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To perform image recognition you have to find a way to represent an image with certain features. One of the defining characteristics of a good image recognition algorithm are it's ability to detect salient regions, that is, regions which contain the most information There is a lot of attention on deep learning for content-based image classification at the ...


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The most natural place where artificial networks can be used in information security is in attack detection. The security team leaders of more than one web hosting company told me the same story. Their teams' daily challenges are to defend against the attacks mounted continuously by several overseas teams against the IT security of their hosting ...


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At first, you can find lots of information as pedestrian detection. As you are trying to localize game characters, the face is not the best option. You need to look for the character in general. About HAAR Cascades, the algorithm is one of the fastest face localization solutions in the market. The reason is, it applies all the feature classifications layer ...


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Yes, in fact neural networks (NNs) are very efficient at segmentation and it seems to me that your problem matches the capabilities of neural networks very well. I think it best for you to truly understand what a NN is before using it. First, let's start with the architecture. A NN has 3 regions, the input layer, the hidden layers and the output layer. The ...


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Firstly, before we commence I will recommend that you refer to similar questions on the network i.e. https://stackoverflow.com/questions/6499880/ios-gesture-recognition-utilizing-accelerometer-and-gyroscope and https://stackoverflow.com/questions/6368618/store-orientation-to-an-array-and-compare Your problem can be divided into three parts. How to gather ...


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Because it is video input and the logos are usually stationary because they are layered over the live or recorded frames by either hardware or software, the task is not difficult. Logos also usually have limited color palettes and crisp edges. The features of their fonts, when they spell words or acronyms are usually consistent too. These are generalities ...


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You should look into unsupervised learning, which is machine learning without a training set. CNN's are cool but they need a training set.


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The terms you are looking for are deeplearning and convolutional neural networks for object detection. Google responds well to these terms. From academical point of view you can start from: Single shot multibox detector: https://arxiv.org/pdf/1512.02325v5.pdf Or Faster-RCNN: https://arxiv.org/pdf/1506.01497.pdf These are not simple architectures and there ...


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Instead of using a neural network, simply sample as many non-anomalous readings from each sensor as you can. If the distribution of the readings from each sensor is approximately normal (check the skew and kurtosis values for the samples from each sensor) then you can work out mean and standard deviation of the samples and, for any future samples, the value ...


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Without knowing the kind of data and the process generating it it's hard to give a definite answer. In general, I would attempt a network that has as inputs the actual sensor readings, and outputs the expected readings. You train this network by presenting data with errors added as inputs, and correct readings as outputs. It should learn to guess the ...


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First, the title mentions "sparse data". Recently the expression has taken a clear meaning: The agent input is data with mostly zeros. In the question a different meaning: A "sparse data stream", where data flows and vanishes sometimes. I understand the question as: "Will training an AI still work if the training data stream breaks?" Note the explicit "...


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Since my comment would not fit, I'll answer the question. I think this problem will be even more better solved if you have the web data as well. Say after adding the required necessity in the cart the customer will check the total amount. And if it is below 50$, the person will add some more items. So checking this can give you a better clue. Another data ...


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Would I be right in saying that this becomes a sort of 'pattern recognition' problem? Technically, yes. In practice: no. I think you might be interpreting the term "pattern recognition" a bit too literal. Even though wikipedia defines Pattern recognition as "a branch of machine learning that focuses on the recognition of patterns and regularities in data",...


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Since the document is scanned, it will not be in an open document format so no associated API can be used. Approach 1 Evaluate TextBridge Pro, FreeOCR, and other alternatives that purport page layout detection. If any of them work, drive them programmatically (preferably headless) to read the scanned document, detect page layout and OCR the text, export ...


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TextDetector, Tesseract and other open source packages implement text detection (object detection for text). There's also a pretrained Tensorflow model that does text detection. A text detector will give you the bounding boxes in your image for any text that it recognizes. In the case of Tesseract, it will also output the text (OCR is built in). So you can ...


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There is absolutely no doubt that fuzzy logic can contribute to the reliability and accuracy of real time diagnostics of mechanical, thermodynamic, and electro-magnetic devices. I can state this with assurance because it exists in aeronautics products today. One of the senior PhDs at the research facility wanted to acquire data via standard techniques and ...


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This task falls within the overlapping fields of information extraction and pattern mining. Information extraction involves automatically extracting instances of specified relations from data. While pattern mining involves using data mining algorithms to discover interesting, unexpected and useful patterns between data in databases (Philippe F). On your ...


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