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    <p>Man, you just pointet out the very reason why so much people work on computer vision. </p> <p>Is is quite easy to mathematically describe objects. Color, shape, density, . . . All those can be calculated easily. </p> <p>But computer vision becomes very complex when talking about "real life objects".</p> <p>Angle, luminosity, and simply non consistency make it really almost impossible to detect an object accurately. </p> <p>When working on computer vision, you should always ask yourself : what makes the object I want to recognize unique ? </p> <p>What descriptor can I use that no other object possess ? </p> <p>Ask yourself the question for theses ramen. Let's say I simply want to detect ramens. What if the color of the soup changes? What if the meat is bigger ?</p> <p>If you want to know more, you should read about pattern recognition and pattern matching.</p> <p>And if you can find the solution to this kind of problems in a generic way, you can register for the nobel price I think :)</p> <p>Some things are quite well known nowadays, like face recognition or OCR; but they are often quite specialized and apply to only one domain. Think about it, even Google's image search algorithm sucks when you feed it with ramen. It is pretty efficient with sudoku though, as he knows exactly what he is searching for. All the difference is made in training, where you give a list of assumptions to help the algorithm. </p> <p>So basically you got it. either you create a really nice computer vision system good at detecting one thing based on a lot of assumptions, or an "ok" but quite generic one :). The choice mostly depends on your application</p>
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