def METHOD_NAME(labels, indexes=None):
    """Given a labeled image, return a list of points per object ordered by
    angle from an interior point, representing the convex hull.s
    labels - the label matrix
    indexes - an array of label #s to be processed, defaults to all non-zero
              labels
    Returns a matrix and a vector. The matrix consists of one row per
    point in the convex hull. Each row has three columns, the label #,
    the i coordinate of the point and the j coordinate of the point. The
    result is organized first by label, then the points are arranged
    counter-clockwise around the perimeter.
    The vector is a vector of #s of points in the convex hull per label
    """
    if indexes is None:
        indexes = np.unique(labels)
        indexes.sort()
        indexes=indexes[indexes!=0]
    else:
        indexes=np.array(indexes)
    if len(indexes) == 0:
        return np.zeros((0,2),int),np.zeros((0,),int)
    #
    # Reduce the # of points to consider
    #
    outlines = labels
    coords = np.argwhere(outlines > 0).astype(np.int32)
    if len(coords)==0:
        # Every outline of every image is blank
        return (np.zeros((0,3),int),
                np.zeros((len(indexes),),int))
    i = coords[:,0]
    j = coords[:,1]
    labels_per_point = labels[i,j]
    pixel_labels = np.column_stack((i,j,labels_per_point))
    return convex_hull_ijv(pixel_labels, indexes)