adaptive-threshold

number of neuron in output layer

由 独自空忆成欢 提交于 2019-12-25 02:16:24
问题 I am new to Artificial neural network but please help me with this question? I am trying to implement an artificial neural network for character recognition (using MLP, and SNN), do I need to have same number of neurons in the output layer as the numbers of characters required to be identified. For example do I need to have 26+26+10 neurons in output layer if I want my network to be able to identify capital letters, small letters and digits. what if I had to identify all the characters in the

Adaptive Threshold CIKernel/CIFilter iOS

由 試著忘記壹切 提交于 2019-12-21 01:06:32
问题 I have researched all over in order to find a kernel that performs adaptive thresholding on iOS. Unfortunately I do not understand the kernel language or the logic behind it. Below, I have found a routine that performs thresholding (https://gist.github.com/xhruso00/a3f8a9c8ae7e33b8b23d) static NSString * const kKernelSource = @"kernel vec4 thresholdKernel(sampler image)\n" "{\n" " float inputThreshold = 0.05;\n" " float pass = 1.0;\n" " float fail = 0.0;\n" " const vec4 vec_Y = vec4( 0.299, 0

How to reconstruct the signal by applying thresholding on wavelet coefficients?

由 只谈情不闲聊 提交于 2019-12-13 15:28:51
问题 I want to reconstruct a 1D audio signal by thresholding the wavelet coefficients. First of all, I read the audio signal and normalised it. After that I added white Gaussian noise. Subsequently I calculated the maximum volume level for decomposition. I applied multi-level wavelet decomposition on the noisy signal using the db2 wavelet and obtained the approximate and detailed coefficients. For the thresholding calculation using wdencmp , I get the threshold value using soft thresholding and

OpenCV binary adaptive threshold OCR

由 感情迁移 提交于 2019-12-04 15:43:10
问题 I need to convert some images to binary for OCR. Here are the functions I am using: Mat binarize(Mat & Img, Mat& res, float blocksize, bool inverse) { Img.convertTo(Img,CV_32FC1,1.0/255.0); CalcBlockMeanVariance(Img,res, blocksize, inverse); res=1.0-res; res=Img+res; if (inverse) { cv::threshold(res,res,0.85,1,cv::THRESH_BINARY_INV); } else { cv::threshold(res,res,0.85,1,cv::THRESH_BINARY); } cv::resize(res,res,cv::Size(res.cols/2,res.rows/2)); return res; } Where CalcBlockMeanVariance : void

OpenCV binary adaptive threshold OCR

由 南笙酒味 提交于 2019-12-03 09:00:57
I need to convert some images to binary for OCR. Here are the functions I am using: Mat binarize(Mat & Img, Mat& res, float blocksize, bool inverse) { Img.convertTo(Img,CV_32FC1,1.0/255.0); CalcBlockMeanVariance(Img,res, blocksize, inverse); res=1.0-res; res=Img+res; if (inverse) { cv::threshold(res,res,0.85,1,cv::THRESH_BINARY_INV); } else { cv::threshold(res,res,0.85,1,cv::THRESH_BINARY); } cv::resize(res,res,cv::Size(res.cols/2,res.rows/2)); return res; } Where CalcBlockMeanVariance : void CalcBlockMeanVariance(Mat& Img,Mat& Res,float blockSide, bool inverse) //21 blockSide - the parameter

Bradley Adaptive Thresholding — Confused (questions)

由 自闭症网瘾萝莉.ら 提交于 2019-11-28 09:35:39
问题 I have some questions, probably stupid, about the implementation of the adaptive thresholding by Bradley. I have read paper about it http://people.scs.carleton.ca:8008/~roth/iit-publications-iti/docs/gerh-50002.pdf and I am a bit confused. Mainly about this statement: if ((in[i,j]*count) ≤ (sum*(100−t)/100)) then Let's assume that we have this input: width, i [0] [1] [2] +---+---+---+ height [0] | 1 | 2 | 2 | j +---+---+---+ [1] | 3 | 4 | 3 | +---+---+---+ [2] | 5 | 3 | 2 | +---+---+---+ and
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