How Machine Learning-Driven Computer Vision Solutions are Solving Business & Environmental Challenges

Computer vision technology is being leveraged by businesses to solve real-world business problems. The integration of computer vision with data ingestion technologies is helping achieve the same. Sectors such as retail, financial services, insurance, automotive, media, and healthcare have deployed computer vision and machine learning for solving their business problems.

The use cases across industries are intriguing. For instance, companies in the insurance segment have deployed computer vision to analyze satellite imagery of cars and oil tank levels to predict car sales in malls and the production of oil, respectively. The automotive industry depends on computer vision and machine learning for its latest models with automated functions such as lane detection, road sign detection, and scene analysis for setting speed limits.

Breaking through the clutter in media and finding their own spot is a big challenge for most brands. But with machine learning in computer vision, brands can position themselves around relevant content. Companies have started allowing users to use images to search for items. It is nothing but onwards and upwards from here for computer vision and machine learning.

What is Machine Learning?

Have you ever wondered about the “Recommended movies” or “TV shows we think you’ll like” while browsing for movies and TV shows on Netflix and Prime video? Have you ever thought about how LinkedIn and Facebook end up finding people you may know? How does all of this happen? Short answer — machine learning.

Machine learning has been around for a while and researchers are working hard to evolve this technology. Big data crunching has made it possible to make machine learning algorithms that are accurate and actually work. Instead of humans extensively programming the machines, machines can now learn on their own with the availability of vast amounts of data. Obviously, it would be creepy if they did everything on their own, there are machine learning models that help the machines train.

In formal terms, machine learning is a branch of artificial intelligence and computer science that uses data and algorithms to learn the way humans do and to slowly increase its accuracy.

What is Computer Vision?

Computer vision is a field of artificial intelligence that uses cameras, edge-to-cloud computing, and software to help computer systems “see” objects. Enabled by big data and machine learning algorithms, computer vision systems can recognize images of objects and people. This lends them the ability to carry out audience demographic analysis, product inspections, and a lot more.

Relationship Between Machine Learning and Computer Vision

Source: Research Gate

The above diagram gives us a view of how computer vision and machine learning are related. Artificial intelligence is an important field of computer science that tries to solve human problems, which require human-level intelligence, with the help of computers. Machine learning, for the most part, is a subset of artificial intelligence. Deep learning is a subset of machine learning. Computer vision is a subset of machine learning and artificial intelligence as it is fed by algorithms of these fields, but it also has some of its own algorithms and methods.

Machine learning fuels functions of computer vision such as recognition and tracking. Machine learning achieves image acquisition, processing, and object focus, and computer vision uses it. However, computer vision is more than just applied machine learning. It includes 3D scene modeling, structure-from-motion, motion estimation, stereo correspondence, etc. which do not involve machine learning as the main element. Machine learning comes into play in the interpreting device and interpretation stage.

Computer Vision Challenges

While “seeing” things comes naturally to humans, it is not so for machines. Simon J.D. Prince, in his book Computer Vision: Models, Learning and Inference, points out that making computers see is a challenging task. Despite four decades of dedicated work by researchers, they have not yet been able to build a general-purpose “seeing the machine.”

One of the main reasons why it is difficult to reproduce human vision in a machine is that we do not quite understand how human vision works, yet. Human vision is composed of the biological perception of things through eyes and the interpretation of the same in the neural networks of the brain. Richard Szeliski, in his book Computer vision: Algorithms and Applications, says that perceptual psychologists have made long strides in gaining an insight into how the visual system works but that “a complete solution to this puzzle remains elusive.”

Another challenge is the visual world around us itself. The complexity of how, where, in what lighting conditions, and from what perspective something is viewed makes it even more difficult to replicate human vision in machines.

Where problems can be easily defined, computer vision works well. But for complex problems such as human visual perception, computer vision is still playing catch up.

10 Major Tasks and Use Cases Computer Vision Solves with Machine Learning

Technology advancements have impacted how work is done and have also enhanced the possibility of doing previously undoable tasks. Some challenging tasks can now be accomplished using computer vision.

Below we see some examples of the same:

1. Optical Character Recognition (OCR): Reading and recognizing text and barcodes is a cumbersome but important everyday task. This is why industries are now incorporating computer vision systems and industrial automation. Business savings can be enhanced by deploying OCR technology. This technology helps read and understand real-time data in images. These images are usually taken as scanned screenshots or documents.
2. Machine Inspection: Identifying defects, flaws, contaminants and other irregularities in manufacturing is important but very difficult for manual inspectors. 3D machine vision systems with several laser displacement sensors and cameras can help solve the problem. These cameras are installed in different locations and at different angles to provide proper orientation to the machine.
3. Retail Tracking Automation: The retail industry has already deployed computer vision for tasks such as customer tracking, people counting, theft detection, waiting-time analytics, social distancing, productivity analytics, and more. However, one of the most challenging tasks in retail is to prevent shopping cart abandonment due to long check-out queues. To tackle this challenge, the retail industry is investing in autonomous checkout systems. Some of the ways in which this technology is implemented are Scan and go, Click and collect, Mobile checkouts, etc.
4. 3D Model Building (Photogrammetry): Photogrammetry concerns itself with geometric accuracy. The application of photogrammetry is in producing topographical maps and close-range photogrammetry for architecture, anthropometrics, industrial metrology, and archeological surveying. The intersection of computer vision and photogrammetry lies in the applications of the central projection camera such as camera calibration, pose detection, model projection, and model construction.
5. Medical Imaging: Deep learning-based image analysis makes it easy to detect biological anomalies through radiological X-rays, ultrasounds, and NMRs. Deep learning-based computer vision systems are flexible like humans and robust and fast like a robot. All images that are different from normal physiology are flagged to be diagnosed by the expert radiologist.
6. Automotive Safety: There are collision avoidance systems also called Advanced Driver Assistance Systems (ADAS) which help with automotive and passenger safety. Deep neural networks help in deep learning which aids these systems.
7. Match Move: TRON (1982) was the first film to match live-action with computer-generated images (CGI) for an extensive time frame of about 20 minutes. Merging CGI with live actors in movies is not new but it has gained a lot of traction in recent years. In fact, a large number of films are incorporating CGI with live-action now. Filmmakers can now create a full 3D graphics world that enhances the overall appeal of the movies for the audience.
8. Motion Capture (MoCap): MoCap is a computer vision technique that helps estimate the position and orientation of a vehicle with the help of an external positioning mechanism. Mocap systems mostly use infrared cameras but they also use Lidar and Ultra-Wideband cameras.
9. Surveillance: Surveillance is an important application of computer vision models. Surveillance cameras are widely deployed across public places including shopping malls, shops, roads, and alleys to detect and prevent suspicious activities such as thefts, robbery, and physical attacks, and abuse of any kind.
10. Fingerprint Recognition and Biometrics: Another important use of computer vision algorithms is in biometrics and fingerprint recognition. Biometrics including fingerprint recognition, iris recognition, and facial recognition help in enhancing security through personal identification and authentication.

What Can Computer Vision Applications Do With Image/Photographic Data?

A large number of computer vision applications try to recognize objects in images by using image recognition algorithms. The following tasks help in developing the algorithms:

• Object Classification: What broad category of object is in this photograph? First, the computer is fed with already labeled images which become the training set of images. Then, computer vision algorithms help label any new images as close to the training set of labels as possible.
• Object Identification: Which type of a given object is in this photograph? Through deep neural network training, the probability of the image being of a particular object is calculated.

Image Classification credit: https://hoya012.github.io/

• Object Verification: Image classification is also used to verify whether an object is or not in the photograph. The bounded box approach with the x and y coordinates or multiple classes in one image can be used to find the same.
• Object Detection: Where are the objects in the photograph? Once the different objects have been identified and labeled, their position in an image or different images can be detected. It is also possible to detect a specific object where multiple classes of objects are present.
• Object Landmark Detection: What are the key points for the object in the photograph? In computer vision applications, deep neural networks often recognize some key points of interest in the image. These are referred to as landmarks and the coordinates of these points instead of the bounding box are used.
• Object Segmentation: What pixels belong to the object in the image? Object or image segmentation refers to dividing an image into different segments and each pixel value is categorized into a particular class.
• Object Recognition: What objects are in this photograph and where are they? Object recognition is an important computer vision technique that helps in identifying objects in an image or a video. It uses deep learning and machine learning algorithms to do this.

Applications of Computer Vision Using Machine Learning

Computer vision and machine learning are often used as complements as they create systems that are strong, fast, and accurate. Some of the machine learning models used in computer vision applications are Neural Networks (NN), Support Vector Machine (SVM), and Probabilistic Graphical Model. Let’s have a look at some of the computer vision applications using machine learning models:

AI Image Processing

Image processing is a process used to either identify information from a given image or to transform image quality or data. With complex machine learning models and computer vision algorithms, image processing can now be done faster and over large datasets. Image processing services are used in fields like life sciences research, radiology, forensics, agriculture, retail, manufacturing and assembly, enterprise resource, planning software, operations and logistics, and surveillance and monitoring.

AI for Drone Surveillance

Another important computer vision application is AI-driven software for drone surveillance. Machine learning models are used to create robust and powerful software which can be used for aerial mapping, modeling, analytics, etc. The high-quality imagery of drone cameras can help in fields like livestock management, terrain mapping, and Smart Farming.

AI for Image Annotation

Image annotation software uses machine learning and computer vision algorithms for visualizing, processing, analyzing, and segmenting objects in images and videos. After this, the user can annotate a large number of images fast and accurately. Image annotations are used for land-marking, 3D cuboids, bounding boxes, polygon annotation, and semantic segmentation.

AI for Image Segmentation

Image segmentation is the process by which an image is separated into several parts of pixels. These pixels are called image objects and can lessen the complexity of the image, thereby enabling easy analysis of the image. The technique is already being used to solve challenging problems in futuristic applications like drones, robots, and autonomous vehicles.

Our ML Solutions that Involve Computer Vision Technology

Vehicle damage detection with AI

The AI-based system includes a set of Machine Learning algorithms and an API based on Computer Vision. These algorithms identify a vehicle’s body and analyze its damage based on pre-trained deep learning models. Fast analytical and machine learning pipelines make it possible to receive results in seconds.

This use case has helped one of our leading Banking & Finance sector clients to automate the insurance claim processing. The claimant can share the images of the damaged vehicle with the insurance office and the images are run through the pre-trained model to detect the damages.

This damage detection with AI solution has enabled the inspectors to accelerate claims processing without visiting the venue of inspection.

Species Detection With AI

By harnessing the computer vision & geospatial analytics tech, our ‘AI for Good’ solutions drives conservation efforts via species detection.

In collaboration with Microsoft AI for Earth, Gramener has developed a species detection AI solution. Using Camera Trap Image Processing, captured images can be classified into different types of flora and fauna.

Over 5,000 plant and animal species may be identified and classified using this API created by Gramener & Microsoft AI for Earth. Using the AI for Earth API backend, researchers working in environmental science and sustainability may quickly create APIs and web-based apps based on machine learning.

Fish Classification & Monitoring With AI

Video-based manual identification and classification of any living species require a lot of resources as well as a lot of time and money to complete. To solve a similar problem for Nisqually River Foundation in Washington, we developed an automated, technology-driven solution for them named “Salmon Detection Web App.”

This was achieved by training Deep Learning AI models to draw boxes around each fish that crossed the camera’s field of view (FOV). One web app managed the whole workflow from input to detection to classification. As part of the automated AI solution, Microsoft Azure and Cognitive Services platform stack was used to apply the newest deep learning algorithms.

Automated Cell Counting With AI

It is a tedious task for a scientist who would rather focus on important research work than sit for days identifying shapes in cell structure manually. With Gramener’s Deep-Learning solution, we reduced days of effort to seconds & improved the accuracy.

Emotion Analytics With AI

A person’s emotional state can be measured using emotions analytics software (EA). It collects information on how they express themselves verbally and nonverbally. Also referred to as Emotion analytics, these analyses provide insight into how a customer feels about a product, how it is presented to them, or how they interact with a customer service representative.

There are several technologies that have been made possible by automatic data analysis such as Convolutional Neural Network (CNN) and Machine Learning (ML). Automated Emotion Analysis, on the other hand, has given rise to a new technology, which has its origins in automated data analysis.

Emotion Analytics will be discussed in the near future. Artificial Intelligence was also a hot topic back in the 1990s.

In and of itself, the phenomenal progress of AI since then is a fascinating tale. Artificial intelligence allows robots to do manual activities with unimaginable speed and precision, thanks to the technology of artificial intelligence. To name a few: recognizing and counting people, animals, and objects in a crowd; interpreting languages; forecasting Football & NBA game outcomes; creating memes, and many more examples are available.

Disaster Recovery With AI

Our team at Gramener in collaboration with SEEDS India and Microsoft developed a platform for generating hyper-local risk information to provide early warnings of impending disasters using an AI-based satellite imaging model.

Components Requirement in Computer Vision and Machine Vision

The evolution of machines has come to the point that through computer vision and machine learning algorithms large amounts of data can be stored, analyzed, and understood. The visual content is computed using the core building blocks of object classification, in which images are classified as belonging to one of the training categories, and object identification, in which the images are finally tagged as a particular object such as a dog and a cat could be tagged for two objects in one image.

For those comparing computer vision vs machine vision, machine vision is the subset of computer vision as it refers to the use of computer vision in industrial environments. Computer vision is the process of automated image capture and processing to provide meaningful results. Computer vision and machine vision have the following overlaps in terms of components and requirements:

• Imaging Device: A camera lens or image sensor is required for getting the images to build both computer vision and machine learning algorithms.
• Image Capture Board: The frame grabber may be required in some cameras, though modern digital cameras may not require it.
• Lighting: It is important to have the right lighting for several applications using computer vision and machine learning. In absence of the right lighting, the images and the data parsed from them may be distorted.
• Processing Software: Both computer vision and machine learning need image processing software to create the algorithms.

Conclusion

Computer vision and machine learning have an important and increasing role in current and future industries. These technologies are used for varied purposes. A 2019 study showed that 59% of marketing agencies were using computer vision to identify unsafe brand content. For example, if a meat provider company’s ad is placed next to an article about swine flu, it wouldn’t bode well for the brand. The computer vision and machine learning technologies can be scaled for optimum results, and the future will have more (maybe all) companies using the technologies for their day-to-day and innovative work.