DIP PROJECTS USING MATLAB

     DIP Projects Using Matlab is the best way to implement Image processing applications and concepts. It is an interesting field due to its advanced pictorial information of human interpretation and processing of large image data for the purpose of transmission, storage and representation of machine perception. Matlab is the best platform for the implementation of Image processing concept. We have well experienced and certified developers who can work on any concept of Image processing using Matlab. Up to now, we have developed 1000+ projects in Matlab, which has made our developers versatile and multitalented. We provide support to students from all over the world through our online guidance and support. If you want to upgrade your profile and stand among the top researchers of the world, you can approach us anytime.

DIP PROJECTS USING MATLAB

     DIP Projects Using Matlab is an emerging field due to its recent development and extension to other fields of science and technology. Many research scholars and students get attracted towards this domain due to its emerging need and applications. To know more about this domain, we need to understand the purpose of Image processing. To be brief, we can say that the major purpose of Image processing is Visualization, Image sharpening and restoration, Image recognition, Image retrieval and pattern identification. We go for Image processing methods due to its versatility and preservation of original data precision. We have presented few major Image processing methods for student’s reference.

Major Methods involved in Image processing applications:

Image preprocessing and Enhancement:

  • Contrast enhancement and stretching(edge enhancement, gamma value adjustment, decorrelation stretching, Histogram equalization )
  • Noise filtering(Low pass, Mean and median filters, high pass, linear and adaptive filters )
  • Color adjustment and conversion(Psuedo-coloring, Gray to RGB conversion, CIE color modeling etc)
  • Sharpening and magnifying (Remap functions, deblurring, resizing )
  • Filters with Morphological operators (Dilation and closure, opening and closing)
  • Image filtering and arithmetic’s(convolution, correlation, edge preserving filters)

Image analysis :

  • Object ,texture and shape analysis(Quad tree decomposition, boundary tracing, Entropy and standard deviation filtering, gray level co-occurrence matrix)
  • Image quality analysis(Peak signal to noise ratio, SSIM image quality metrics, Mean squared error)
  • Image transforms(Fourier, radon, Hough, fan beam transforms)
  • Device independent color management

Image segmentation:

  • Edge detection(Canny method)
  • Region growing and segmentation
  • Thresholding(Otsu’s method)
  • Morphological Operators(Watershed segmentation)
  • Color based segmentation(K-mean clustering)
  • Texture segmentation using texture filters
  • Graph partition methods
  • Multi scale and model based segmentation
  • Variational methods and partial differential equation based method

Feature Extraction and selection methods(Shape, color, texture):

  • Principal component analysis
  • Multi-factor dimensionality reduction
  • Partial least squares
  • Latent semantic analysis
  • Non linear dimensionality reduction
  • Multi-linear subspace learning

Image classification techniques(supervised and unsupervised learning techniques):

  • Artificial neural networks
  • Decision tree
  • Fuzzy logics
  • Support vector machine
  • Post processing applications (Output as image and data)-Apply required method

Major applications of Image processing:

  • Intelligent transportation system
  • Remote sensing(City planning, flood control, resource mobilization, agricultural production monitoring)
  • Textiles and forensic studies
  • Graphics arts and printing Industry
  • Material science and non destructive evaluation
  • Military applications
  • Film industry and document processing
  • Moving object tracking(Motion based and recognition based tracking)
  • Aerial surveillance (Land and ocean surveillance)
  • Automatic visual inspection system (Incandescent lamp filaments, surface inspection systems,  faulty component identification )
  • Biomedical Imaging techniques(Heart and lung disease identification, digital mammograms –Imaging tools like CT, X-ray ,MRI and ultrasound)

 

       We have provided overall information about the major methods and applications of Image processing.  DIP projects are mainly based on one among the few concepts and methods. If students have a glance over it, they can have a clear idea about their project. If students have any other query or doubt, they can click their mails, we will be back to them.