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diff --git a/macros/trainCascadeObjectDetector.sci b/macros/trainCascadeObjectDetector.sci
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+function trainCascadeObjectDetector(outputFolder,positiveInstances,negativeImages,varargin)
+// It creates trained cascade XML file  
+//
+// Calling Sequence
+// trainCascadeObjectDetector(outputFolder,positiveInstances,"negativeImages")
+//
+// Parameters
+// outputFolder: Folder name to store trained cascade (cascade.xml) and intermediate files  
+// positiveInstances: Array of structure with image file names (including path) and an M-by-4 matrix of bounding box.
+// negativeImages: path to a negative images folder
+// numPos: number of positive samples. Default-30
+// numNeg: number of negative samples. Default- 20
+// nsplits: number of splits. Default- 2
+// numStages: number of cascade stages to be trained. Default- 30
+// featureType: Type of features to be used, possible types are HAAR, LBP, HOG. Default-HAAR
+// minHitRate: Minimal desired hit rate for each stage of the classifier and value in the range 0 and 1 inclusive. Default- 0.995
+// maxFalseAlarmRate: Maximal desired false alarm rate for each stage of the classifier and value in the range 0 and 1 inclusive. Default- 0.5
+// w: Width of training samples (in pixels). Default-25
+// h: Height of training samples (in pixels). Default-25
+//
+// Description
+// It produces dataset of positive  samples in a file with .vec extension and negative samples are enumerated in a special text file in
+// which  each line contains an image filename of negative sample image. Negative images must not contain detected objects.
+//
+// By using these two files trainCascade will create cascade.xml file inside the outputFolder which is used to detect objects in an image.
+//
+// Examples
+// positiveInstances(1)=struct("path",'image1.jpg',"bbox",[1 5 2 3]);
+// positiveInstances(2)=struct("path",'image2.jpg',"bbox",[2 7 4 2]);
+// trainCascadeObjectDetector("data",positiveInstances,"negativeImagesFolder");
+// trainCascadeObjectDetector("data",positiveInstances,"negativeImagesFolder","numStages",35,"featureType","HOG");
+// trainCascadeObjectDetector("data",positiveInstances,"negativeImagesFolder","minHitRate",0.9,"numPos",8,"numNeg",5);
+
+    [lhs rhs]=argn(0);
+    if rhs<3 then
+         error(msprintf(" Not enough input arguments"))
+    elseif rhs>21 then
+         error(msprintf(" Too many input arguments to the function"))
+    elseif modulo(rhs-3,2)
+         error(msprintf(" wrong number of input arguments,name-value pairs not macthed"))
+    end
+    
+    //validating variables
+    if ~isdir(outputFolder) then
+        error(msprintf(" wrong input argument #1, existing directory expected"))
+    elseif ~isdir(negativeImages)
+        error(msprintf(" wrong input argument #3,existing directory expected"))
+    end
+    
+    //default values
+    numPos=30;
+    numNeg=20;
+    numStages=30;
+    nsplits=2;
+    featureType="HAAR"
+    minHitRate=0.995
+    maxFalseAlarmRate=0.5
+    w=25
+    h=25
+    
+    for i=1:2:rhs-3
+       if strcmpi(varargin(i),"numPos")==0 then
+            i=i+1;
+            numPos=varargin(i);
+            if numPos<0 then
+                error(msprintf(" numPos value must be positive"))
+            end
+        elseif strcmpi(varargin(i),'numNeg')==0 then
+            i=i+1;
+            numNeg=varargin(i);
+            if numNeg<0 then
+                error(msprintf(" numNeg value must be positive"))
+            end
+            
+        elseif strcmpi(varargin(i),'numStages')==0 then
+            i=i+1;
+            numStages=varargin(i);
+            if numStages<0 then
+                error(msprintf(" numStages value must be positive"))
+            end
+            
+        elseif strcmpi(varargin(i),'nslits')==0 then
+            i=i+1;
+            nsplits=varargin(i);
+            if nsplits<0 then
+                error(msprintf(" nsplits value must be positive"))
+            end
+            
+        elseif strcmpi(varargin(i),'featureType')==0 then
+            i=i+1;
+            featureType=varargin(i);
+            if strcmpi(featureType,'haar') & strcmpi(featureType,'lbp') & strcmpi(featureType,'hog')
+                error(msprintf(" wrong input argument #%d,featureType not matched",i));
+            end
+            
+        elseif strcmpi(varargin(i),'minHitRate')==0 then
+            i=i+1;
+            minHitRate=varargin(i);
+            if minHitRate<0 | minHitRate>1 then
+                error(msprintf(" minHitRate value must lie in between 0 and 1"))
+            end
+            
+        elseif strcmpi(varargin(i),'maxFalseAlarmRate')==0 then
+            i=i+1;
+            maxFalseAlarmRate=varargin(i);
+            if maxFalseAlarmRate<0  | minFalseRate>1 then
+                error(msprintf(" maxFalseAlarmRate value must lie in between 0 and 1"))
+            end
+            
+        elseif strcmpi(varargin(i),'w')==0 then
+            i=i+1;
+            w=varargin(i);
+            if h<0 then
+                error(msprintf(" w value must be positive"))
+            end
+            
+        elseif strcmpi(varargin(i),'h')==0 then
+            i=i+1;
+            h=varargin(i);
+            if h<0 then
+                error(msprintf(" h value must be positive"))
+            end
+        else
+            error(msprintf(_(" Wrong value for input argument #%d",i)));
+        end
+    end
+    
+    [nRows noOfPositiveInstances]=size(positiveInstances);
+    fields=fieldnames(positiveInstances);
+    fd = mopen('positive.txt','wt');
+    for i=1:noOfPositiveInstances
+        mfprintf(fd,'%s 1',getfield(fields(1),positiveInstances(i)));
+        boxVals=getfield(fields(2),positiveInstances(i));
+        for j=1:4
+            mfprintf(fd,' %d',boxVals(j));
+        end
+        mfprintf(fd,'\n');
+    end
+    mclose(fd);
+    
+    disp("Creating positive samples:");
+    cmd=sprintf("opencv_createsamples -info positive.txt -num%d -vec positive.vec -w %d -h %d",numPos,w,h);
+    unix_w(cmd);
+    if isdir(negativeImages)
+        if getos()=="Linux"
+            temp=strcat(["ls ",negativeImages])
+        elseif getos()=="Windows"
+            temp=strcat(["dir ",negativeImages])
+        end
+        s=unix_g(temp);
+        [noOfFilesInFolder noOfCols]=size(s);
+        fd = mopen('negative.txt','wt');
+        for i=1:noOfFilesInFolder
+            [path,fname,extension]=fileparts(s(i))
+            if ~strcmp(extension,".jpg") | ~strcmp(extension,".jpeg") | ~strcmp(extension,".png") | ~strcmp(extension,".bmp") | ~strcmp(extension,".pgm") | ~strcmp(extension,".JPG") | ~strcmp(extension,".JPEG") | ~strcmp(extension,".PNG") | ~strcmp(extension,".BMP") | ~strcmp(extension,".PGM")
+                mfprintf(fd,'%s/%s\n',negativeImages,s(i));
+            end
+        end
+     end
+     disp("Training Cascade:");
+     cmd=sprintf("opencv_traincascade -data %s -vec positive.vec -bg negative.txt -numPos %d -numNeg %d -numStages %d -nsplits %d -featureType %s -minHitRate %d -maxFalseAlarmRate %d -w %d -h %d",outputFolder,numPos,numNeg,numStages,nsplits,featureType,minHitRate,maxFalseAlarmRate,w,h);
+     unix_w(cmd);
+endfunction;