转载自:http://ganeshtiwaridotcomdotnp.blogspot.com/2011/08/silence-removal-and-end-point-detection.html Visithttp://ganeshtiwaridotcomdotnp.blogspot.com/2011/06/final-report-text-prompted-remote.html for more detail about our project.For the purpose of…

转载自:http://ganeshtiwaridotcomdotnp.blogspot.com/2011/08/silence-removal-and-end-point-detection_29.html For the purpose of silence removal of captured sound, we used the algorithm in our final year project. In this post, I am publishing the endpoint…

论文:Frequency-tuned Salient Region Detection.CVPR.2009 MATLAB代码运行出错如下: Error using makecform>parseWPInput (line 389)Expected input number 2, PROPERTYNAME, to match one of these strings: AdaptedWhitePoint The input, ''whitepoint'', did not match any of…

plot a critical difference diagram , MATLAB code 建立criticaldifference函数 function cd = criticaldifference(s,labels,alpha) % % CRITICALDIFFERNCE - plot a critical difference diagram % % CRITICALDIFFERENCE(S,LABELS) produces a critical difference diagra…

Compute Mean Value of Train and Test Dataset of Caltech-256 dataset in matlab code clc;imPath = '/home/wangxiao/Downloads/Link to caltech_256_dataset/image_/ori_total_im_/';imageFiles = dir(imPath); train_txtFile = '/home/wangxiao/Downloads/caltech25…

Matlab Code for Visualize the Tracking Results of OTB100 dataset 2018-11-12 17:06:21 %把所有tracker的结果画在一张图上,结果保存在当前目录下的trackingResultsDisplay下 clc; close all; clear all; dataPath = 'C:\Users\WANG XIAO\Desktop\Tracking_evaluation\OTB100_benchmark\Benchm…

建立smo.m % function [alpha,bias] = smo(X, y, C, tol) function model = smo(X, y, C, tol) % SMO: SMO algorithm for SVM % %Implementation of the Sequential Minimal Optimization (SMO) %training algorithm for Vapnik's Support Vector Machine (SVM) % % This…

%function ccc=mfcc(x) %归一化mel滤波器组系数 filename=input('input filename:','s'); [x,fs,bits]=wavread(filename); bank=melbankm(24,256,fs,0,0.5,'m'); bank=full(bank); bank=bank/max(bank(:)); �T系数,12*24 for k=1:12 n=0:23; dctcoef(k,:)=cos((2*n+1)*k*pi/(2*24))…

A0=R(:,1:2:end); for i=1:17 A1=A0(i,:); p=sort(unique(A1)); for j=1:length(p) Rank0(A1==p(j))=j; end Rank(i,:)=Rank0; end RD5=mean(Rank);…

clear load fisheriris X = meas; m = size(X,2); % load machine % load census % % X = meas; % X=X(1:2000,:); d = pdist(X,'euclidean'); Z = linkage(d,'ward');%Create a hierarchical binary cluster tree using linkage % [Hr,Tr]=dendrogram(Z, 0); %generates…