This software has become the go to for signal and image processing studies, but in real it is a numerical computing environment. An additional feature of the software it’s GUI based application creation. MATLAB allows matrix manipulations, plotting of functions and data, implementation of algorithms, creation of user interfaces, and interfacing with programs written in other languages. MATLAB toolboxes are professionally developed, rigorously tested, and fully documented. MATLAB apps let you see how different algorithms work with your data. Iterate until you’ve got the results you want, then automatically generate a MATLAB program to reproduce or automate your work. There’s no need to rewrite your code or learn big data programming and out-of-memory techniques.
What is Digital Image Processing?
The Origins of Digital Image Processing.
Examples of Fields that use of Image processing
Fundamental steps in Digital Image Processing
Components of an Image Processing Systems
Image sensing and Acquisition
Image Sampling and Quantization
Pixel Relationships
Mathematical tools used in Digital Image Processing
Basics of Intensity transformations
Image negative
Log transformations
Power law transformations
Piece-wise linear transformations
Histogram Processing – Histogram Equalization and Histogram matching
The mechanics of Spatial filtering
Spatial correlation and convolution
Generating Spatial filter masks
Smoothing linear filter
Smoothing non-linear (Order statistics) filter
Sharpening filters – Laplacian filter, Unsharp masking, Highboost filtering
Sharpening non-linear filter – Gradient filter
Combining Spatial enhancement techniques
Fourier Transform
Discrete Fourier transform
2D Discrete Fourier transform
Image aliasing
Smoothing filters: Ideal lowpass, Butterworth lowpass and Gaussian lowpass filters
Sharpening filters: Ideal highpass, Butterworth highpass, Gaussian highpass filters and Homomorphic filtering
Selective filtering: Bandpass, Bandreject and Notch filters
Discrete Fourier transform and Inverse Discrete Fourier transform
Image degradation and restoration process
Noise models
Spatial filtering: Restoration in the presence of noise only – Mean filters, Order Statistics and Adaptive filters
Frequency filtering: Periodic noise reduction – Bandreject, Bandpass, Notch and Optimum notch filters
Inverse filtering
Wiener filtering
Constraint Least squares filtering
Geometric mean filtering
Image reconstruction by projections
Colour Fundamentals
Colour Models – RGB, CMY, CMYK and HIS models
Psuedocolour Image processing
Colour transformations
Smoothing and Sharpening
Image segmentation based on colour
Noise in colour images
Image pyramids
Subband coding
Haar transform
Fast wavelet transform
Wavelet packets
Types of redundancy: Coding, Spatial and Temporal
Irrelevant information and measuring image information
Fidelity criteria
Image compression models – Huffman, Golomb, Arithmetic, LZW, Run-length, Symbol-based, Bit-plane, Block transformation, Predictiv and Wavelet coding
Digital Image watermarking
Erosion and Dilation
Opening and Closing
Hit-or-miss transformation
Boundary extraction
Hole filling
Extraction of connected components
Convex Hull
Thinning
Thickening
Skeletons
Pruning
Morphological reconstruction
Gray-scale Morphology
Point detection
Line detection
Edge detection and edge models
Global thresholding
Variable thresholding
Adaptive thresholding
Region – based segmentation – Region growing and Region splitting & merging
Segmentation using morphological watersheds
Representation – Boundary following, Chain codes, Polygonal approximations using minimum perimeter polygons, signatures, boundary segments and skeleton
Boundary descriptors – Simple descriptors, Shape numbers, Fourier descriptors and statistical moments
Regional descriptors – Simple descriptors, topological descriptors, texture and Moment invariants
Relational descriptors
Pattern and pattern classes
Recognition based on Decision theoretic method – Matching, Optimum Statistical classifiers and Neural networks
Structural methods Matching shape numbers, String matching
Discrete time (DT) signals
Continuous time (CT) signals
Basic operations on DT and CT signals
Classification of the signals
Convolution of signals
Properties of Convolution
Linear Time invariance
Convolution sum
De-convolution
Linear Convolution and Circular Convolution
Discrete Fourier Transform (DFT) and Inverse Discrete Fourier Transform (IDFT)
Properties of DFT and IDFT
Linear filtering using DFT
Discrete Time Fourier Transform (DTFT) and Inverse Discrete Time Fourier Transform (IDTFT)
Properties of DTFT and IDTFT
Fast Fourier Transform (FFT)
Properties of FFT
DFT Computation using Decimation In Frequency -FFT and Decimation In Time –FFT
Unilateral Laplace Transform (LT)
Properties of LT
Inverse Laplace Transform
Solving differential equations
Time convolution of LT
Network analysis
Bilateral LT
Connection between LT, Fourier transform (FT) and Z transform
Region of Convergence (ROC)
Properties of ROC
Properties of Z transform
Inverse Z transform
Sampling process
Sampling theorem
Frequency domain sampling
Aliasing
Application of Sampling theore
Ideal filter characteristics
Symmetric and anti – symmetric filters
Design of Linear – phase FIR filters using windows: Rectangular, Hamming
Filter design – Frequency sampling
Design of analog filters: Chebyshev, Butterworth
Design of digital low pass IIR filter from analog filters: Impulse variance, Approximation of derivatives and Bilinear transformation
Multi-rate processing: Decimation, Interpolation and sampling
Weiner filter
Basics of Adaptive filters
FIR adaptive filters
Adaptive filters: Steepest descent method, LMS algorithm and normalized LMS algorithm
Signal Processing Plays a Key Role in Multiple Industries: Unlike in most fields of study, in signal processing, future jobs are not defined by or restricted to a single professional area. Signals are used to transmit information in nearly every imaginable field. Even processing images are in every field from small companies to space companies like NASA. To anyone keen to advance growth in signal processing and image processing this course is for you.
One best example of image processing is that it is used by many police departments to detect the vehicle plate numbers from images which do not follow traffic rules. Image processing is also used for exploring satellite images
Digital signal processing is used primarily in arenas of audio signal, speech processing, RADAR, seismology, audio, SONAR, voice recognition, and some financial signals. For example, Digital Signal Processing is used for speech compression for mobile phones, as well as speech transmission for mobile phones.
Matlab is very easy to understand and has syntactical structure similar to normal English language. It has many inbuilt functions and libraries for signal and image processing.
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