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diff --git a/samples/java/tutorial_code/ImgTrans/distance_transformation/ImageSegmentationDemo.java b/samples/java/tutorial_code/ImgTrans/distance_transformation/ImageSegmentationDemo.java
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+import java.util.ArrayList;
+import java.util.List;
+import java.util.Random;
+
+import org.opencv.core.Core;
+import org.opencv.core.CvType;
+import org.opencv.core.Mat;
+import org.opencv.core.MatOfPoint;
+import org.opencv.core.Point;
+import org.opencv.core.Scalar;
+import org.opencv.highgui.HighGui;
+import org.opencv.imgcodecs.Imgcodecs;
+import org.opencv.imgproc.Imgproc;
+
+/**
+ *
+ * @brief Sample code showing how to segment overlapping objects using Laplacian filtering, in addition to Watershed
+ * and Distance Transformation
+ *
+ */
+class ImageSegmentation {
+    public void run(String[] args) {
+        //! [load_image]
+        // Load the image
+        String filename = args.length > 0 ? args[0] : "../data/cards.png";
+        Mat srcOriginal = Imgcodecs.imread(filename);
+        if (srcOriginal.empty()) {
+            System.err.println("Cannot read image: " + filename);
+            System.exit(0);
+        }
+
+        // Show source image
+        HighGui.imshow("Source Image", srcOriginal);
+        //! [load_image]
+
+        //! [black_bg]
+        // Change the background from white to black, since that will help later to
+        // extract
+        // better results during the use of Distance Transform
+        Mat src = srcOriginal.clone();
+        byte[] srcData = new byte[(int) (src.total() * src.channels())];
+        src.get(0, 0, srcData);
+        for (int i = 0; i < src.rows(); i++) {
+            for (int j = 0; j < src.cols(); j++) {
+                if (srcData[(i * src.cols() + j) * 3] == (byte) 255 && srcData[(i * src.cols() + j) * 3 + 1] == (byte) 255
+                        && srcData[(i * src.cols() + j) * 3 + 2] == (byte) 255) {
+                    srcData[(i * src.cols() + j) * 3] = 0;
+                    srcData[(i * src.cols() + j) * 3 + 1] = 0;
+                    srcData[(i * src.cols() + j) * 3 + 2] = 0;
+                }
+            }
+        }
+        src.put(0, 0, srcData);
+
+        // Show output image
+        HighGui.imshow("Black Background Image", src);
+        //! [black_bg]
+
+        //! [sharp]
+        // Create a kernel that we will use to sharpen our image
+        Mat kernel = new Mat(3, 3, CvType.CV_32F);
+        // an approximation of second derivative, a quite strong kernel
+        float[] kernelData = new float[(int) (kernel.total() * kernel.channels())];
+        kernelData[0] = 1; kernelData[1] = 1; kernelData[2] = 1;
+        kernelData[3] = 1; kernelData[4] = -8; kernelData[5] = 1;
+        kernelData[6] = 1; kernelData[7] = 1; kernelData[8] = 1;
+        kernel.put(0, 0, kernelData);
+
+        // do the laplacian filtering as it is
+        // well, we need to convert everything in something more deeper then CV_8U
+        // because the kernel has some negative values,
+        // and we can expect in general to have a Laplacian image with negative values
+        // BUT a 8bits unsigned int (the one we are working with) can contain values
+        // from 0 to 255
+        // so the possible negative number will be truncated
+        Mat imgLaplacian = new Mat();
+        Imgproc.filter2D(src, imgLaplacian, CvType.CV_32F, kernel);
+        Mat sharp = new Mat();
+        src.convertTo(sharp, CvType.CV_32F);
+        Mat imgResult = new Mat();
+        Core.subtract(sharp, imgLaplacian, imgResult);
+
+        // convert back to 8bits gray scale
+        imgResult.convertTo(imgResult, CvType.CV_8UC3);
+        imgLaplacian.convertTo(imgLaplacian, CvType.CV_8UC3);
+
+        // imshow( "Laplace Filtered Image", imgLaplacian );
+        HighGui.imshow("New Sharped Image", imgResult);
+        //! [sharp]
+
+        //! [bin]
+        // Create binary image from source image
+        Mat bw = new Mat();
+        Imgproc.cvtColor(imgResult, bw, Imgproc.COLOR_BGR2GRAY);
+        Imgproc.threshold(bw, bw, 40, 255, Imgproc.THRESH_BINARY | Imgproc.THRESH_OTSU);
+        HighGui.imshow("Binary Image", bw);
+        //! [bin]
+
+        //! [dist]
+        // Perform the distance transform algorithm
+        Mat dist = new Mat();
+        Imgproc.distanceTransform(bw, dist, Imgproc.DIST_L2, 3);
+
+        // Normalize the distance image for range = {0.0, 1.0}
+        // so we can visualize and threshold it
+        Core.normalize(dist, dist, 0, 1., Core.NORM_MINMAX);
+        Mat distDisplayScaled = dist.mul(dist, 255);
+        Mat distDisplay = new Mat();
+        distDisplayScaled.convertTo(distDisplay, CvType.CV_8U);
+        HighGui.imshow("Distance Transform Image", distDisplay);
+        //! [dist]
+
+        //! [peaks]
+        // Threshold to obtain the peaks
+        // This will be the markers for the foreground objects
+        Imgproc.threshold(dist, dist, .4, 1., Imgproc.THRESH_BINARY);
+
+        // Dilate a bit the dist image
+        Mat kernel1 = Mat.ones(3, 3, CvType.CV_8U);
+        Imgproc.dilate(dist, dist, kernel1);
+        Mat distDisplay2 = new Mat();
+        dist.convertTo(distDisplay2, CvType.CV_8U);
+        distDisplay2 = distDisplay2.mul(distDisplay2, 255);
+        HighGui.imshow("Peaks", distDisplay2);
+        //! [peaks]
+
+        //! [seeds]
+        // Create the CV_8U version of the distance image
+        // It is needed for findContours()
+        Mat dist_8u = new Mat();
+        dist.convertTo(dist_8u, CvType.CV_8U);
+
+        // Find total markers
+        List<MatOfPoint> contours = new ArrayList<>();
+        Mat hierarchy = new Mat();
+        Imgproc.findContours(dist_8u, contours, hierarchy, Imgproc.RETR_EXTERNAL, Imgproc.CHAIN_APPROX_SIMPLE);
+
+        // Create the marker image for the watershed algorithm
+        Mat markers = Mat.zeros(dist.size(), CvType.CV_32S);
+
+        // Draw the foreground markers
+        for (int i = 0; i < contours.size(); i++) {
+            Imgproc.drawContours(markers, contours, i, new Scalar(i + 1), -1);
+        }
+
+        // Draw the background marker
+        Imgproc.circle(markers, new Point(5, 5), 3, new Scalar(255, 255, 255), -1);
+        Mat markersScaled = markers.mul(markers, 10000);
+        Mat markersDisplay = new Mat();
+        markersScaled.convertTo(markersDisplay, CvType.CV_8U);
+        HighGui.imshow("Markers", markersDisplay);
+        //! [seeds]
+
+        //! [watershed]
+        // Perform the watershed algorithm
+        Imgproc.watershed(imgResult, markers);
+
+        Mat mark = Mat.zeros(markers.size(), CvType.CV_8U);
+        markers.convertTo(mark, CvType.CV_8UC1);
+        Core.bitwise_not(mark, mark);
+        // imshow("Markers_v2", mark); // uncomment this if you want to see how the mark
+        // image looks like at that point
+
+        // Generate random colors
+        Random rng = new Random(12345);
+        List<Scalar> colors = new ArrayList<>(contours.size());
+        for (int i = 0; i < contours.size(); i++) {
+            int b = rng.nextInt(256);
+            int g = rng.nextInt(256);
+            int r = rng.nextInt(256);
+
+            colors.add(new Scalar(b, g, r));
+        }
+
+        // Create the result image
+        Mat dst = Mat.zeros(markers.size(), CvType.CV_8UC3);
+        byte[] dstData = new byte[(int) (dst.total() * dst.channels())];
+        dst.get(0, 0, dstData);
+
+        // Fill labeled objects with random colors
+        int[] markersData = new int[(int) (markers.total() * markers.channels())];
+        markers.get(0, 0, markersData);
+        for (int i = 0; i < markers.rows(); i++) {
+            for (int j = 0; j < markers.cols(); j++) {
+                int index = markersData[i * markers.cols() + j];
+                if (index > 0 && index <= contours.size()) {
+                    dstData[(i * dst.cols() + j) * 3 + 0] = (byte) colors.get(index - 1).val[0];
+                    dstData[(i * dst.cols() + j) * 3 + 1] = (byte) colors.get(index - 1).val[1];
+                    dstData[(i * dst.cols() + j) * 3 + 2] = (byte) colors.get(index - 1).val[2];
+                } else {
+                    dstData[(i * dst.cols() + j) * 3 + 0] = 0;
+                    dstData[(i * dst.cols() + j) * 3 + 1] = 0;
+                    dstData[(i * dst.cols() + j) * 3 + 2] = 0;
+                }
+            }
+        }
+        dst.put(0, 0, dstData);
+
+        // Visualize the final image
+        HighGui.imshow("Final Result", dst);
+        //! [watershed]
+
+        HighGui.waitKey();
+        System.exit(0);
+    }
+}
+
+public class ImageSegmentationDemo {
+    public static void main(String[] args) {
+        // Load the native OpenCV library
+        System.loadLibrary(Core.NATIVE_LIBRARY_NAME);
+
+        new ImageSegmentation().run(args);
+    }
+}