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As a followup (and this is rather unrelated to the original post), you can combine a feature detector with a statistical clustering algorithm to automatically i
by wickedchicken 15y ago
As a followup (and this is rather unrelated to the original post), you can combine a feature detector with a statistical clustering algorithm to automatically identify the generic visual properties of objects in an unsupervised manner. One of the first papers attempting this is http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.72.2610&rep=rep1&type=pdf http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.72.... but many have followed it. From what I understand these still simply check for the existence of features in an image instead of their spatial relationship ('a picture of a cat has two ears, a tail, and a blob for a body' versus 'a picture of a cat has two ears positioned on the end of a blob with a tail on the other end of the blob'). Nevertheless, they represent the current state-of-the-art in automatic object classification and recognition algorithms.
- sigil 15y agoI went to a talk by Yann LeCun [1] a few months back, "Learning Feature Hierarchies for Vision." The current state of the art in this field is mind-blowing to an outsider. The final demo was a program that he trained in a matter of seconds to recognize and distinguish faces of various random audience members, in real time, from different viewing angles. [1] http://yann.lecun.com/ http://yann.lecun.com/
- _delirium 15y agoIs any of that software available?
- sigil 15y agoSome of the libraries are [1]. Probably not the whole kit and caboodle. Interestingly, there was a focus on special purpose hardware that made convolutional network learning possible in realtime. One of the other demos was an autonomous driving robot that learned to recognize obstacles in video. Again, just mind-blowing. [1] http://www.cs.nyu.edu/~yann/software/index.html http://www.cs.nyu.edu/~yann/software/index.html