Do you know how computers can see the world? It is very important as self-driving cars will be available in near future. If you do not know it, you can not be brave enough to ride on them. So let me explain it for a while.
1.Image can be expressed as a sequence of number
I believe that you have heard the word “RGB“. R stands for red, G stands for green, B stands for blue. Every color is created by mix of three colors of R,G and B. Each R, G and B has a value of number which is somewhere from 0 to 255. Therefore each point in the images, which is called “pixel” has a vector such as [255, 35, 57]. So each image can be expressed as a sequence of numbers. The sequence of numbers are fed into computers to understand what it is.
2. Convnet and classifier learn and classify images
Once images are fed into computers, convnet is used to analyze these data. Convent is one of the famous algorithms of deep learning and frequently used for computer vision. Basic process of image classification is explained as follows.
- The images is fed into computers as a sequence of numbers
- Convolutional neural network identifies features to represent the object in the image
- Features are obtained as a vector
- Classifier provides the probability of each candidate of the objective
- The object in the image is classified as an object with the highest probability
In this case, probability of Dog is the highest. So computers can classify “it is a dog”. Of course, each image has a different set of probabilities so that computers can understand what it is.
3. This is a basic process of computer vision. In order to achieve higher accuracy, many researchers have been developing better algorithms and processing methods intensively. I believe that the most advanced computer vision algorithm is about to surpass the sight of human being. Could you look at the famous experiment by a researcher with his sight? (1) . His error rate is 5.1%.
Now I am very interested in computer vision and focus on this field in my research. Hope I can update my new finding in near future.
1.What I learned from competing against a ConvNet on ImageNet, Andrej Karpathy, a Research Scientist at OpenAI, Sep 2 2014
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