pattern-recognition

Closed . This question needs to be more focused . It is not currently accepting answers. Want to improve this question? Update the question so it focuses on one problem only by editing this post . Closed 5 years ago . Hough Transform Algorithm is one of the algorithm use for text line detection and edge detection. Does Hough Transform Algorithm can be use for Detecting text in Images? What must be the process or implementation in java for this question? or It must be have another algorithm to make it possilble? Hoping for positive response. Hough transform was initially designed to detect

Description of my problem: I have a sample audio file and need to find it in another audio file (for example, to find starting and ending time of recognition fragment in the audio file). Is there any library for performing this task? 1) CMU Sphinx . It is a Open Source Toolkit For Speech Recognition. Description : CMUSphinx is a speaker-independent large vocabulary continuous speech recognizer released under BSD style license. It is also a collection of open source tools and resources that allows researchers and developers to build speech recognition systems. 2) RWTH ASR (short RASR) is an

We have had a production web based product that allows users to make predictions about the future value (or demand) of goods, the historical data contains about 100k examples, each example has about 5 parameters; Consider a class of data called a prediciton: prediction { id: int predictor: int predictionDate: date predictedProductId: int predictedDirection: byte (0 for decrease, 1 for increase) valueAtPrediciton: float } and a paired result class that measures the result of the prediction: predictionResult { id: int valueTenDaysAfterPrediction: float valueTwentyDaysAfterPrediction: float

I’m currently working on pattern recognition using SURF in OpenCV. What do I have so far: I’ve written a program in C# where I can select a source-image and a template which I want to find. After that I transfer both pictures into a C++-dll where I’ve implemented a program using the OpenCV-SURFdetector, which returns all the keypoints and matches back to my C#-program where I try to draw a rectangle around my matches. Now my question: Is there a common measure of accuracy in pattern recognition? Like for example number of matches in proportion to the number of keypoints in the template? Or

I have to find the best approach for tackling a problem for trying to recognize physical movements - with an iPhone in a pocket - like waling, stopping, turning left/right, sitting. I was thinking on just heuristically find the data corresponding to each action, then to check the incoming values against this data (with a threshold) and see what's happening. That's a very rough approach, of course, so maybe using something like the Support Vector Machine methods, but this seems too complicated for the amount of time I have to develop this. Which approach would you suggest here? Walking : Do an

A lot univariate decision tree learner implementations (C4.5 etc) do exist, but does actually someone know multivariate decision tree learner algorithms? Bennett and Blue's A Support Vector Machine Approach to Decision Trees does multivariate splits by using embedded SVMs for each decision in the tree. Similarly, in Multicategory classification via discrete support vector machines (2009) , Orsenigo and Vercellis embed a multicategory variant of discrete support vector machines (DSVM) into the decision tree nodes. CART algorithm for decisions tree can be made into a Multivariate. CART is a