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Image Recognition Neural Networks, Open Sourced

Posted by timothy on from the don't-tell-hal-about-this dept.

sevarac writes "The latest release of Java Neural Network Framework Neuroph v2.3 comes with ready-to-use image recognition support. It provides GUI tool for training multi layer perceptrons for image recognition, and an easy-to-use API to deploy these neural networks in end-user applications. This image recognition approach has been successfully used by DotA AutoScript tool, and now it is released as open source. With this Java library basic image recognition can be performed in just few lines of code. The developers have published a howto article and an online demo which can be used to train image recognition neural networks online."

7 of 98 comments (clear)

  1. Re:Wow by Anonymous Coward · · Score: 0, Informative

    State-of-the-art 20 years ago! Why /., why?

  2. !Open Sourced by RiotingPacifist · · Score: 5, Informative

    Maybe im wrong but to me "open sourced" sounds like the project was closed then it was "open sourced" and now its open source. This project has AFAICT always been open:

    Neuroph started as a graduate thesis project, after that a part of master theses, and on September 2008. it became open source project on SourceForge.

    Also why not give the license in the summary (LGPL3).

    --
    IranAir Flight 655 never forget!
    1. Re:!Open Sourced by logfish · · Score: 4, Informative

      To open source thesis work, you will have to get approval from the university your are working at. So I think it probably got open-sourced later then it was developed at least. Often (at my faculty at least) implementations are protected aggressively, to keep other people from getting the same performance.

    2. Re:!Open Sourced by Anonymous Coward · · Score: 2, Informative

      To open source thesis work, you will have to get approval from the university your are working at. So I think it probably got open-sourced later then it was developed at least. Often (at my faculty at least) implementations are protected aggressively, to keep other people from getting the same performance.

      man... your university's IP policy sucks.

  3. Re:Usage of the comma, very weird by hansraj · · Score: 2, Informative

    Hey! Slashdot editors are highly trained professionals. Trained in the art of screwing up summaries that is.

    Thank your stars that they just misused one comma. Their talent for grammatical slaughter was underused. Probably they were feeling merciful today.

  4. Re:This wont work... by Dachannien · · Score: 2, Informative

    There's also the brute-force approach of sampling sub-images from the image at a ton of different sizes and locations and then feeding the sub-images to your classifier. And for controlled images (such as in face recognition) normalizing the image isn't too difficult (by scaling the image to normalize the distance between the eyes). Even then, dimension reduction by PCA and/or LDA is useful to reduce the needed complexity of the classifier.

    FFNNs lost their popularity probably 15 or 20 years ago and haven't been touched very often in image analysis since then. The SIFT features you mentioned seem to hold a lot of promise, and there are already folks using them in web-based image search and video indexing.

  5. They do support other things by OeLeWaPpErKe · · Score: 4, Informative

    So please don't take the (dumb) slashdot blurb as the exhaustive feature list of the library.

    Supported :
    -> Adaline
    -> Perceptron
    -> Multi layer perceptron
    -> Hopfield
    -> Kohonen
    -> Hebbian
    -> Maxnet
    -> Competitive network
    -> RBF network

    So it does support several quite modern approaches. It also has a training utility which supports image training. This should be very useful to students imho.