Projects for Digital Image Processing Using Matlab

Matlab is an image information tool which is used to specify a particular image in an efficient way. Projects for Digital Image Processing Using Matlab has more built-in functions to create and solve complex problems. Digital image processing concepts are implemented using matlab simulation.

 

Matlab Concepts:

  • File Naming Conventions
  • Reading and Writing Data Files
  • Matlab Command prompt

 

Techniques of Digital Image Processing:

  • Hidden Markov Models
  • Partial Differential Equations
  • Self-Organizing Maps
  • Wavelets
  • Pixelation

 

Characteristics of Digital Images:

  • Resolution.
  • Dynamic Range.
  • Picture Elements.

 

Need of Digital Image Processing:

  • Visualize.
  • Convenience.
  • Transmit/Store.
  • Practicality.
  • More Surveillance.
  • Machine Vision.

ADVANTAGES OF PROJECTS FOR DIGITAL IMAGE PROCESSING USING MATLAB:

 

N

Image segmentation is used to detect discontinuity

N

Image compression techniques should reduce an amount of data required to represent a digital image

N

Improve visual quality of an image and distribution of intensity

Dimensions of Digital Signals:

Digital images and signals are represented as several dimensions such as

  • 1-Dimension Signal – Waveform
  • 2-Dimension Signals – Image
  • 3-Dimension Signals – Animated Character
  • 4-Dimension Signals – Animated Movie

 

Object Recognition Models on DIP:

  • Template Matching
  • Image Segmentation and Blob Analysis
  • Bag-of-words models with features
  • Viola-Jones Algorithm
  • Extracted Features and Boosted Learning Algorithms

SYNATX ON PROJECTS FOR DIGITAL IMAGE PROCESSING USING MATLAB:

bag = bagOfFeatures(imgSet)
bag = bagOfFeatures(imgSet,’CustomExtractor’,extractorFcn)
bag = bagOfFeatures(imgSet,Name,Value)
Example Code for Create a Bag of Visual Words:
Load two image sets
setDir = fullfile(toolboxdir(‘vision’),’visiondata’,’imageSets’);
imgSets = imageSet(setDir, ‘recursive’);
To Create Training Set
trainingSets = partition(imgSets, 2);
Create Bag of Features
bag = bagOfFeatures(trainingSets,’Verbose’,false);
Compute & Store Histogram as Feature Vector
img = read(imgSets(1), 1);
featureVector = encode(bag, img);