diff --git a/ext/opencv/mat.cpp b/ext/opencv/mat.cpp
index 9fbbb28..45e6077 100644
--- a/ext/opencv/mat.cpp
+++ b/ext/opencv/mat.cpp
@@ -1247,6 +1247,7 @@ namespace rubyopencv {
       rb_define_method(rb_klass, "gaussian_blur", RUBY_METHOD_FUNC(rb_gaussian_blur), -1); // in ext/opencv/mat_imgproc.cpp
       rb_define_method(rb_klass, "median_blur", RUBY_METHOD_FUNC(rb_median_blur), 1); // in ext/opencv/mat_imgproc.cpp
       rb_define_method(rb_klass, "threshold", RUBY_METHOD_FUNC(rb_threshold), 3); // in ext/opencv/mat_imgproc.cpp
+      rb_define_method(rb_klass, "adaptive_threshold", RUBY_METHOD_FUNC(rb_adaptive_threshold), 5); // in ext/opencv/mat_imgproc.cpp
 
       rb_define_method(rb_klass, "save", RUBY_METHOD_FUNC(rb_save), -1);
 
diff --git a/ext/opencv/mat_imgproc.cpp b/ext/opencv/mat_imgproc.cpp
index 07adf5f..56ff53d 100644
--- a/ext/opencv/mat_imgproc.cpp
+++ b/ext/opencv/mat_imgproc.cpp
@@ -311,5 +311,37 @@ namespace rubyopencv {
       }
       return ret;
     }
+
+    /*
+     * Applies an adaptive threshold to an array.
+     *
+     * @overload adaptive_threshold(max_value, adaptive_method, threshold_type, block_size, delta)
+     *   @param max_value [Number] Non-zero value assigned to the pixels for which the condition is satisfied.
+     *   @param adaptive_method [Integer] Adaptive thresholding algorithm to use.
+     *   @param threshold_type [Integer] Thresholding type that must be either <tt>THRESH_BINARY</tt>
+     *     or <tt>THRESH_BINARY_INV</tt>.
+     *   @param block_size [Integer] Size of a pixel neighborhood that is used to calculate a threshold value
+     *     for the pixel: 3, 5, 7, and so on.
+     *   @param delta [Number] Constant subtracted from the mean or weighted mean.
+     *     Normally, it is positive but may be zero or negative as well.
+     *   @return [Mat] Destination image of the same size and the same type as <tt>self</tt>.
+     * @opencv_func cv::adaptiveThreshold
+     */
+    VALUE rb_adaptive_threshold(VALUE self, VALUE max_value, VALUE adaptive_method, VALUE threshold_type,
+				VALUE block_size, VALUE delta) {
+      cv::Mat* selfptr = obj2mat(self);
+      cv::Mat* dstptr = NULL;
+      try {
+	dstptr = new cv::Mat();
+	cv::adaptiveThreshold(*selfptr, *dstptr, NUM2DBL(max_value), NUM2INT(adaptive_method),
+			      NUM2INT(threshold_type), NUM2INT(block_size), NUM2DBL(delta));
+      }
+      catch (cv::Exception& e) {
+	delete dstptr;
+	Error::raise(e);
+      }
+
+      return mat2obj(dstptr, CLASS_OF(self));
+    }
   }
 }
diff --git a/ext/opencv/mat_imgproc.hpp b/ext/opencv/mat_imgproc.hpp
index d2e4514..10ef711 100644
--- a/ext/opencv/mat_imgproc.hpp
+++ b/ext/opencv/mat_imgproc.hpp
@@ -15,6 +15,8 @@ namespace rubyopencv {
     VALUE rb_gaussian_blur(int argc, VALUE *argv, VALUE self);
     VALUE rb_median_blur(VALUE self, VALUE ksize);
     VALUE rb_threshold(VALUE self, VALUE threshold, VALUE max_value, VALUE threshold_type);
+    VALUE rb_adaptive_threshold(VALUE self, VALUE max_value, VALUE adaptive_method,
+				VALUE threshold_type, VALUE block_size, VALUE delta);
   }
 }
 
diff --git a/ext/opencv/opencv_const.cpp b/ext/opencv/opencv_const.cpp
index 21c7c81..cc771c1 100644
--- a/ext/opencv/opencv_const.cpp
+++ b/ext/opencv/opencv_const.cpp
@@ -386,5 +386,8 @@ namespace rubyopencv {
     rb_define_const(rb_module, "THRESH_MASK", INT2FIX(cv::THRESH_MASK));
     rb_define_const(rb_module, "THRESH_OTSU", INT2FIX(cv::THRESH_OTSU));
     rb_define_const(rb_module, "THRESH_TRIANGLE", INT2FIX(cv::THRESH_TRIANGLE));
+
+    rb_define_const(rb_module, "ADAPTIVE_THRESH_MEAN_C", INT2FIX(cv::ADAPTIVE_THRESH_MEAN_C));
+    rb_define_const(rb_module, "ADAPTIVE_THRESH_GAUSSIAN_C", INT2FIX(cv::ADAPTIVE_THRESH_GAUSSIAN_C));
   }
 }
diff --git a/test/test_mat_imgproc.rb b/test/test_mat_imgproc.rb
index 229d463..3700320 100755
--- a/test/test_mat_imgproc.rb
+++ b/test/test_mat_imgproc.rb
@@ -284,10 +284,48 @@ class TestCvMat < OpenCVTestCase
     assert_raise(TypeError) {
       m0.threshold(25, 255, DUMMY_OBJ)
     }
+
     # m0 = Cv::imread(FILENAME_LENA256x256, 0)
     # m = m0.threshold(127, 255, Cv::THRESH_BINARY)
     # w = Window.new('Original | Binary')
     # w.show(Cv::hconcat([m0, m]))
     # Cv::wait_key
   end
+
+  def test_adaptive_threshold
+    m0 = Cv::Mat.new(2, 2, Cv::CV_8U)
+    m0[0, 0] = Cv::Scalar.new(10)
+    m0[0, 1] = Cv::Scalar.new(20)
+    m0[1, 0] = Cv::Scalar.new(30)
+    m0[1, 1] = Cv::Scalar.new(40)
+
+    expected = "<Cv::Mat:2x2,depth=0,channels=1,\n[  0,   0;\n 255, 255]>"
+    m = m0.adaptive_threshold(255, Cv::ADAPTIVE_THRESH_MEAN_C, Cv::THRESH_BINARY, 3, 0)
+    assert_equal(expected, m.to_s)
+
+    m = m0.adaptive_threshold(255, Cv::ADAPTIVE_THRESH_GAUSSIAN_C, Cv::THRESH_BINARY, 3, 0)
+    assert_equal(expected, m.to_s)
+
+    assert_raise(TypeError) {
+      m0.adaptive_threshold(DUMMY_OBJ, Cv::ADAPTIVE_THRESH_MEAN_C, Cv::THRESH_BINARY, 3, 0)
+    }
+    assert_raise(TypeError) {
+      m0.adaptive_threshold(255, DUMMY_OBJ, Cv::THRESH_BINARY, 3, 0)
+    }
+    assert_raise(TypeError) {
+      m0.adaptive_threshold(255, Cv::ADAPTIVE_THRESH_MEAN_C, DUMMY_OBJ, 3, 0)
+    }
+    assert_raise(TypeError) {
+      m0.adaptive_threshold(DUMMY_OBJ, Cv::ADAPTIVE_THRESH_MEAN_C, Cv::THRESH_BINARY, DUMMY_OBJ, 0)
+    }
+    assert_raise(TypeError) {
+      m0.adaptive_threshold(DUMMY_OBJ, Cv::ADAPTIVE_THRESH_MEAN_C, Cv::THRESH_BINARY, 3, DUMMY_OBJ)
+    }
+
+    # m0 = Cv::imread(FILENAME_LENA256x256, 0)
+    # m = m0.adaptive_threshold(255, Cv::ADAPTIVE_THRESH_MEAN_C, Cv::THRESH_BINARY, 25, 0)
+    # w = Window.new('Original | Binary')
+    # w.show(Cv::hconcat([m0, m]))
+    # Cv::wait_key
+  end
 end