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The phenomenon that makes machines such as computers or mobile phones see the surroundings is known as Computer Vision. Serious work on re-creating a human eye started way back in the 50s and since then, we have come a long way. Computer vision has already made its way to our mobile phones via different e-commerce or camera apps.
How to work with Computer Vision?
Working on Computer Vision is equivalent to working on millions of calculations in the blink of an eye with almost the same accuracy as that of a human eye. It is not just about converting a picture into pixels and then trying to make sense of what’s in the picture through those pixels, you will have to first understand the bigger picture of how to extract information from those pixels and understand what they represent.
There are a few major sections in understanding how machines see images:
Understanding colours as numbers: In computers, every colour was programmed to be represented by a hexadecimal value. This differs from humans as we have a natural understanding of different shades.
Image Segmentation: Computers are made to group pixels with similar colours together. This is known as image segmentation. Pixels near to each other tend to have very similar hex-represented colours values, grouping them together could help machines to understand the images better. For example, foreground and background.
Finding corners: After image segmentation, images are then looked up for certain features, also known as corners. In other words, machines look for lines that meet at an angle and cover a specific part of the image with one colour shade.
Finding textures: Another important aspect to identify any images correctly is to determine the textures in an image. The difference in textures between two objects makes it easier for a machine to categorize one object correctly.
Draw a conclusion: After going through the aforementioned steps, the machine is mandated to make a near-to-right guess to classify the given image into one category and match the image with those present in the database.
Advancement of computer vision
From the improvement of performances of deep learning neural networks in 2012, computer vision nowadays is mostly involved in discussions of deep learning. With the power of deep learning, computer vision has already penetrated into our daily lives.
For example, face recognition algorithms were used across the globe for phone unlock. In the future, there will be more ground-breaking implementations that will affect the world.
