Workshop on Image Processing with Machine Learning

With the increasing attention in recent years, of interest to this special issue is research that demonstrates how machine learning algorithms have contributed, and are contributing to the research and applications of intelligent image processing. It is not difficult to enumerate a large number of successful examples of using machine learning in intelligent image processing, e.g., structured sparsity has been successfully applied to image and video modeling; human machine interactions significantly improve the performance of large scale image retrieval; sparse linear and multilinear subspace methods dramatically enhance the recognition rates in human behavior analysis and face synthesis; random fields and probabilistic graphical models show promising advantages in image and video analysis; graph cut and spectral clustering are widely applied to image segmentation; kernel machines, such as the support vector machines, are successfully used in visual tracking and handwriting recognition; and reinforcement learning is applied to visual texture synthesis.

It is the time to motivate image processing researchers and machine learning researchers to work together and pay more attention to each other’s field. Therefore, there is a chance to obtain significant performance improvement for practical utilizations of intelligent image processing by developing particular learning algorithms, and to bring in interesting utilizations of machine learning algorithms for particular intelligent image processing. The editors expect to gather a set of recent research outputs together, to report the progress of what is going on, and to build a forum for researchers to exchange their innovative ideas on machine learning in intelligent image processing.

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Fundamental (1 - day)

Prerequisites: Undergraduate-level mathematics and experience with basic computer operations

  1. Introduction to MATLAB
    • Historical background
    • Applications
    • Scope of MATLAB
    • Importance to engineers
    • Features
    • MATLAB windows (editor, work space, command history, • Command window)
    • Operations with variables
    • Naming and checking existence
  2. Data and data flow in MATLAB
    • Matrix operations & operators
    • Reshaping matrices
    • Arrays
  3. MATLAB Graphics
    • Simple graphics
    • Graphic types
    • Plotting functions


Technical Computing using MATLAB (Fundamentals)

Prerequisites: MATLAB Fundamentals

  1. Introduction to practical significance of Mathematics
  • Introduction to linear equation
  • Practical significance of roots and zeros
  • Convergence and divergence of series
  • Differentiation
  • Integration
  1. Zeros and Roots
  • Roots function
  • Iteration and Solution
  • Graphical representation
  1. Floating points and Decimal accuracy
  • Introduction to floating points
  • Effect of round off errors
  • Decimal accuracy


Programming in MATLAB (Intermediate)

Prerequisites: MATLAB Fundamentals

  • Flow Control
  • Conditional Statements
  • Error Handling
  • Working with Multidimensional Arrays
  • Cell Array and Character
  • Structure
  • Script Writing
  • Developing User Defined Function
  • Function handle and its application
  • Searching& Sorting


Building Graphical User Interface using MATLAB (Intermediate)

Prerequisites: MATLAB Fundamentals

  1. Introduction to GUIDE
  • Guide interface
  • Understanding of components
  • Sketching
  • Components properties
  1. Callback functions
  2. Application development
  • Designing of calculator
  • Plotting editor and many more
  1. GUI deployment
  • Developing MATLAB independent application


Signal Processing using MATLAB (Intermediate)

Prerequisites: MATLAB Fundamentals, Basic concept of signals

  1. Introduction to Signal Processing
  • Types of signals
  • Concept of frequency
  • Creating signals in MATLAB
  • Signal visualization
  1. Frequency analysis
  • Harmonics analysis
  • Energy and power of a signal
  • Frequency analysis of external sound
  1. Real time sound processing
  • Recording
  • Importing and exporting of signal (voice, music file etc.)
  • Audio player designing
  • Audio cutter designing
  1. Designing and implementation of filters
  • Introduction to various types of filters
  • Filter designing using tool
  • Filter applications on various signals
  • Producing sound effects


Image Processing using MATLAB (Intermediate)

Prerequisites: MATLAB Fundamentals

  1. Introduction to Image Processing
  • Types of images
  • Concept of frequency in images
  • Importing and exporting of images
  • Function based image processing
  1. Frequency analysis of image
  • Fourier transformation based frequency analysis
  • Frequency visualization by using different techniques
  1. Image processing based detection
  • Edge detection
  • Colour detection
  • Skin detection
  • Line detection
  1. Filtering in image processing
  • Spatial Filtering
  • Noise Filtering
  • Blur Filtering


Image Processing using MATLAB (Advance)

Prerequisites: MATLAB Fundamentals, Intermediate concept of image processing

  1. Morphological operation and its application
  • Erosion
  • Dilation
  • Opening
  • Closing
  • Contrast improvement
  • Image enhancement
  • Morphological reconstruction
  1. Object detection using image processing
  • Fundamentals of connected components
  • Unwanted area removing
  • Object detecting
  1. Region of interest (ROI) based image processing
  • Specifying a ROI
  • Filtering an ROI
  • Filling an ROI
  • Image enhancement


Video Processing using MATLAB (Advance)

Prerequisites: MATLAB Fundamentals, Intermediate concept of image processing

  1. Introduction to Image acquisition Toolbox
  • Introduction to video processing
  • Camera interfacing
  • Resources and hardware checking
  • Video taking and exploration
  1. Real time Image processing
  • Snapshot
  • Function based real time image processing
  1. Real time video processing based detection
  • Edge detection
  • Colour detection
  • Object detection***
  • Skin detection

*** Advance concept of image processing required


Technical Computing using MATLAB (Advance)

Prerequisites: MATLAB Fundamentals, Good at undergraduate level mathematics

  1. Introduction to practical significance of Mathematics
  • Introduction to linear equation
  • Practical significance of roots and zeros
  • Convergence and divergence of series
  1. Zeros and Roots
  • Roots function
  • Iteration and Solution
  • Bisection
  • Newton’s method
  1. Solution to linear equation
  • Matrix method
  • Gauss elimination technique
  1. Matrix and Factorization
  • Permutation matrix
  • Triangular matrices
  • LU Factorization
  1. Effect of round off errors
  • Decimal accuracy of a machine
  • Decimal accuracy of an algorithm
  1. Pivoting and its requirements
  • Importance
  • Partial
  • Diagonal
  1. Sensitivity and perturbation
  • Norms
  • Condition number
  • Residue


Implementation of Research Paper with MATLAB

  1. Introduction to Research Paper
  • Fundamentals
  • Paper Overview
  1. Implementation with MATLAB
  • Keywords Review
  • Flow chart of proposed methodologies
  • Approach to the paper
  • Implementation of the paper for its feasibility.

Scikit-learn & Machine Learning                           

Brief overview of the library                                                                                     

Overview of Machine Learning                                

  • What is Machine Learning
  • History and it’s Evolution

Types of Machine Learning                                                                                        

  • Supervised Machine Learning
  • Unsupervised Machine Learning


  • Linear Regression
  • Assumptions
  • Fitting of model
  • Interpretation of Parameters & it’s tuning
  • Logistic Regression
  • Assumptions
  • Fitting of Model
  • Interpretation of Parameters & it’s tuning

Decision trees                                                                                                                  

  • Classification and Regression Trees (CART)
  • Building a decision tree
  • Pruning of decision trees
  • Random Forest
  • What is Random Forest?
  • Why you need Random Forest?


  • K-means Algorithm
  • When & how to use K-means?

Dimensionality reduction                                                                                                           

  • Curse of Dimensionality
  • Principal Component Analysis(PCA)

Advanced Algorithms                                                                                                    

  • Support Vector Machines
  • Kernels
  • SVMs in Practice
  • Neural Networks
  • Motivation
  • Fitting a neural network
  • Classification using Neural nets
  • Parameter tuning
  • XG Boost and its Application
  • Classification using XGBoost
  • Features                                                                                                     
  • Feature Engineering
  • Feature Selection
  • Feature Scaling

Validation & Evaluation                                                                                              

  • Training/testing data split
  • Cross-Validation
  • Precision, Recall & F1 Score

Ensemble Methods                                                                                                                       

  • Why do we need Ensemble?
  • How to do Ensemble ?
  • Blending/Stacking

The Workshop content consists of an approximately equal mixture of lecture and hands-on lab. 

Recommendation: It is strongly recommended to bring your own LAPTOP during the training on which you can install and run programs if you would like to do the optional, hands-on experiments/exercises after the trainings/ workshops.

Certificates will be provided by ISO 9001:2008 certified I-Medita Learning Solutions Pvt. Ltd. Company which is registered with Ministry of Corporate Affairs for providing IT Trainings all over India and IBNC India which is a trademark championship that has already been executed in 106 Engineering colleges till March 2015.

  • Participation Certificate: Given to each candidate who participate in the workshop
  • Appreciation Certificate: Given to the College/ Institution who help in conducting the workshop.
  • Excellence Certificate: Given to the winner of the Zonal Center Championship.
  • Coordination Certificate: Given to those active & strong students and faculty coordinators who help in making the workshop and training successful.

IBNC Team gives you freedom to ask your relationship manager at IBNC India for certificate samples in advance so that you could be aware of the certificate you will be receiving before hand the trainings/workshops.

IBNC DVD to each participant to help them learn more about the workshop containing e books, presentations, videos and softwares after the training.

  • One on one interaction in the class with the Trainer.
  • Study material designed by panel of experts from industry.
  • Lead trainer will also have supporting trainers with him/her so that they can help you fall in love with the technology.
  • We believe in learning with fun!
  • The skills we develop are those that employers within the industry are looking for.
  • Covering theoretical and practical concepts in such a way that it is fun to learn the technology and easy to make unique projects.
  • Your skills and certifications are recognized anywhere in the world your career takes you.
  • Successfully trained over 15000 students in India till now.
  • Watch video testimonials given by students:
  • Check out our facebook page to see live comments from our prestigious students.
  • IBNC makes sure that you should get the full worth of the money you have paid during the trainings anywhere in India.

Please contact IBNC India Team to know more about careers in this technology.