Java Mini Projects

    Java Mini Projects provide impressive solutions for a variety of real-world issues. Our organization offer a forum for research scholars and students those who working on Java projects. We share our knowledge through the latest developments. The prime objective of work is to develop high-quality and also original work that describing innovative ideas. Unlike other organizations, our organization emphasizes projects on interesting vision and significant ideas. Students are heartily encouraged to join our research journey as well as community. If you are at Java Mini Projects, contact us through mail or phone. We reach of you in few seconds. You can also communicate through Team Viewer. We are available at 24/7. Contact us TODAY.

Java Mini Projects

    Java Mini Projects have a huge potential in many research related aspects from project guidance to project documentation. Our top experts bring together students and research scholars from various streams (CSE, ECE, IT) to share novel methods and ideas on current and future use of technology in world. In this page, we provided computer vision based ideas and research methods on the following topics:

Java Mini Projects: List of Ideas

 Computer Vision

  • Binary Ops
  • Interpolation
  • Image Convolution
  • Image Derivatives
  • Color Space
  • Thresholding
  • Image Blur
  • Noise Removal
  • Fourier Transform
  • Fisheye Cameras
  • Equirectangular
  • Wavelet Decomposition
  • Discrete Image Pyramid

Geometric Operations

  • 3D Stereo Cloud
  • Video Mosaic
  • Video Stabilization
  • Camera Calibration
  • Mono Calibration
  • Remove Distortion
  • Video Stabilization
  • Video Mosaic
  • 3D Rotations
  • 2D/3D Transformations
  • Visual Odom: RGB-D, Mono Plane, Stereo


  • Interest Points
  • Line Detection
  • Motion Detection
  • Polygon Fitting
  • Dense Features
  • Super Pixels
  • Color Segmentation
  • Dense Optical Flow
  • Template Matching
  • Non-Max Suppression
  • Ellipse Fitting
  • Point Tracking
  • Feature detectors
  • Feature Tracking
  • Feature Matching
  • Feature Descriptors


  • Active Contours
  • Merge and Split
  • Mean Shift and Mode Finding
  • Energy based Methods
  • Graph Cuts

Feature based Alignment

  • 2D/3D feature based alignment
  • Pose estimation
  • Geometric intrinsic calibration

Structure from Motion

  • Factorization
  • Bundle Adjustment
  • Triangulation
  • Two Frame Structure from Motion
  • Constrained Structure and Motion

Image Stitching

  • Motion Models
  • Global Alignment
  • Compositing


  • KNN Classifier
  • Object Tracking
  • Face Recognition
  • Instance Recognition
  • Pedestrian Detection
  • CNN Classifiers
  • ANN Classifiers
  • Fuzzy Logic
  • Deep Learning Networks
  • Category Recognition
  • Pattern Recognition
  • Scene and Context Understanding

/* Sample Java Code for Ellipse Fitting */

// Source code

public  void ExampleFitEllipse( BufferedImage image ) {
GrayF32 input = ConvertBufferedImage.convertFromSingle(image, null, GrayF32.class);
GrayU8 binary = new GrayU8(input.width,input.height);

// Considered mean pixel value is often a suitable threshold when creating a binary image

double mean = ImageStatistics.mean(input);

// Create a binary image by thresholding

ThresholdImageOps.threshold(input, binary, (float) mean, true);

// Reduce noise with some filtering

GrayU8 filtered = BinaryImageOps.erode8(binary, 1, null);
filtered = BinaryImageOps.dilate8(filtered, 1, null);

// Find the contour around the shapes

List<Contour> contours = BinaryImageOps.contour(filtered, ConnectRule.EIGHT,null);

// Fit an ellipse to each external contour and draw the results

Graphics2D g2 = image.createGraphics();
g2.setStroke(new BasicStroke(3));
for( Contour c : contours ) {
FitData<EllipseRotated_F64> ellipse = ShapeFittingOps.fitEllipse_I32(c.external,0,false,null);
VisualizeShapes.drawEllipse(ellipse.shape, g2);
ShowImages.showWindow(VisualizeBinaryData.renderBinary(filtered, false, null),”Binary”,true);

    In this code, ellipses are fitted to 2D objects and the best fit ellipses found inside an image. Our goal is to fit ellipses to contours of binary blots more precisely. Let’s try it with the following topics,

  • On using shape features with ellipse fitting based on TB diagnosis of Microscope image processing system
  • A modern image processing method intended for floating benzene slick detection based on water surface
  • An effective mechanism for robust ellipse fitting algorithm derived from sparsity based on outliers
  • By using Gaussian Mixture Model with Error Ellipse function of Improved ISAC Algorithm to Retrieve Atmospheric Parameters commencing on HyTES Hyperspectral Images
  • An innovative method of NIR-based on gaze tracking among fast pupil ellipse fitting used for real-time wearable eye trackers
  • An Automatic identification function of blood vessel cross-section designed for central venous catheter placement by a cascading classifier system
  • An inventive process of ultra-short synthetic with experimental Heart Rate Variability series for Investigation of Lagged Poincaré Plot reliability system
  • On using binary support vector machine process based on categorization of human fall in top Viewed kinect depth images scheme
  • An inventive driving assistant safety method for anchored in human eye fatigue detection
  • The consequence extraction process of irregular dial device based on the image scheme
  • An effective Algorithm process used for sine wave cure fit derived from frequency precise estimation
  • The effectual performance for Automatic Fetal Head Circumference Measurement in Ultrasound via Random Forest with Fast Ellipse Fitting
  • On the use of Projective Invariant Pruning based on for Fast Ellipse Detector method