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Hello, I'm having some problem with finding objects in the camera frame using edges or contours (in order to dismiss the background of the object) in android platform, I've tried to find keypoints in the Canny results of both the template image and the frame, and then matching them, but it doesn't work as I expected.

Is there a better way of doing it? If not please help me figure out what I'm missing.

Applying Canny on the reference image and finding keypoints:

Imgproc.Canny(mReferenceImage, referenceImageGray, 80, 90);
    mFeatureDetector.detect(referenceImageGray,
            mReferenceKeypoints);
    mDescriptorExtractor.compute(referenceImageGray,
            mReferenceKeypoints, mReferenceDescriptors);

Applying Cany on the frame and matching:

Imgproc.Canny(src, mGraySrc, 80, 90);
    mFeatureDetector.detect(mGraySrc, mSceneKeypoints);
    mDescriptorExtractor.compute(mGraySrc, mSceneKeypoints,
            mSceneDescriptors);
    mDescriptorMatcher.match(mSceneDescriptors,
            mReferenceDescriptors, mMatches);

After that a function is called to find the corners of the object:

private void findSceneCorners() {

    final List<DMatch> matchesList = mMatches.toList();
    if (matchesList.size() < 4) {
        // There are too few matches to find the homography.
        return;
    }

    final List<KeyPoint> referenceKeypointsList =
            mReferenceKeypoints.toList();
    final List<KeyPoint> sceneKeypointsList =
            mSceneKeypoints.toList();

    // Calculate the max and min distances between keypoints.
    double maxDist = 0.0;
    double minDist = Double.MAX_VALUE;
    for (final DMatch match : matchesList) {
        final double dist = match.distance;
        if (dist < minDist) {
            minDist = dist;
        }
        if (dist > maxDist) {
            maxDist = dist;
        }
    }

    // The thresholds for minDist are chosen subjectively
    // based on testing. The unit is not related to pixel
    // distances; it is related to the number of failed tests
    // for similarity between the matched descriptors.
    if (minDist > 50.0) {
        // The target is completely lost.
        // Discard any previously found corners.
        mSceneCorners.create(0, 0, mSceneCorners.type());
        return;
    } else if (minDist >= 25.0) {
        // The target is lost but maybe it is still close.
        // Keep any previously found corners.
        return;
    }

    // Identify "good" keypoints based on match distance.
    final ArrayList<Point> goodReferencePointsList =
            new ArrayList<Point>();
    final ArrayList<Point> goodScenePointsList =
            new ArrayList<Point>();
    final double maxGoodMatchDist = 1.75 * minDist;
    for (final DMatch match : matchesList) {
        if (match.distance < maxGoodMatchDist) {
           goodReferencePointsList.add(
                   referenceKeypointsList.get(match.trainIdx).pt);
           goodScenePointsList.add(
                   sceneKeypointsList.get(match.queryIdx).pt);
        }
    }

    if (goodReferencePointsList.size() < 4 ||
            goodScenePointsList.size() < 4) {
        // There are too few good points to find the homography.
        return;
    }

    // There are enough good points to find the homography.
    // (Otherwise, the method would have already returned.)

    // Convert the matched points to MatOfPoint2f format, as
    // required by the Calib3d.findHomography function.
    final MatOfPoint2f goodReferencePoints = new MatOfPoint2f();
    goodReferencePoints.fromList(goodReferencePointsList);
    final MatOfPoint2f goodScenePoints = new MatOfPoint2f();
    goodScenePoints.fromList(goodScenePointsList);

    // Find the homography.
    final Mat homography = Calib3d.findHomography(
            goodReferencePoints, goodScenePoints);

    // Use the homography to project the reference corner
    // coordinates into scene coordinates.
    Core.perspectiveTransform(mReferenceCorners,
            mCandidateSceneCorners, homography);

    // Convert the scene corners to integer format, as required
    // by the Imgproc.isContourConvex function.
    mCandidateSceneCorners.convertTo(mIntSceneCorners,
            CvType.CV_32S);

    // Check whether the corners form a convex polygon. If not,
    // (that is, if the corners form a concave polygon), the
    // detection result is invalid because no real perspective can
    // make the corners of a rectangular image look like a concave
    // polygon!

   if (Imgproc.isContourConvex(mIntSceneCorners)) {
        // The corners form a convex polygon, so ...