Computer Vision (CV) is the field of AI which helps computers replicate the human visual system and enables computers to identify images, videos like we do. One such vital application of computer vision is facial recognition, digital image processing. There are many industry applications as well. Computer vision algorithms are heavily utilized to understand customer behavior, help in cancer detection or even diagnose for Covid-19. From a preventive perspective, CV can help in avoiding accidents, prevent collision to guiding self-driven cars and much more.
How Does Computer Vision Work
A machine is trained using lots of data. For instance, if a machine needs to recognize a human face, the ML engineer might need to go through several calculations such as distance between the eyes, corners of mouth, shape of nose, etc. Computer Vision can assert the theme of any image, or video or even camera feeds.
Training is also given to machines which includes a lot of noise. Here noise implies individual pixel being lighter or darker than the required proportion. It is also important to have multiple pictures of the same object from different angles, so that the machine is aware of the different possibilities.
Convolutional Neural Network (CNN) is the integral mechanism behind building a computer vision technique. Either a dataset is created using images or an existing one may be used. Then feature extraction takes place to train the deep learning model. Training mechanism helps the machine to get hold of the data even more. Lastly comes model evaluation and validation. Though it is a basic strategy, supervised machine learning, it serves the purpose well.
Applications of Computer Vision
Computer vision has multiple use cases. Some of them are: retail, manufacturing, sports, healthcare.
Brick and mortar industry can make use of computer vision to understand customer behavior. Customer’s movement can also be tracked when they are inside stores. Through directional gaze detection, retailers can actually improve on customer satisfaction by rearranging stuffs and regrouping on placement of stuffs inside the store. Inventories can be well managed as the algorithms generate the near correct estimate of the number of items in the store.
In the manufacturing segment, computer vision algorithms come handy for predictive maintenance and defective reduction. Whenever a component is broken or damaged, there is reduction in profit for some time. With the help of cameras attached to robots, this damaged parts can be detected and may be put up much before for maintenance purpose.
When it comes to sports, automated detection and motion capturing cameras can analyze athletes’ performance. It can also identify and classify strokes, say in table tennis. Even coaches can reduce on feedback time and improve resource efficiency. Ball trajectory data is very crucial to understand game strategies and evaluate player’s performance.
When it comes to healthcare, currently we all are fighting against Corona virus. There is already a deep learning computer vision models COVID-Net which is digital chest x-ray radiography images. This comes a long way in coping up with the disease. Masked face recognition helps in regulating and limiting the spread of coronavirus. With this feature implemented in mobile apps, app cab drivers can also be aware whether passengers are wearing masks or not. With the help of deep learning and use of in-depth cameras, abnormal respiratory patterns can be detected in patients and thus they can be provided extra care.
End Notes
In case multiple objects are present, there might be an overlap. Machines might be unable to recognize an object if some characteristic remain hidden. At times, the same object is sub-divided into different sub categories. For instance, the algorithm should be able to classify a ceiling lamp as well as a night-stand lamp. Mis-recognition is a common problem in computer vision. Thus, nowadays deep artificial neural networks work side by side with CV. It is applicable in industries ranging from energy, to manufacturing to automotive and the market continues to grow. Thus with continuous improvement in algorithms, computer vision has much scope to showcase it’s utility.
