MATLAB GUI PROJECTS
Graphical user Interface (GUI) allows the user to perform communal task using controls called components from the window. Matlab GUI Projects execute any type of calculations, write and read files and also communicate with other GUI. In Matlab GUI projects data’s are displayed as tables or plots.Feature extraction is nothing but grouping objects of same values in one category and the remaining values in other category. Image segmentation in Matlab GUI projects has become as target of present research.
2015 IEEE MATLAB GUI PROJECTS
- Automated Histology Analysis: Opportunities for signal processing.
- Pansharpening of Multispectral Images Based on Nonlocal Parameter Optimization.
- A High-Order Imaging Algorithm for High-Resolution Spaceborne SAR Based on a Modified Equivalent Squint Range Model.
- Rotational Pixel Swapping Method for Detection of Circular Features in Binary Images.
- Complementarity of Discriminative Classifiers and Spectral Unmixing Techniques for the Interpretation of Hyperspectral Images.
- Snakes on a Plane: A perfect snap for bioimage analysis.
- Progressive Band Processing of Constrained Energy Minimization for Subpixel Detection.
- Hyperspectral Image Denoising via Sparse Representation and Low-Rank Constraint.
- Unsupervised Band Selection by Integrating the Overall Accuracy and Redundancy.
- Efficient Compressed Sensing Method for Moving-Target Imaging by Exploiting the Geometry Information of the Defocused Results.
- Linear Spectral Mixture Analysis via Multiple-Kernel Learning for Hyperspectral Image Classification.
- An Efficient Approach for Automatic Rectangular Building Extraction From Very High Resolution Optical Satellite Imagery.
- Joint Image Registration and Fusion for Panchromatic and Multispectral Images.
- Accelerated Phase-Cycled SSFP Imaging With Compressed Sensing.
- A Novel Approach of an Absolute Encoder Coding Pattern.
- A Novel Multiple-Instance Learning-Based Approach to Computer-Aided Detection of Tuberculosis on Chest X-Rays.
- Indicator Cokriging-Based Subpixel Mapping Without Prior Spatial Structure Information.
- Rotation-Invariant Object Detection in Remote Sensing Images Based on Radial-Gradient Angle.
- No Reference Uneven Illumination Assessment for Dermoscopy Images.
- Data-Driven Soft Sensor Modeling for Algal Blooms Monitoring.
Criteria’s satisfying Image segmentation for MATLAB GUI PROJECTS:
- Every region should be uniform.
- Every region should be linked to set of pixels.
- Every pixel should be allotted to regions.
- Any mixed pair of neighbor regions must be non-uniform.
- Every pixel should belong to individual region only.
Favors of segmentation are:
- Does not care about the relationships between pixels of a feature.
- Pixels in group are based on global attribute.
- Provides relationships within pixels.
- Shoots the pixels of interest.
- Benefits involves recognition, measurement and detection.
Pixels are classified in a single class based on decision rule. Known techniques of classification are as below:
- Maximum likelihood classifier.
- Minimum distance classifier.
- Classifiers such as Fuzzy set theory and expert systems.
- Multi level slice classifier.
Filtering types are as follows:
- Noise removal by averaging filter.
- Noise removal by median filter.
Structured and semi structured documents like bills, payment drafts, invoices etc can be converted by using OCR/ICR applications.
It locates information’s located in stamps, logos, pictures, txt in images, including numbers and characters during recognition process.
For matching the fingerprints we use fingerprint hybrid matching algorithm in miniate information and texture form.
For extracting an image structure during preprocessing we can use thresholding method.
Pattern is a body, borely defined and also can be guien name.
Examples of pattern recognition are speech signal, DNA sequence, fingerprint image and handwritten word.
The two main approaches of noise reduction are: