Overview of image processing Session1.pptx

Prof. Yogesh N, Dept. of CSD, ATMECE
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Computer Graphics & Fundamentals of
Image Processing - (21CS63)
Course Coordinator:
Prof. Yogesh N
Assistant Professor
Dept. of CSD
ATMECE, Mysuru

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Module 4
Introduction to Image processing: overview, Nature of IP, IP and its related fields, Digital Image
representation, types of images. Digital Image Processing Operations: Basic relationships and distance
metrics, Classification of Image processing Operations.
Text book 2: Chapter 1, 3
Experiential learning: Computer vision and OpenCV: What is computer vision, Evolution of computer
vision, Application of Computer vision, Feature of OpenCV, OpenCV library modules, OpenCV environment,
Reading, writing and storing images using OpenCV. OpenCV drawing Functions. OpenCV Geometric
Transformations.

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Session 1
Introduction to Image processing
• Overview
• Nature of IP
• IP and its related fields

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Overview of Image Processing
 Computers are faster and more accurate than human beings in processing numerical data. However, human
beings score over computers in recognition capability.
 The human brain is so sophisticated that we recognize objects in a few seconds without much difficulty.
 Human beings use all the five sensory organs to gather knowledge about the outside world.
 Among these perceptions, visual information plays a major role in understanding the surroundings.
 Other kinds of sensory information are obtained from hearing, taste, smell and touch.
 With the advent of cheaper digital cameras and computer systems, we are witnessing a powerful digital
revolution, where images are being increasingly used to communicate ideas effectively.

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 Images are encountered everywhere in our daily lives. We see many visual information sources such as
paintings and photographs in magazines, journals, image galleries, digital libraries, newspapers,
advertisement boards, television, and the Internet.
 Many of us take digital snaps of important events in our lives and preserve them as digital albums. Then
from the digital album, we print digital pictures or mail them to our friends to share our feelings of
happiness and sorrow.
 Images are not used merely for entertainment purposes. Doctors use medical images to diagnose problems
for providing treatment. With modern technology, it is possible to image virtually all anatomical
structures, which is of immense help to doctors in providing better treatment.
 Forensic imaging application process fingerprints, faces and irises to identify criminals.
 Industrial applications use imaging technology to count and analyse industrial components.

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 Remote sensing applications use images sent by satellites to locate the minerals present in the earth.
 Images are imitations of real-world objects. Image is a two-dimensional (2D) signal f(x, y), where the
values of the function f(x, y) represent the amplitude or intensity of the image.
 For processing using digital computers, this image has to be converted into a discrete form using the
process of sampling and quantization, known collectively as digitization.
 In image processing, the term ‘image’ is used to denote the image data that is sampled, quantized and
readily available in a form suitable for further processing by digital computers. Image processing is an
area that deals with manipulation of visual information.

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 Major objectives of image processing is to
1. Improve the quality of pictorial information for better human interpretation.
2. Facilitate the automatic machine interpretation of images.

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 Images are everywhere! Sources of Images are paintings, photographs in magazines, Journals, Image
galleries, digital Libraries, newspapers, advertisement boards, television and Internet.
 Images are imitations of Images.
 In image processing, the term ‘image’ is used to denote the image data that is sampled, quantized, and
readily available in a form suitable for further processing by digital computers.
 There are three scenarios or ways of acquiring an image
1. Reflective mode Imaging
2. Emissive Type Imaging
3. Transmissive Imaging
Nature of Image Processing

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 The radiation source shown in Fig. below is the light source.

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 Objects are perceived by the eye because of light.
 The sun, lamps, and clouds are all examples of radiation or light sources.
 The object is the target for which the image needs to be created.
 The object can be people, industrial components, or the anatomical structure of a patient.
 The objects can be two-dimensional, three-dimensional or multidimensional mathematical functions
involving many variables.
 For example, a printed document is a 2D object. Most real-world objects are 3D.

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Reflective Mode Imaging
 Reflective mode imaging represents the simplest form of imaging and uses a sensor to acquire the
digital image.
 All video cameras, digital cameras, and scanners use some types of sensors for capturing the image.
 Image sensors are important components of imaging systems.
 They convert light energy to electric signals.

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Emissive Type Imaging
 In Emissive type imaging , images are acquired from self-luminous objects without the help of a
radiation source .
 In emissive type imaging, the objects are self-luminous.
 The radiation emitted by the object is directly captured by the sensor to form an image.
 Thermal imaging is an example of emissive type imaging.
 In thermal imaging, a specialized thermal camera is used in low light situations to produce images
of objects based on temperature.
 Other examples of emissive type imaging are magnetic resonance imaging (MRI) and positron
emissive tomography (PET).

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Transmissive Imaging
 In Transmissive imaging, the radiation source illuminates the object.
 The absorption of radiation by the objects depends upon the nature of the material.
 Some of the radiation passes through the objects.
 The attenuated radiation is sensed into an image. This is called transmissive imaging.
 Examples of this kind of imaging are X-ray imaging, microscopic imaging, and ultrasound imaging.

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 The first major challenge in image processing is to acquire the image for further processing.
 Fig. below shows three types of processing – optical, analog and digital image processing.
Image Processing

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Optical Image Processing
 Optical image processing is the study of the radiation source, the object, and other optical processes
involved.
 It refers to the processing of images using lenses and coherent light beams instead of computers.
 Human beings can see only the optical image. An optical image is the 2D projection of a 3D scene. This is
a continuous distribution of light in a 2D surface and contains information about the object that is in
focus.
 This is the kind of information that needs to be captured for the target image.
 Optical image processing is an area that deals with the object, optics, and how processes are applied to an
image that is available in the form of reflected or transmitted light.
 The optical image is said to be available in optical form till it is converted into analog form.

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 An analog or continuous image is a continuous function f(x, y) where x and y are two spatial
coordinates.
 Analog signals are characterized by continuous signals varying with time. They are often referred to as
pictures.
 The processes that are applied to the analog signal are called analog processes.
 Analog image processing is an area that deals with the processing of analog electrical signals using
analog circuits.
 The imaging systems that use film for recording images are also known as analog imaging systems.
Analog Image Processing

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Digital Image Processing
 The analog signal is often sampled, quantized and converted into digital form using digitizer.
 Digitization refers to the process of sampling and quantization.
 Sampling is the process of converting a continuous-valued image f(x, y) into a discrete image, as
computers cannot handle continuous data. So the main aim is to create a discretized version of the
continuous data.
 Sampling is a reversible process, as it is possible to get the original image back.
 Quantization is the process of converting the sampled analog value of the function f(x, y) into a discrete-
valued integer.

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 Digital image processing is an area that uses digital circuits, systems and software algorithms to carry
out the image processing operations.
 The image processing operations may include quality enhancement of an image, counting of objects,
and image analysis.
Digital Image Processing

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Reasons for Popularity of DIP
1. It is easy to post process the image. Small corrections can be made in the captured image using
software.
2. It is easy to store the image in the digital memory.
3. It is possible to transmit the image over networks. So sharing an image is quite easy.
4. A digital image does not require any chemical process. So it is very environment friendly, as harmful
film chemicals are not required or used.
5. It is easy to operate a digital camera.

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 Image processing is an exciting interdisciplinary field that borrows ideas freely from many fields.
 Fig. below illustrates the relationships between image processing and other related fields.
Image Processing and Related Fields

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Image Processing and Computer Graphics
 Computer graphics and image processing are very closely related areas.
 Image processing deals with raster data or bitmaps, whereas computer graphics primarily deals
with vector data.
 Raster data or bitmaps are stored in a 2D matrix form and often used to depict real images.
 Vector images are composed of vectors, which represent the mathematical relationships between the
objects.
 Vectors are lines or primitive curves that are used to describe an image.
 Vector graphics are often used to represent abstract, basic line drawings.

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 The algorithms in computer graphics often take numerical data as input and produce an image as
output. However, in image processing, the input is often an image.
 The goal of image processing is to enhance the quality of the image to assist in interpreting it.
 Hence, the result of image processing is often an image or the description of an image. Thus, image
processing is a logical extension of computer graphics and serves as a complementary field.

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Image Processing and Signal Processing
 Human beings interact with the environment by means of various signals.
 In digital signal processing, one often deals with the processing of a one-dimensional signal.
 In the domain of image processing, one deals with visual information that is often in two or more
dimensions. Therefore, image processing is a logical extension of signal processing.

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Image Processing and Machine Vision
 The main goal of machine vision is to interpret the image and to extract its physical, geometric, or
topological properties. Thus, the output of image processing operations can be subjected to more
techniques, to produce additional information for interpretation.
 Artificial vision is a vast field, with two main subfields –machine vision and computer vision.
 The domain of machine vision includes many aspects such as lighting and camera, as part of the
implementation of industrial projects, since most of the applications associated with machine vision
are automated visual inspection systems.

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 The applications involving machine vision aim to inspect a large number of products and achieve
improved quality controls.
 Computer vision tries to mimic the human visual system and is often associated with scene
understanding.
 Most image processing algorithms produce results that can serve as the first input for machine
vision algorithms.

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Image Processing and Video processing
 Image processing is about still images.
 Analog video cameras can be used to capture still images.
 A video can be considered as a collection of images indexed by time.
 Most image processing algorithms work with video readily. Thus, video processing is an extension of
image processing.
 Images are strongly related to multimedia, as the field of multimedia broadly includes the study of
audio, video, images, graphics and animation.

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Image Processing and Optics
 Optical image processing deals with lenses, light, lighting conditions, and associated optical circuits.
 The study of lenses and lighting conditions has an important role in study of image processing.

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Image Processing and Statistics
 Image analysis is an area that concerns the extraction and analysis of object information from the
image.
 Imaging applications involve both simple statistics such as counting and mensuration and complex
statistics such as advanced statistical inference. So statistics plays an important role in imaging
applications.
 Image understanding is an area that applies statistical inferencing to extract more information from
the image.

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Summary
• In today’s session, you all have gone through the following topics
• Introduction to Image processing
• Overview
• Nature of IP
• IP and its related fields

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Discussion and Interaction

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Topics for Next Session
• Introduction to Image processing
• Digital Image representation
• Types of images

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Overview of image processing Session1.pptx

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