INTRODUCTION OF SIGNAL&
IMAGE PROCESSING USING MATLAB:
As a buddying engineer of Electronics and Telecommunications Engineering, ‘Signals’, ‘Processing’, ‘MATLAB’ have always been the big words interesting all of us.
A lecture on ‘Introduction of Signal and Image Processing using MATLAB’ was conducted on 5th October 2018 from 10:30AM to 2:30PM by Dr. Gargi S. Phadke, Department of Instrumentation Engineering(RAIT).
Signal is defined as an observable change in a quantifiable entity. In the physical world, any quantity exhibiting variation in time or variation in space (such as an image) is potentially a signal that might provide information on the status of a physical system, or convey
a message between observers, among other possibilities. Signal includes audio, video, speech, image, communication, geophysical, sonar, radar, medical and musical signals. There are two types ofsignals
– Analog and Digital. Dr Phadke explained with a simple example of a physical signal: ECG(Electrocardiography) where the continuous and discrete signals are processed and quantized to get the resultant digital
signal. A discussion regarding Convolution led to the conclusion about the importance of Linear and Visual Convolutions in Signal Processing.
This lecture informed us that over the years, MATLAB has become a very important aspect of Signal Processing. Correlation in MATLAB is used for the comparison of signals. One typical example of correlation is, ‘Two sensors at different locations measure vibrations caused by a car as it crosses a bridge. Load the signals and the sample rate, create time vectors and plot the signals. The signal from Sensor 2 arrives at an earlier time than the signal from Sensor 1.’ Where the cross-correlation of the two measurements is maximum at a lag equal to the delay and a
plot is generated to express the delay as several samples and in seconds.
A Sound signal is a 1-Dimension signal where we convert a signal into its frequency components, so that we can have a better analysis of that signal. Fourier Transform (FT) is used to convert a signal into its corresponding frequency domain. Filtering is used to remove noise from the sound. Filters have two types – IIR (Infinite Impulse Response) filter and FIR (Finite Impulse Response). In MATLAB tools such as filterBuilder and Wintool are used for filter design.
Discussions upon Image Processing started with the basics such as
‘What is an Image?’ Well, for a MATLAB Engineer its simply a matrix of integer values. An image signal is 2-Dimensional in nature which can be represented as a function F(x, y) where, F(x, y) gives the intensity at position (x, y). A colour image is 3 functions pasted together. Its vector valued function is given by,
A digital image is usually worked upon where the sample is a 2-D space on a regular grid and each sample is quantized – rounded to the nearest decimal. Images consists of small grid like structures called as pixels.
They determine the resolution and detail of the image. Images are enhanced using multiple methods such as Point Processing, Contrast stretching and Study of Image Histograms. Histograms are used to get
information about the occurrences of pixels in the image matrix which helps in achieving better storage efficiency and in filter applications.
In the lecture Dr Phadke also discussed about Video Signal Processing which is a Digital or Analog signal varying over time whose spatiotemporal contents represent a sequence of images (or frames) according to a predefined scanning convention. We were briefed about various constraints such as Video enhancement, Optical flow and Horn- Schunck algorithm. We then practiced the hands-on examples on computers and learnt various techniques such as the Block Matching
Technique, Fuzzy-C Cluttering, Local minimization, Real time video processing and video tracking.
Implementation of Signal processing and Image & Video processing was discussed in detail on how they can be used in the making of projects and their presentation in conferences.