def METHOD_NAME(self, image, label_bboxes, axes):
    """This function computes local binary pattern histograms
    for the object, the neighborhood of the object (without
    the object itself), and both the object and the neighborhood)
    The LBPs are computed via the scikit.image package
    """
    rawbbox = image
    mask_object, mask_both, mask_neigh = label_bboxes
    import skimage.feature as ft
    P = 8
    R = 1
    lbp_total = numpy.zeros(mask_both.shape)
    # LBPs are inherently 2D. For 3D data, loop over the z slices
    # This function always gets a 3D bounding box, for 2D data
    # the box will have a singleton dimension
    for iz in range(image.shape[axes.z]):
        # an lbp image
        bboxkey = [slice(None)] * 3
        bboxkey[axes.z] = iz
        bboxkey = tuple(bboxkey)
        lbp_total[bboxkey] = ft.local_binary_pattern(rawbbox[bboxkey], P, R, "uniform")
    # extract relevant parts
    lbp_incl = lbp_total[mask_both]
    lbp_excl = lbp_total[mask_neigh.astype(bool)]
    lbp_obj = lbp_total[mask_object.astype(bool)]
    lbp_hist_incl, _ = numpy.histogram(lbp_incl, normed=True, bins=(P + 2), range=(0, P + 2))
    lbp_hist_excl, _ = numpy.histogram(lbp_excl, normed=True, bins=(P + 2), range=(0, P + 2))
    lbp_hist_obj, _ = numpy.histogram(lbp_obj, normed=True, bins=(P + 2), range=(0, P + 2))
    result = {}
    result["lbp" + self.local_out_suffixes[1]] = lbp_hist_incl
    result["lbp" + self.local_out_suffixes[0]] = lbp_hist_excl
    result["lbp"] = lbp_hist_obj
    return result