaining economical.
We will look at some of the leading providers of computer vision without code platforms later in this article.
Benefits of Computer Vision
There are three core benefits to computer vision over human vision.
Although the human brain is amazing in that it can complete images based on a minimal amount of information, it is also excellent at deceiving us. By nature, human decisions are fuelled by assumptions, stereotypes, and preconceptions of the truth.
For example, consider the “Mandela Effect.” There are countless examples where the human brain believes it has extracted information from an image, but in truth, it interprets it based on an assumption. If you think the popular shoe brand is known as “Sketchers”, you would be wrong. The image on the right is correct.
Computer vision bases decisions on data, not assumptions. It does not have the innate human tendency to jump to conclusions before analyzing all of the data. Furthermore, computer vision operates at a pixel level that human brains are not capable of processing.
Therefore, the results are more accurate and are advantageous in medical screenings or spotting 'deep fakes' that require an eye for detail. In manufacturing, computer vision can actually improve defect detection rates by up to 90%.
So it's a win-win - fewer repetitive tasks for error-prone humans; improved accuracy, faster turnarounds and lower costs for businesses.
Acquiring better results, faster
Imagine you are part of a digital marketing team tasked with labeling 5,000 product images from your e-commerce website. It’s a daunting task that you would need to allow weeks to complete without working on other projects. However, computer vision can analyze those images in seconds - once again allowing human resources to focus on other tasks that still require a human mind to execute.
A great example of how computer vision speeds up work is pulmonary medicine during the COVID-19 pandemic. The technology sped up diagnostics by analyzing lung CT scans within a matter of mere seconds. A doctor achieves the same result of accuracy in 15 minutes.
After training data is fed into a machine, it can continue to complete the same tasks on a cycle and even learn while it does so. For example, consider a manufacturing line. A computer vision model can be trained on the assembly line to recognize any defects.
A camera or sensor is set up and paired to the algorithm. Manufacturers then get real-time alerts to solve problems early on in the process, decrease the risk of loss of materials, and reduce their operational costs.
In the US, $4 billion is lost in the orange market due to crop disease. Computer vision can detect its early signs to optimize yield and reduce costs.
The types of computer vision
There are several different types of computer vision that companies can deploy depending on the use case.
We're going to briefly chat through each of these types of computer vision shown in the graphic above.
Classification is a technique that breaks an image down into abstract shapes and colors to form a hypothesis about the content. The method will predict a specific class or label using a defined set of data points. For example, the model is first trained using sample photos. Image classification algorithms can take a new image and identify the “class” that it falls into, e.g. dog or cat. With deep learning, machines can accomplish this even if they are in different positions or poses by extracting its features.
Object detection locates an object within an image. A typical application is how social media platforms find faces within photos. Each object has its own unique features. For example, in face identification, the algorithm knows to look for eyes, nose, and lips, and distance between the eyes can be found. Object detection draws bounding boxes around detected objects, allowing us to locate where they are within a scene.
Following on from object detection, object tracking creates a unique ID for each set of bounding box coordinates. Using that ID, the object can be tracked as it moves around the frames in a video. Traditional applications are in video and real-time interactions where observations are required following the initial detection. One such example could be self-driving vehicles to avoid accidents.
Object tracking works on two levels, single object tracking (SOT), and multiple object tracking (MOT).
In a semantic segmentation technique, every pixel in the image is linked to a class label. For example, each pixel can be labeled as, respectively, grass, cat, tree, and sky. In an application like autonomous vehicles, semantic segmentation can help cars understand the context of the environment, beyond standard object classification. As a result, the vehicle could ‘see’ that it’s driving through a forest.
With image segmentation, an image is divided into different regions based on the characteristics of the pixels. The goal is to identify objects and boundaries to simplify the image and analyze it. One of the most common applications is the “green screen” in filmmaking. Image segmentation crops out the foreground and places it onto a different background.
Applications of computer vision
There are five prominent applications of computer vision.
Edge detection finds the boundaries of objects within an image by detecting discontinuities in brightness. Thus, it reduces the amount of data without affecting the structural properties of the objects within the image. Some of the most popular use cases for edge detection are satellite imagery, lane detection, and converting an image into a sketch.
A pattern detection algorithm uses machine learning to train a solution to recognize various visual patterns. In healthcare, pattern detection models can understand different types of cells and support a medical diagnosis. One paper explains how computer vision is used for pattern detection in chromosome maps.
Classification is one of the most common applications of computer vision. The technology classifies images from training data and tags them with the correct labels. For example, a self-driving car needs to always be aware of its environment. It needs to know what objects are around it at any given time. Image classification can label every object using cameras to help the vehicle adapt to a changing environment.
Face recognition systems will output bounding boxes in an image containing faces. It works by finding the face coordinates using features such as the eyes, nose and lips. We have already discussed a use case for social media, but during the Covid-19 pandemic, software company LeewayHertz launched a FaceMask Detection System using CCTV, computer vision, and AI to detect people who are not complying with the rules in place at the time. It alerts CCTV operators to people not wearing a mask so they can act accordingly.
In an image, a feature could be a specific structure like a point, edge, or object. They can be in particular locations or matched based on their orientation or local appearance. A feature matching technique will establish correlations between two images of the same scene or object by detecting interest points.
Leading providers of computer vision software
The number of computer vision without code platforms that allow people without technical skills to build algorithms is increasing rapidly. They allow non-AI experts to build AI systems with drag-and-drop style interfaces and menus instead of writing thousands of lines of code. Three of the leading providers of computer vision software are Levity, Clarifai, and Lobe.
Using Levity, businesses can remove daily repetitive and mundane tasks to improve productivity. Without using a single line of code, powerful AI delivers a platform that can automate workflows involving images, documents, or text that are difficult to analyze with a human eye.
Users can connect to a data source and train a custom computer vision model using a drag and drop interface. The machine will learn from the data automatically and improve itself with new inputs. Some of the common use cases include:
- Checking documentation to ensure it contains all the necessary inputs and meets quality standards.
- Tag images for your database by training the AI to recognize thousands of pictures and place them in suitable files.
- Categorize service requests automatically by type or urgency.
- Classify customer reviews and feedback or product information to allow your teams to respond proactively.
- Tagging email attachments and filing them appropriately.
- Categorizing social media images.
Clarifai is designed for developers, data scientists, and business operators. The integrated AI Computer Vision platform helps businesses gather insights from images, videos, and text within a single integrated workspace.
The platform comes with custom and pre-trained AI models to help get you started, with a drag-and-drop user interface to evaluate and optimize the algorithms. It is easy to use for all skill levels, enabling the fast deployment of AI solutions. Use cases include:
- Facial recognition to detect the presence of faces in images and videos. Governments and finance firms use these applications to offer an added layer of security to processes in physical spaces.
- Visual search where customers can find items using an image instead of typing keywords, increasing their likelihood of purchase.
- Monitoring content for NSFW images, copyright infringement, or checking brand guidelines have been met.
- Predictive maintenance to determine the condition of equipment.
Lobe helps users to train machine learning models with an easy-to-use platform. No code or experience is required making it accessible to everyone. Lobe works with a three-step process.
- Collect and label your images using a webcam or dragging a folder in from your computer.
- Train the model and analyze the results automatically without the need for a complicated setup or configuration.
- Test and optimize the model by giving it feedback before deploying it to your application.
Common use cases of the platform are to identify hand gestures, species of animals or plants, recognize workout positions to count reps, send emojis using facial recognition, monitor if masks are being worn correctly, or baby monitoring to notify parents when they are awake proactively.
Deep Vision AI
Deep Vision AI provides computer vision for media services and applications. For example, detecting and recognizing faces from images or videos and performing facial matches to find target subjects, and auto-tagging objects, products and scenes.
- Detect and identify information like the year, make and model of vehicles from any angle, interior or exterior
- Detect and recognize brand logos in images and video.
- Get the most visually similar objects in your images with visual search, for example in ecommerce.
Their offerings fall largely into the realm of analyzing visual, anonymized data for the creation of smart cities, for retail & ecommerce, and in entertainment such as novel ways to personalize and tag the consumption of media.
Computer vision is a revolutionary technique that can improve the accuracy and efficiency of business operations while reducing costs. Although a decade ago, SMEs might require a skilled resource, the introduction of computer vision without code platforms allows non-technical people to execute complex models. Furthermore, studies show that low code/no-code solutions can reduce development time by up to 90% over custom AI solutions.
When AI is becoming a must-have tool rather than a nice-to-have technology, no-code platforms can help SMEs take their business to the next level. It is creating opportunities for organizations to turn raw data into something insightful and useful. Companies that embrace computer vision without code will start to move to the forefront of their field as they deploy innovative solutions quickly.
No-code is arguably a COVID-19 silver lining as businesses strive for a path to digital success. You don’t need to be an Amazon, Google, Facebook, or Apple to implement exciting technology. AI is available for the masses and is here to stay.
If you’re interested in learning how your company could leverage computer vision without code, reach out – we’ll be more than happy to see how Levity could help out your business! Our platform makes it possible for you to easily train a model (with less data than you think), that can classify images in your organization using computer vision.