Matlab Projects on Image Processing.

Matlab is a language for programming which solves problems related to linear algebra in matrix. Environment development, application programming, and mathematical function are the functions of Matlab systems. Problems on Computational and classification in Digital Image processing can be solved by Matlab Projects. Various concepts of Image processing such as geometric transformations, color corrections, registrations of more images, image editing, digital retouching, segmentation, interpolation and recovery of images are performed with Matlab simulation tool.

Segmentation Techniques carried in Matlab Projects.

Edge Based Segmentation: It is performed by identifying the edges. Discontinuities are recognized by this edge. There are many types of edge detectors that involves in the process of segmentation. They are sobel operators, prewit operators and canny edge detector.

Feature Based Clustering: It is a direct method involving images. It is otherwise known as grouping. This process needs cylindrical decision elements of color space. Clustering is defined as fuzzy clustering.

Threshold Based Segmentation: The input image is processed and split into parts. It is the most used segmentation method. It involves both global thresholding and local thresholding.By using global thresholding values such as 0 and 1 is obtained. Local thresholding splits images into parts and gets the threshold values.

Model Based Segmentation: It is otherwise known as Markov Random Field (MRF). The inbuilt region smoothness constraints processes color segmentation. MRF and various detection methods should come together to identify edges accurately.


Region Based Segmentation: Many images processing application uses this method for segmentation. It meaningfully splits the image parts. By this division the status of image is understood. Region growing and region splitting and merging are the types of region based segmentation.

Image Enhancement: Various features of an image are enhanced for human observers’ sake. Frequency domain method and spatial domain method are two types of Image enhancement method. Fourier transform is used in frequency domain method. Pixels of an image are manipulated and enhance in spatial domain method.

Image processing concepts:

Concepts of image Processing functions with the help of MATLAB simulation tools. Such concepts are as follows:

It is a display technique which solves the problems of limited display resources. It displays high rage intensities by exploiting spatial integration. Limited color display and low quantization display are the two important technique of application that helps in dithering.
Gaussian smoothing and Zero crossing edge detectors are some of the finest example of isotropic operators. This concept is applied in all directions of the image.
It is otherwise known as normal image space. The image space shifts and changes its position. This makes the intensity vary each and every time of shifting.
Both ground truth image and original image figure makes up binary images. A pixel of these has two different intensity values. The differences of these values are identified by black and white color. Processes such as segmentation, thinning, contour extraction, noise suppression, medical axis computation, feature extraction and component labeling is done by binary images.
Various objects can be segmented using edge detector. By this process an image is split into many parts. Edges with strong intensity contrast are detected through edge detection.
They are obtained from Boolean algebra. It is a way to transform true values by a mathematical method. Only with the help of logical operators a logical building blocks with texture images can be constructed.
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This is needed during the decrease of color information from an image. Through this 24bit color image is transformed as 8 bit color image. All color related problems is solved through this method.
Certain pixel value images are hidden in another image using masking. Steganography and cryptography algorithm is requiring giving security and performing data masking. With the help of pixel multiplication or logical AND operations masking can be done fastly.
Best Matlab Projects on Image Processing

Technique utilized in Matlab projects on Image Processing:

  • Background subtraction.
  • Morphological operations.
  • Intensity thresholding.
  • Shape based processing.
  • Color based processing.
Advantages of Matlab Projects on Image Processing:
  • Contains rich data type’s namely 3D matrix, cell array and complex number.
  • Available lots of users, mathematicians, economic and engineers.
  • Availability of high quality graphics and visualization.
  • To provide effective and easy handling tools for end users.
Total completed Communication Projects
2014-15.Completed Communication Projects
2015-16.Ongoing Communication Projects
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