Digital Image Processing Lecture Notes PDF

Mansoura University Faculty of Computers and Information Department of Information Technology First Semester- 2022-2023 Digital Image Processing Third Year (MI , SE ) Dr. Nehal Sakr  Lecture 1 Introduction Outline ❑ Course Guidelines ❑ Signal vs. Image ❑ Field...

Mansoura University Faculty of Computers and Information Department of Information Technology First Semester- 2022-2023 Digital Image Processing Third Year (MI , SE ) Dr. Nehal Sakr  Lecture 1 Introduction Outline ❑ Course Guidelines ❑ Signal vs. Image ❑ Fields related to Digital Image ❑ What is Digital Image Processing? ❑ Different Imaging Modalities ❑ Application Areas of Digital Image Processing ❑ Key Stages of Digital Image Processing ❑ Main Components of an image processing system  Lectures and Grading ❑ Course Meeting Time: According to Faculty Timetables (Thursday: 12:30 to 2 pm) ❑ Course Labs: Practical implementations using MATLAB. ❑ Grading : Activities Percentages Participation (Lectures and Labs) 5% Project 5% Practical Exam 10% Oral Exam 10% Midterm 10% Final Exam 60%  Course Objectives ❑ Course Goal: 1. To become familiar with digital image fundamentals 2. To get exposed to simple image enhancement techniques in Spatial and Frequency domain. 3. To learn concepts of degradation function and restoration techniques. 4. To study the image segmentation and representation techniques. 5. To become familiar with image compression and recognition methods Outline ❑ Course Guidelines ❑ Signal vs. Image ❑ Fields related to Digital Image ❑ What is Digital Image Processing? ❑ Different Imaging Modalities ❑ Application Areas of Digital Image Processing ❑ Key Stages of Digital Image Processing ❑ Main Components of an image processing system Signal vs. Image ❑ A signal is a mathematical function, and it conveys some information. ❑ A signal can be one dimensional or two dimensional or higher dimensional signal. ❑ One dimensional signal is a signal that is measured over time such as voice signal. ❑ The two dimensional signals are those that are measured over some other physical quantities such as a digital image.  Signal vs. Image ❑ An image may be defined as a two-dimensional function, f (x, y), where x and y are spatial (plane) coordinates. ❑ The amplitude of f at any pair of coordinates (x, y) is called the intensity or gray level of the image at that point. ❑ When x, y, and the intensity values of f are all finite, discrete quantities, we call the image a digital image. Outline ❑ Course Guidelines ❑ Signal vs. Image ❑ Fields related to Digital Image ❑ What is Digital Image Processing? ❑ Different Imaging Modalities ❑ Application Areas of Digital Image Processing ❑ Key Stages of Digital Image Processing ❑ Main Components of an image processing system  Fields related to Digital Image ❑ Computer Graphics: Creation of images synthetically. ❑ Image Processing: Enhancement or manipulation of the image – the result of which is usually another image. ❑ Video Processing (new!): Similar with image processing, but processing of multiple images/frames. ❑ Computer Vision: Analysis and understanding of image content. ❑ Pattern recognition: the automated recognition of patterns and regularities in data. Fields related to Digital Image Outline ❑ Course Guidelines ❑ Signal vs. Image ❑ Fields related to Digital Image ❑ What is Digital Image Processing? ❑ Different Imaging Modalities ❑ Application Areas of Digital Image Processing ❑ Key Stages of Digital Image Processing ❑ Main Components of an image processing system What is Digital Image Processing ? ❑ The field of digital image processing refers to processing digital images by means of a digital computer in order to either: Improve its pictorial information for human interpretation Humans like their images to be sharp, clear and detailed. Render it more suitable for autonomous machine perception. Machines prefer their images to be simple and uncluttered. What is Digital Image Processing ? ❑ The continuum from image processing to computer vision can be broken up into low-, mid- and high-level processes. Low Level Process Mid Level Process High Level Process Input: Image Input: Image Input: Attributes Output: Image Output: Attributes Output: Understanding Examples: Noise Examples: Object Examples: Scene removal, image recognition, segmentation understanding, sharpening autonomous navigation In this course we will stop here Outline ❑ Course Guidelines ❑ Signal vs. Image ❑ Fields related to Digital Image ❑ What is Digital Image Processing? ❑ Different Imaging Modalities ❑ Application Areas of Digital Image Processing ❑ Key Stages of Digital Image Processing ❑ Main Components of an image processing system  Different Imaging Modalities ❑ One of the simplest ways to develop a basic understanding of the extent of image processing applications is to categorize images according to their source (e.g., X-ray, visual, infrared, and so on). ❑ Energy sources for images include: ✓ Electromagnetic energy spectrum ✓ Acoustic ✓ ultrasonic ✓ Electronic (in the form of electron beams used in electron microscopy). ✓ Synthetic images, used for modeling and visualization, are generated by computer.  Different Imaging Modalities ❑ The principal energy source for images in use today is the electromagnetic energy spectrum. ❑ Electromagnetic waves can be conceptualized as propagating sinusoidal waves of varying wavelengths, or they can be thought of as a stream of massless particles, each traveling in a wavelike pattern and moving at the speed of light. ❑ Each massless particle contains a certain amount (or bundle) of energy. ❑ Each bundle of energy is called a photon.  Different Imaging Modalities ❑ If spectral bands are grouped according to energy per photon, we obtain the spectrum shown below, ranging from gamma rays (highest energy) at one end to radio waves (lowest energy) at the other. GAMMA-RAY Imaging X-RAY IMAGING IMAGING IN THE ULTRAVIOLET BAND IMAGING IN THE VISIBLE AND INFRARED BANDS Outline ❑ Course Guidelines ❑ Signal vs. Image ❑ Fields related to Digital Image ❑ What is Digital Image Processing? ❑ Different Imaging Modalities ❑ Application Areas of Digital Image Processing ❑ Key Stages of Digital Image Processing ❑ Main Components of an image processing system  Application Areas of Digital Image Processing ❑ Digital image processing techniques are now used in many fields such as: ✓ Medicine ✓ Agriculture ✓ Weather forecasting ✓ Security ✓ Banking ✓ Defense ✓ Industrial automation ✓ Forensics ✓ Underwater image restoration and enhancement Application Areas of Digital Image Processing ❖ Document Handling Application Areas of Digital Image Processing ❖ Signature Verification Application Areas of Digital Image Processing ❖ Biometrics Application Areas of Digital Image Processing ❖ Object Recognition Application Areas of Digital Image Processing ❖ Indexing into Databases Application Areas of Digital Image Processing ❖ Target Recognition Application Areas of Digital Image Processing ❖ Autonomous Vehicles Application Areas of Digital Image Processing ❖ Traffic Monitoring Application Areas of Digital Image Processing ❖ Face Detection Application Areas of Digital Image Processing ❖ Face Recognition Application Areas of Digital Image Processing ❖ Facial Expression Recognition Application Areas of Digital Image Processing ❖ Human Activity Recognition Application Areas of Digital Image Processing ❖ Medical Applications skin cancer breast cancer Outline ❑ Course Guidelines ❑ Signal vs. Image ❑ Fields related to Digital Image ❑ What is Digital Image Processing? ❑ Different Imaging Modalities ❑ Application Areas of Digital Image Processing ❑ Key Stages of Digital Image Processing ❑ Main Components of an image processing system Key Stages of Digital Image Processing Key Stages of Digital Image Processing Image Acquisition Image Acquisition ❑ Acquisition could be as simple as being given an image that is already in digital form. ❑ Generally, the image acquisition stage involves preprocessing, such as scaling. Key Stages of Digital Image Processing Image Enhancement Image Enhancement ❑ The process of manipulating an image so the result is more suitable than the original for a specific application. ❑ There is no general “theory” of image enhancement. ❑ When an image is processed for visual interpretation, the viewer is the ultimate judge of how well a particular method works. Key Stages of Digital Image Processing ❑ Image Restoration Image Restoration ❑ It is an area that also deals with improving the appearance of an image. ❑ Image Enhancement, is subjective; is based on human subjective preferences regarding what constitutes a “good” enhancement result. ❑ Image restoration, is objective; in the sense that restoration techniques tend to be based on mathematical or probabilistic models of image degradation. Key Stages of Digital Image Processing ❑ Color Image Processing  Color Image Processing ❑ It is an area that has been gaining in importance because of the significant increase in the use of digital images over the internet. ❑ Color is used also as the basis for extracting features of interest in an image. Key Stages of Digital Image Processing Wavelet Transform Wavelet and other Transforms ❑ Wavelet transforms, are the foundation for representing images in various degrees of resolution. ❑ It is used for image data compression and for pyramidal representation, in which images are subdivided successively into smaller regions. Key Stages of Digital Image Processing Image Compression  Compression and Watermarking ❑ Deals with techniques for reducing the storage required to save an image, or the bandwidth required to transmit it. ❑ Image compression is familiar to most users of computers in the form of image file extensions, such as the jpg file extension used in the JPEG (Joint Photographic Experts Group) image compression standard. Key Stages of Digital Image Processing Morphological Processing  Morphological Processing ❑ Is a collection of non-linear operations related to the shape or morphology of features in an image. ❑ Deals with tools for extracting image components that are useful in the representation and description of shape. ❑ Morphological operations rely only on the relative ordering of pixel values, not on their numerical values, therefore are especially suited to the processing of binary images. Key Stages of Digital Image Processing Segmentation  Segmentation ❑ Partitions an image into its constituent parts or objects. ❑ Autonomous segmentation is one of the most difficult tasks in digital image processing. ❑ Usually is raw pixel data, constituting either the boundary of a region (i.e., the set of pixels separating one image region from another) or all the points in the region. ❑ In general, the more accurate the segmentation, the more likely automated object classification is to succeed. Key Stages of Digital Image Processing Feature Extraction  Feature Extraction ❑ Always follows the output of a segmentation stage. ❑ Feature extraction consists of: Feature detection : refers to finding the features in an image, region, or boundary. Feature description: assigns quantitative attributes to the detected features.  Feature Extraction ❑ For example, we might detect corners in a region, and describe those corners by their orientation and location; both of these descriptors are quantitative attributes. ❑ Feature descriptors should be as insensitive as possible to variations in parameters such as scale, translation, rotation, illumination, and viewpoint. Key Stages of Digital Image Processing Image Pattern Classification  Image Pattern Classification ❑ Is the process that assigns a label (e.g., “vehicle”) to an object based on its feature descriptors. ❑ The methods of image pattern classification ranges from “classical” approaches such as minimum-distance, correlation, and Bayes classifiers, to more modern approaches implemented using deep neural networks. Key Stages of Digital Image Processing Knowledge Base Knowledge Base ❑ This knowledge may be: Simple such as detailing regions of an image where the information of interest is known to be located, thus limiting the search that has to be conducted in seeking that information. Complex such as an image database containing high-resolution satellite images of a region in connection with change-detection applications. ❑ Guides the operation of each processing module, the knowledge base also controls the interaction between modules. Outline ❑ Course Guidelines ❑ Signal vs. Image ❑ Fields related to Digital Image ❑ What is Digital Image Processing? ❑ Different Imaging Modalities ❑ Application Areas of Digital Image Processing ❑ Key Stages of Digital Image Processing ❑ Main Components of an image processing system Components of an image processing system Components of an image processing system  Components of an image processing system ❑ Image Sensors consists of two subsystems: The physical sensor A digitizer Components of an image processing system ❑ Specialized Image Processing Hardware: A digitizer Hardware that performs other primitive operations, such as an arithmetic logic unit (ALU) Components of an image processing system ❑ Computer: General purpose computer Customized computer Components of an image processing system ❑ Image Processing Software: specialized modules that perform specific tasks. Ex: MATLAB. Components of an image processing system ❑ Image Displays: Color, flat screen monitors. Components of an image processing system ❑ Mass Storage: Short-term storage for use during processing. On-line storage for relatively fast recall. Archival storage for infrequent access. Components of an image processing system ❑ Hardcopy devices: Laser printers. Film cameras. Heat-sensitive devices. Digital units such as CD-ROM disks. Components of an image processing system ❑ Network and Cloud Communication: Bandwidth for image transmission. Image data compression. Thank You