function [model] = defineModel(dx,dy,model,quantVal)

%Build a 2D Gradient histogram

%Scale image between 0 and 255
sgradX = scale(dx,[0 255]);
sgradY = scale(dy,[0 255]);


%Quantize image
qgradX = reduceGrad(sgradX,quantVal);
qgradY = reduceGrad(sgradY,quantVal);
 
model.qgradX = uint8(qgradX/quantVal + 1);
model.qgradY = uint8(qgradY/quantVal + 1);

sizeRegX = size(qgradX);
sizeRegY = size(qgradY);

maxheight = min([sizeRegX(1) sizeRegX(2)]);
maxwidth =  min([sizeRegX(2) sizeRegY(2)]);
 
height = model.h(2)*2;
width = model.h(1)*2;

startX = model.center(1)-model.h(1);
startY = model.center(2)-model.h(2);

if(startX<1)
  startX = 1;
end
if(startY<1)
  startY=1;
end

index = uint8(256/quantVal + 1);
model.gradTable(index,index) = double(0);
model.modelDist(index,index) = double(0);

CM1=0;
%count gradient values
for y = startY:startY+height-1
   for x = startX:startX+width-1
      
      indexX = x;
      indexY = y;
      if(x>=maxwidth)
	indexX = maxwidth;
      end
      if(y>=maxheight)
        indexY = maxheight;
      end

      if(x < 1)
	indexX=1;
      end
      if(y < 1)
	indexY=1;
      end
      diffX = model.qgradX(indexY,indexX);	
      diffY = model.qgradY(indexY,indexX);
      normDist = (model.center-[x y])./model.h;
      kresult = epanech(normDist,1);
      CM1 = CM1+kresult;
      model.modelDist(diffX,diffY) = model.modelDist(diffX,diffY) + kresult;
      model.gradTable(diffX,diffY) = model.gradTable(diffX,diffY) + 1;
   end
end

model.C = 1/CM1;
model.modelDist = model.modelDist*model.C;
sum(sum(model.modelDist));

function [reduced] = reduceGrad(grad,quantVal);

reduced = double(grad);
reduced = quant(reduced,quantVal);