image processing by using matlab

1- Mean Filter: also known as smoothing, averaging or box filter function [ outImg ] = meanFilter( inImg ) Mean filtering is a simple, intuitive and easy to implement method of smoothing images, i.e. reducing the amount of intensity variation between one pixel and the next. It is often used to reduce noise in images. The idea of mean filtering is simply to replace each pixel value in an image with the mean (average) value of its neighbors, including itself. This has the effect of eliminating pixel values that are unrepresentative of their surroundings. Note: Pay close attention to the pixels on the edge (first row, last row, first column, last column). How many neighboring pixels do they have?

2- Median Filter: reduce noise in an image, somewhat like the mean filter. function [ outImg ] = medianFilter( inImg ) Like the mean filter, the median filter replaces the value of each pixel. Instead of simply replacing the pixel value with the mean of neighboring pixel values, it replaces it with the median of those values. The median filter often does a better job than the mean filter of preserving useful detail in the image.

3- Nearest color: reduces the number of colors in an image function [ outImg ] = nearestColor( inImg, palette ) This function will replace each pixel with the nearest color from a predefined palette limited to eight colors of black, blue, red, magenta/purple, green, cyan, yellow and white. There are various methods for finding the nearest color. Here we will use the Euclidean distance method. The Euclidean distance method works by first calculating the difference between the separate RGB values of the actual color pixel and each of the colors available from the palette. These differences are then squared (to ensure there are no negative values) and added together to produce the distance. The palette color that has the smallest distance from the actual pixel color will be the nearest.

4- Binary image: similar to the blue-screening technique on TV and in movies function [ outImg1, outImg2, outImg3] = binaryMask( inImg ) Create three masks, three binary images outImg1, outImg2, outImg3 that represent the boundaries of three objects of interest in the input image inImg. A binary image has only values of 0 and 1, so only black and white pixels, no grey shades. To test this function, use the image wrenches.jpg. Each binary image should be the corresponding mask for one of the three wrenches in the image.

Matlab and Mathematica

Project ID: #7224044

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