The capabilities of artificial intelligence (AI) have already gone beyond those of the human mind. Data analysis, accurate predictions, finding invisible patterns, and object recognition are some of the features modern AI solutions are embedded with. Let’s analyze the ability of AI to recognize objects and ways to use this technology in healthcare.
- What is Object Recognition
- Object Recognition vs. Object Detection vs Object Tagging
- How Object Recognition Works in Machine Learning vs. Deep Learning
- Object Recognition in Healthcare
What is Object Recognition?
The definition of object recognition is intuitively clear. Artificial intelligence can recognize particular objects in images, videos, or others with the guidance of specified parameters.
Facial recognition software embedded in most modern smartphones is the simplest example of this technology in action. It memorizes the face of an authorized owner and compares it to the one in front of the camera to unlock the smartphone.
Object Recognition Use Cases
- Automotive industry. This technology maintains self-driving cars' capability of recognizing objects around them and making safety-first decisions depending on the environment.
- Security and surveillance. Private enterprises use object, face, and retina recognition technologies embedded in smart cameras for public safety.
- Agriculture. Robotic tools empowered with computer vision can distinguish seedlings from weeds.
- Retail. Smart cameras with a visual object recognition feature can detect customers and track their behavior in a store. Such insights are used to create better product and shelf placements.
Object Recognition vs. Object Detection vs Object Tagging
Image object recognition and object detection are both computer vision tasks, while object detection is a sub-task of computer vision object recognition. The software first detects the object, then finds out whether it has the specified parameters for a particular category, and then recognizes the object with a high degree of accuracy. More advanced object recognition software can also tag objects by assigning them traits the algorithm is aware of. For example, you can identify the image of a cat and hypothetically determine its breed if you have scheduled this task. Below are the results of an object recognition test we get from each operation:
- The result of object detection: This is an object.
- The result of object recognition: This object is 95% cat.
- The result of object tagging: This is a 95% Scottish straight cat.
How Object Recognition Works in Machine Learning vs. Deep Learning
The task of detecting an object is pretty simple for modern algorithms. And the capabilities of machine learning are enough to cope with it successfully. However, you need more advanced technologies to create these systems, when it comes to object recognition and tagging. In these cases, the developers use neural network models or deep learning, which is the next generation of machine learning.
Such projects are usually created using Python, TensorFlow object recognition API, and an OpenCV object recognition library. This library contains a pre-developed set of features for selective search for object recognition, multiple object recognition with visual attention, face recognition, voice recognition, speech recognition, and even handwriting analysis.
Object Recognition Techniques
An object recognition algorithm may use different techniques to detect, recognize and tag an object. For example, an object recognition OpenCV library allows you to use the following tactics to make the algorithm learn and work.
- Template matching. It is the most elementary technique that implies object comparison. It is used to recognize characters, letters, numbers, and objects.
- Color-based matching - used when color is the main identifying feature. For example, the algorithm can distinguish oranges from apples, knowing that oranges are only orange, while apples can be red, yellow, or green.
- Active and passive recognition. Passive recognition means the algorithm recognizes objects in existing images. Active recognition implies providing an algorithm with a live image to allow it to conclude - e.g., in medical scanning with the help of an AI-powered mobile app.
- Shape-based recognition. Similar to color matching, the system can match the shape of an object.
Object Recognition in Healthcare
There are plenty of ways to apply object recognition technology. But when it comes to healthcare, the opportunities are promisingly life-saving.
- Medical imaging. People use object recognition technology for image segmentation, disease detection, prevention, and prediction, embedding it into imaging and scanning equipment. The same technology supports mobile apps for self-diagnosis. For example, it is currently possible to take a skin cancer test using a mobile phone.
- Treatment process and healthcare rule compliance control. With the help of a mobile app with object recognition embedded, a patient can take prescribed pills and confirm it via face recognition. It can be helpful when the patient is taking a controlled medication or is forgetful. In the latter case, the application can remind the patient to take the pill. Face recognition technology is also extremely useful in compliance with the rules of quarantine and self-isolation. For example, the facial recognition park entry procedure the Walt Disney World tested in March-April 2021.
- Healthcare data analysis. The insights extracted from images can be used for research and analysis with the help of artificial intelligence.
- Complex surgeries. Smart cameras with embedded computer vision are useful for doctors performing surgeries on parts of the body that are only accessible with the help of a camera like a retina.
- Preventing mental health issues. This technology can also detect early Parkinson’s disease symptoms by analyzing patient photos. And it can determine whether a person suffers from depression by analyzing their facial expressions (according to this study).
Using artificial intelligence, machine learning, and deep learning is quite promising for custom software development. In healthcare, it gives plenty of life-saving opportunities for early diagnosis, treatment plan control, research, and analysis. If you like to move toward innovations and better efficiency, you need a tech-savvy development partner with experience in both AI and healthcare technologies.
Cprime Studios might just be the custom software development vendor you are looking for. Our up-to-date expertise in these niches allows us to create top-notch healthcare solutions powered by the most innovative technologies. You are welcome to reach out to us and find out how we can help!