Image Processing using Matlab Projects offers new methods to generate image processing application and sub domain using algorithms toenhance input and output images.Image process transforms one image into another. There are two different aspects. First is preparing image and later the prepared image should be improved by its appearance. Graph results can be obtained through image processing using Matlab Projects.

## Different Methods in Image Processing Using Matlab Projects:

Many methods are involved in this process they are:

- Filtering.
- Edge detection.
- Edge enhancement.
- Compression.
- Segmentation.

Further there are three models of color there are

- Device oriented color models.
- Device Independent color models.
- User oriented color models.

**Progress on Image Processing**:

Fourier transforms neighborhood operators, pyramid, wavelets and local operators contain image processing.

**Fourier transforms:**

Fast Fourier transformation (FFT) method is used to do Fourier analysis. Shift, superposition, domain scaling, Fourier transformation, convolution and correlation are the important properties of Fourier transformation.

**Neighborhood operator**:

Process of eliminating noise, image sharpening, blurring, and detection of edge is done by neighborhood operators. Non linear filtering, distance transforms, linear filtering, morphology and connected components are used by neighborhood operators in Image Processing using Matlab Projects.

**Pyramids and wavelets**:

An object of small instance can be found by using pyramid to from a search operation. Multi resolution geometric processing is done by wavelets.

**Local operators:**

It is the easy method of image processing. The output pixel value equals the input pixel. Gamma correction is done to eliminate mapping transforms of pixel, color, are some of the types or local operators.

**Image segmentation:**

It is the initial step in image analysis. It is difficult yet efficient task. Different types of image segmentation are

- Hybrid method.
- Edge based approach.
- Region based approach.
- Histogram thresholding.

Clustering otherwise known as grouping pixel element makes the process of segmentation effective. Many algorithms are used in clustering. Top down manner and bottom down manner is done by clustering. Segmentation process is used by Fuzzy clustering method in Image Processing using Matlab Projects. Some of the Fuzzy clustering improved algorithms are possiblistic clustering, modified fuzzy c- means, fuzzy possibilistic c- means and possibilistic fuzzy c- means.

**Future scope:**

More application based on more concepts to enlarge the future of computer security applications of image and video in Image Processing using Matlab Projects.