'**SUMMARY**
This method performs hue histogram back projection on the image and uses
the results to generate a FeatureSet of blob objects. This is a very
quick and easy way of matching objects based on color. Given a hue histogram
taken from another image or an roi within the image we attempt to find all
pixels that are similar to the colors inside the histogram.
**PARAMETERS**
* *model* - The histogram to use for pack projection. This can either be
a histogram, anything that can be converted into an ROI for the image (like
an x,y,w,h tuple or a feature, or another image.
* *smooth* - A bool, True means apply a smoothing operation after doing the
back project to improve the results.
* *threshold* - If this value is not None, we apply a threshold to the
result of back projection to yield a binary image. Valid values are from
1 to 255.
* *minsize* - the minimum blob size in pixels.
* *maxsize* - the maximum blob size in pixels.
**RETURNS**
A FeatureSet of blob objects or None if no blobs are found.
**EXAMPLE**
>>>> img = Image(\'lenna\')
>>>> hist = img.getNormalizedHueHistogram((0,0,50,50)) # generate a hist
>>>> blobs = img.findBlobsFromHueHistogram(hist)
>>>> blobs.show()
**SEE ALSO**
ImageClass.getNormalizedHueHistogram()
ImageClass.backProjectHueHistogram()'

def findBlobsFromHueHistogram(self, model, threshold=1, smooth=True, minsize=10, maxsize=None):

    newMask = self.backProjectHueHistogram(model, smooth, fullColor=False, threshold=threshold)
    return self.findBlobsFromMask(newMask, minsize=minsize, maxsize=maxsize)
