diff --git a/ext/opencv/cvmat.cpp b/ext/opencv/cvmat.cpp
index 997cdb4..5287e4f 100644
--- a/ext/opencv/cvmat.cpp
+++ b/ext/opencv/cvmat.cpp
@@ -381,7 +381,6 @@ void define_ruby_class()
   rb_define_method(rb_klass, "draw_contours!", RUBY_METHOD_FUNC(rb_draw_contours_bang), -1);
   rb_define_method(rb_klass, "draw_chessboard_corners", RUBY_METHOD_FUNC(rb_draw_chessboard_corners), 3);
   rb_define_method(rb_klass, "draw_chessboard_corners!", RUBY_METHOD_FUNC(rb_draw_chessboard_corners_bang), 3);
-  rb_define_method(rb_klass, "pyr_segmentation", RUBY_METHOD_FUNC(rb_pyr_segmentation), 3);
   rb_define_method(rb_klass, "pyr_mean_shift_filtering", RUBY_METHOD_FUNC(rb_pyr_mean_shift_filtering), -1);
   rb_define_method(rb_klass, "watershed", RUBY_METHOD_FUNC(rb_watershed), 1);
 
@@ -4959,42 +4958,6 @@ rb_draw_chessboard_corners_bang(VALUE self, VALUE pattern_size, VALUE corners, V
   return self;
 }
 
-/*
- * call-seq:
- *   pyr_segmentation(<i>level, threshold1, threshold2</i>) -> [cvmat, cvseq(include cvconnectedcomp)]
- *
- * Does image segmentation by pyramids.
- * The pyramid builds up to the level <i>level<i>.
- * The links between any pixel a on <i>level<i>i and
- * its candidate father pixel b on the adjacent level are established if
- *   p(c(a),c(b)) < threshold1. After the connected components are defined, they are joined into several clusters. Any two segments A and B belong to the same cluster, if
- *   p(c(A),c(B)) < threshold2. The input image has only one channel, then
- *   p(c^2,c^2)=|c^2-c^2|. If the input image has three channels (red, green and blue), then
- *   p(c^2,c^2)=0,3*(c^2 r-c^2 r)+0.59*(c^2 g-c^2 g)+0,11*(c^2 b-c^2 b) . There may be more than one connected component per a cluster.
- *
- * Return segmented image and sequence of connected components.
- * <b>support single-channel or 3-channel 8bit unsigned image only</b>
- */
-VALUE
-rb_pyr_segmentation(VALUE self, VALUE level, VALUE threshold1, VALUE threshold2)
-{
-  IplImage* self_ptr = IPLIMAGE(self);
-  CvSeq *comp = NULL;
-  VALUE storage = cCvMemStorage::new_object();
-  VALUE dest = Qnil;
-  try {
-    dest = cIplImage::new_object(cvGetSize(self_ptr), cvGetElemType(self_ptr));
-    cvPyrSegmentation(self_ptr, IPLIMAGE(dest), CVMEMSTORAGE(storage), &comp,
-		      NUM2INT(level), NUM2DBL(threshold1), NUM2DBL(threshold2));
-  }
-  catch (cv::Exception& e) {
-    raise_cverror(e);
-  }
-  if (!comp)
-    comp = cvCreateSeq(CV_SEQ_CONNECTED_COMP, sizeof(CvSeq), sizeof(CvConnectedComp), CVMEMSTORAGE(storage));
-  return rb_ary_new3(2, dest, cCvSeq::new_sequence(cCvSeq::rb_class(), comp, cCvConnectedComp::rb_class(), storage));
-}
-
 /*
  * call-seq:
  *   pyr_mean_shift_filtering(<i>sp, sr[,max_level = 1][termcrit = CvTermCriteria.new(5,1)]</i>) -> cvmat
diff --git a/ext/opencv/cvmat.h b/ext/opencv/cvmat.h
index fae26c6..0878d2b 100644
--- a/ext/opencv/cvmat.h
+++ b/ext/opencv/cvmat.h
@@ -204,7 +204,6 @@ VALUE rb_draw_contours(int argc, VALUE *argv, VALUE self);
 VALUE rb_draw_contours_bang(int argc, VALUE *argv, VALUE self);
 VALUE rb_draw_chessboard_corners(VALUE self, VALUE pattern_size, VALUE corners, VALUE pattern_was_found);
 VALUE rb_draw_chessboard_corners_bang(VALUE self, VALUE pattern_size, VALUE corners, VALUE pattern_was_found);
-VALUE rb_pyr_segmentation(VALUE self, VALUE level, VALUE threshold1, VALUE threshold2);
 VALUE rb_pyr_mean_shift_filtering(int argc, VALUE *argv, VALUE self);
 VALUE rb_watershed(VALUE self, VALUE markers);
 VALUE rb_moments(int argc, VALUE *argv, VALUE self);
diff --git a/ext/opencv/iplimage.cpp b/ext/opencv/iplimage.cpp
index cf1d60e..baccc66 100644
--- a/ext/opencv/iplimage.cpp
+++ b/ext/opencv/iplimage.cpp
@@ -60,7 +60,7 @@ define_ruby_class()
   rb_define_method(rb_klass, "set_coi", RUBY_METHOD_FUNC(rb_set_coi), 1);
   rb_define_alias(rb_klass, "coi=", "set_coi");
   rb_define_method(rb_klass, "reset_coi", RUBY_METHOD_FUNC(rb_reset_coi), 0); 
-
+  rb_define_method(rb_klass, "pyr_segmentation", RUBY_METHOD_FUNC(rb_pyr_segmentation), 3);
   rb_define_method(rb_klass, "smoothness", RUBY_METHOD_FUNC(rb_smoothness), -1);
 
   rb_define_singleton_method(rb_klass, "decode_image", RUBY_METHOD_FUNC(rb_decode_image), -1);
@@ -591,6 +591,42 @@ high_pass_range(const IplImage *pImage, float lostPercentage, int &outLow, int &
   outLow = (int)(lostPercentage * outHigh);
 }
 
+/*
+ * call-seq:
+ *   pyr_segmentation(<i>level, threshold1, threshold2</i>) -> [iplimage, cvseq(include cvconnectedcomp)]
+ *
+ * Does image segmentation by pyramids.
+ * The pyramid builds up to the level <i>level<i>.
+ * The links between any pixel a on <i>level<i>i and
+ * its candidate father pixel b on the adjacent level are established if
+ *   p(c(a),c(b)) < threshold1. After the connected components are defined, they are joined into several clusters. Any two segments A and B belong to the same cluster, if
+ *   p(c(A),c(B)) < threshold2. The input image has only one channel, then
+ *   p(c^2,c^2)=|c^2-c^2|. If the input image has three channels (red, green and blue), then
+ *   p(c^2,c^2)=0,3*(c^2 r-c^2 r)+0.59*(c^2 g-c^2 g)+0,11*(c^2 b-c^2 b) . There may be more than one connected component per a cluster.
+ *
+ * Return segmented image and sequence of connected components.
+ * <b>support single-channel or 3-channel 8bit unsigned image only</b>
+ */
+VALUE
+rb_pyr_segmentation(VALUE self, VALUE level, VALUE threshold1, VALUE threshold2)
+{
+  IplImage* self_ptr = IPLIMAGE(self);
+  CvSeq *comp = NULL;
+  VALUE storage = cCvMemStorage::new_object();
+  VALUE dest = Qnil;
+  try {
+    dest = cIplImage::new_object(cvGetSize(self_ptr), cvGetElemType(self_ptr));
+    cvPyrSegmentation(self_ptr, IPLIMAGE(dest), CVMEMSTORAGE(storage), &comp,
+		      NUM2INT(level), NUM2DBL(threshold1), NUM2DBL(threshold2));
+  }
+  catch (cv::Exception& e) {
+    raise_cverror(e);
+  }
+  if (!comp) {
+    comp = cvCreateSeq(CV_SEQ_CONNECTED_COMP, sizeof(CvSeq), sizeof(CvConnectedComp), CVMEMSTORAGE(storage));
+  }
+  return rb_ary_new3(2, dest, cCvSeq::new_sequence(cCvSeq::rb_class(), comp, cCvConnectedComp::rb_class(), storage));
+}
 
 VALUE
 new_object(int width, int height, int type)
diff --git a/ext/opencv/iplimage.h b/ext/opencv/iplimage.h
index b564794..d1c53fd 100644
--- a/ext/opencv/iplimage.h
+++ b/ext/opencv/iplimage.h
@@ -38,6 +38,8 @@ VALUE rb_get_coi(VALUE self);
 VALUE rb_set_coi(VALUE self, VALUE coi);
 VALUE rb_reset_coi(VALUE self);
 
+VALUE rb_pyr_segmentation(VALUE self, VALUE level, VALUE threshold1, VALUE threshold2);
+
 VALUE rb_smoothness(int argc, VALUE *argv, VALUE self);
 typedef enum { SMOOTH = 1, BLANK = 2, MESSY = 3 } Smoothness;
 Smoothness compute_smoothness(const IplImage *pFourierImage, const double lowFreqRatio, const double blankDensity, const double messyDensity, const double highFreqRatio, double &outLowDensity, double &outHighDensity);
diff --git a/test/test_cvmat_imageprocessing.rb b/test/test_cvmat_imageprocessing.rb
index 3844a29..0f78c11 100755
--- a/test/test_cvmat_imageprocessing.rb
+++ b/test/test_cvmat_imageprocessing.rb
@@ -1316,44 +1316,6 @@ class TestCvMat_imageprocessing < OpenCVTestCase
     }
   end
 
-  def test_pyr_segmentation
-    mat0 = CvMat.load(FILENAME_LENA256x256, CV_LOAD_IMAGE_ANYCOLOR | CV_LOAD_IMAGE_ANYDEPTH)
-    mat1, seq1 = mat0.pyr_segmentation(4, 255, 50)
-    assert_equal('ebd9bad0bbc90b1d4a25289b7d59c958', hash_img(mat1))
-    assert_equal(5, seq1.total)
-
-    img0 = IplImage.load(FILENAME_CAT, CV_LOAD_IMAGE_ANYCOLOR | CV_LOAD_IMAGE_ANYDEPTH)
-    img0.set_roi(CvRect.new(0, 0, 256, 512))
-    img2, seq2 = img0.pyr_segmentation(2, 255, 50)
-    assert_equal('963b26f51b14f175fbbf128e9b9e979f', hash_img(img2))
-    assert_equal(11, seq2.total)
-
-    assert_raise(CvStsAssert) {
-      img0.pyr_segmentation(-1, 255, 50)
-    }
-    assert_raise(CvStsAssert) {
-      img0.pyr_segmentation(1000, 255, 50)
-    }
-    assert_raise(CvStsAssert) {
-      img0.pyr_segmentation(4, -1, 50)
-    }
-    assert_raise(CvStsAssert) {
-      img0.pyr_segmentation(4, 255, -1)
-    }
-    assert_raise(TypeError) {
-      img0.pyr_segmentation(DUMMY_OBJ, 255, 50)
-    }
-    assert_raise(TypeError) {
-      img0.pyr_segmentation(4, DUMMY_OBJ, 50)
-    }
-    assert_raise(TypeError) {
-      img0.pyr_segmentation(4, 255, DUMMY_OBJ)
-    }
-    assert_raise(CvBadDepth) {
-      IplImage.new(10, 10, :cv32f, 2).pyr_segmentation(4, 255, 50)
-    }
-  end
-
   def test_pyr_mean_shift_filtering
     mat0 = CvMat.load(FILENAME_LENA256x256, CV_LOAD_IMAGE_ANYCOLOR | CV_LOAD_IMAGE_ANYDEPTH)
     mat1 = mat0.pyr_mean_shift_filtering(30, 30)
diff --git a/test/test_iplimage.rb b/test/test_iplimage.rb
index c6eb3ee..0228709 100755
--- a/test/test_iplimage.rb
+++ b/test/test_iplimage.rb
@@ -198,6 +198,39 @@ class TestIplImage < OpenCVTestCase
       should_classify_images_as image, :blank
     end
   end
+
+  def test_pyr_segmentation
+    img0 = IplImage.load(FILENAME_CAT, CV_LOAD_IMAGE_ANYCOLOR | CV_LOAD_IMAGE_ANYDEPTH)
+    img0.set_roi(CvRect.new(0, 0, 256, 512))
+    img1, seq1 = img0.pyr_segmentation(2, 255, 50)
+    assert_equal('963b26f51b14f175fbbf128e9b9e979f', hash_img(img1))
+    assert_equal(11, seq1.total)
+
+    assert_raise(CvStsAssert) {
+      img0.pyr_segmentation(-1, 255, 50)
+    }
+    assert_raise(CvStsAssert) {
+      img0.pyr_segmentation(1000, 255, 50)
+    }
+    assert_raise(CvStsAssert) {
+      img0.pyr_segmentation(4, -1, 50)
+    }
+    assert_raise(CvStsAssert) {
+      img0.pyr_segmentation(4, 255, -1)
+    }
+    assert_raise(TypeError) {
+      img0.pyr_segmentation(DUMMY_OBJ, 255, 50)
+    }
+    assert_raise(TypeError) {
+      img0.pyr_segmentation(4, DUMMY_OBJ, 50)
+    }
+    assert_raise(TypeError) {
+      img0.pyr_segmentation(4, 255, DUMMY_OBJ)
+    }
+    assert_raise(CvBadDepth) {
+      IplImage.new(10, 10, :cv32f, 2).pyr_segmentation(4, 255, 50)
+    }
+  end
 end