DIP PROJECTS

Digital image Processing means Processing digital images using digital computer. DIP Projects mainly concentrates on computer algorithms which performs processing over a digital image.The applications of DIP Projects are widely found in remote sensing, robotics, forensics, photography and medical imaging. Its main target is to achieve a high resolution and its characteristics from its original image. DIP Projects uses five different technique such as Projection, Classification, Feature extraction, Multi-scale signal Analysis, Pattern recognition.

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2015 IEEE DIP PROJECTS TITLES

• Aircraft Recognition in High-Resolution Optical Satellite Remote Sensing Images.
• Analysis of Laser Speckle Contrast Images Variability Using a Novel Empirical Mode Decomposition: Comparison of Results With Laser Doppler Flowmetry Signals Variability.
• Vibration Spectrum Imaging: A Novel Bearing Fault Classification Approach.
• Median Filtered Image Quality Enhancement and Anti-Forensics via Variational Deconvolution.
• Stereoscopic Visualization and 3-D Technologies in Medical Endoscopic Teleoperation.
• A Methodology for Visually Lossless JPEG2000 Compression of Monochrome Stereo Images.
• Geometrically Consistent Stereoscopic Image Editing Using Patch-Based Synthesis.
• Quantitative Error Analysis of Bilateral Filtering.
• Consistency-Driven Alternating Optimization for Multigraph Matching: A Unified Approach.
• Entertainment and Immersive Content: What’s in store for your viewing pleasure.
• Binocular Suppression-Based Stereoscopic Video Coding by Joint Rate Control With KKT Conditions for a Hybrid Video Codec System.
• Overview of Measurement Methods for Factors Affecting the Human Visual System in 3D Displays.
• Improving the Spatial Resolution of Landsat TM/ETM+ Through Fusion With SPOT5 Images via Learning-Based Super-Resolution.
• Correcting Incompatible DN Values and Geometric Errors in Nighttime Lights Time-Series Images.
• Analysis of Interactions Among Two Tropical Depressions and Typhoons Tembin and Bolaven (2012) in Pacific Ocean by Using Satellite Cloud Images.
• Quantitative Aspects of Single-Molecule Microscopy: Information-theoretic analysis of single-molecule data.
• MTSAT-1R Visible Imager Point Spread Function Correction, Part II: Theory.
• A Fast Integral Image Computing Hardware Architecture With High Power and Area Efficiency.
• Active Contour-Based Cell Segmentation During Freezing and Its Application in Cryopreservation.
• A Novel Stability Quantification for Disk Laser Welding by Using Frequency Correlation Coefficient Between Multiple-Optics Signals.
• Validation of a Nonrigid Registration Error Detection Algorithm Using Clinical MRI Brain Data.
• Superpixel Segmentation for Polarimetric SAR Imagery Using Local Iterative Clustering.
• A Fast Radial Scanned Near-Field 3-D SAR Imaging System and the Reconstruction Method.
• Extracting Man-Made Objects From High Spatial Resolution Remote Sensing Images via Fast Level Set Evolutions.
• A Self-Adaptive Wavelet-Based Algorithm for Wave Measurement Using Nautical Radar.
  

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STEPS INVOLVED IN DIP PROJECTS

• Projection.
• Classification.
• Feature extraction.
• Multi-scale signal Analysis.
• Pattern recognition.

Projection:

To project a three dimensional image onto a planar surface. We use projection technique, which is nothing but a set of rules, this technique does not use any numerical calculations which are used in technical drawing in Dip Projects.

Classification:

This technique uses the composition or condition of the target surface which relates to quantitative spectral information of an image.

Feature Extraction:

Extracting the edges of an image is very import because ,it detects the boundaries of the image.It is done by means of digital differentiation operation.

Multi-scale signal Analysis:

In  DIP Projects multi-scale signal analysis is most widely used which takes care of the applications, fundamental theory, implementation and algorithms of processing information’s which are contained in many symbolic, abstract or physical formats which are broadly known as signals.

Pattern Recognition:

Pattern recognition concentrates on the regularities in data and recognition of patterns. It is also same as machine learning.

DIP Projects uses classifiers namely clustering k-means classification, Bayes classifier, Support vector machine (SVM),Minimum distance classifier, Learning classifiers and k-nearest neighbor classifier.

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