This document provides an introduction to digital image processing. It defines what a digital image is as a finite set of pixels representing a two-dimensional scene. Digital image processing is described as focusing on improving images for human interpretation and processing images for machine perception. The history of digital image processing is outlined from early applications in newspapers to current uses in fields like medicine, astronomy, and industrial inspection. Key stages of digital image processing are identified as image acquisition, enhancement, restoration, morphological processing, segmentation, representation, object recognition, color processing, and compression.
This presentation discusses digital image processing. It begins with definitions of digital images and digital image processing. Digital image processing focuses on improving images for human interpretation and processing images for machine perception. The history of digital image processing is then reviewed from the 1920s to today. Key examples of applications like medical imaging, satellite imagery, and industrial inspection are provided. The main stages of digital image processing are outlined, including image acquisition, enhancement, restoration, segmentation, and compression. The document concludes with an overview of a system for automatic face recognition using color-based segmentation.
This document discusses digital image processing. It begins by defining a digital image and digital image processing. It then provides a brief history of digital image processing from the 1920s to today. Examples of digital image processing applications are given in various domains like medicine, geography, industrial inspection, law enforcement, human-computer interfaces, and art. Key stages of digital image processing like enhancement, segmentation, and understanding are also mentioned.
Digital image processing involves representing images as arrays of pixels and then processing those pixels to improve or analyze the image. It has applications in fields like medicine, mapping, law enforcement, and human-computer interfaces. The key stages of digital image processing include image acquisition, enhancement, restoration, morphological processing, segmentation, object recognition, representation and description, compression, and color image processing.
Digital images are represented as arrays of numbers called pixels. Each pixel value corresponds to attributes like intensity, color, or height at that location. Digital image processing involves techniques to enhance, analyze, and extract information from digital images for tasks like interpretation, transmission, and machine perception. It has evolved from early applications processing images from space missions and medical scans to now being used widely across fields such as entertainment, surveillance, and industrial inspection. Key stages in digital image processing typically involve image acquisition, enhancement, analysis through techniques like segmentation and recognition, and output of processed results.
Digital image processing involves techniques to improve and analyze digital images. It focuses on tasks like enhancing images for human interpretation, processing images for machine applications, and processing image data for storage and transmission. Key stages in digital image processing include image acquisition, enhancement, restoration, segmentation, and representation. Digital image processing has a long history and is now widely used in applications like medical imaging, satellite imagery analysis, industrial inspection, and law enforcement.
This document discusses digital image processing. It defines a digital image and digital image processing. The history of digital image processing is covered from the 1920s to today. Examples of applications are given, including image enhancement, medical imaging, industrial inspection, and more. The key stages of digital image processing are outlined, such as image acquisition, enhancement, restoration, segmentation, and others.
This document provides an introduction to a course on digital image processing. It discusses what a digital image is, defines digital image processing, and outlines the history and key applications of the field. The lecture will cover the definition of a digital image, the tasks of digital image processing, the history and evolution of the field from the 1920s to today, examples of applications in areas like medicine, satellite imagery, industrial inspection, and human-computer interfaces, and the main stages of digital image processing work including image acquisition, enhancement, restoration, and recognition.
This presentation discusses digital image processing. It begins with definitions of digital images and digital image processing. Digital image processing focuses on improving images for human interpretation and processing images for machine perception. The history of digital image processing is then reviewed from the 1920s to today. Key examples of applications like medical imaging, satellite imagery, and industrial inspection are provided. The main stages of digital image processing are outlined, including image acquisition, enhancement, restoration, segmentation, and compression. The document concludes with an overview of a system for automatic face recognition using color-based segmentation.
This document discusses digital image processing. It begins by defining a digital image and digital image processing. It then provides a brief history of digital image processing from the 1920s to today. Examples of digital image processing applications are given in various domains like medicine, geography, industrial inspection, law enforcement, human-computer interfaces, and art. Key stages of digital image processing like enhancement, segmentation, and understanding are also mentioned.
Digital image processing involves representing images as arrays of pixels and then processing those pixels to improve or analyze the image. It has applications in fields like medicine, mapping, law enforcement, and human-computer interfaces. The key stages of digital image processing include image acquisition, enhancement, restoration, morphological processing, segmentation, object recognition, representation and description, compression, and color image processing.
Digital images are represented as arrays of numbers called pixels. Each pixel value corresponds to attributes like intensity, color, or height at that location. Digital image processing involves techniques to enhance, analyze, and extract information from digital images for tasks like interpretation, transmission, and machine perception. It has evolved from early applications processing images from space missions and medical scans to now being used widely across fields such as entertainment, surveillance, and industrial inspection. Key stages in digital image processing typically involve image acquisition, enhancement, analysis through techniques like segmentation and recognition, and output of processed results.
Digital image processing involves techniques to improve and analyze digital images. It focuses on tasks like enhancing images for human interpretation, processing images for machine applications, and processing image data for storage and transmission. Key stages in digital image processing include image acquisition, enhancement, restoration, segmentation, and representation. Digital image processing has a long history and is now widely used in applications like medical imaging, satellite imagery analysis, industrial inspection, and law enforcement.
This document discusses digital image processing. It defines a digital image and digital image processing. The history of digital image processing is covered from the 1920s to today. Examples of applications are given, including image enhancement, medical imaging, industrial inspection, and more. The key stages of digital image processing are outlined, such as image acquisition, enhancement, restoration, segmentation, and others.
This document provides an introduction to a course on digital image processing. It discusses what a digital image is, defines digital image processing, and outlines the history and key applications of the field. The lecture will cover the definition of a digital image, the tasks of digital image processing, the history and evolution of the field from the 1920s to today, examples of applications in areas like medicine, satellite imagery, industrial inspection, and human-computer interfaces, and the main stages of digital image processing work including image acquisition, enhancement, restoration, and recognition.
Lecture 1 for Digital Image Processing (2nd Edition)Moe Moe Myint
-What is Digital Image Processing?
-The Origins of Digital Image Processing
-Examples of Fields that Use Digital Image Processing
-Fundamentals Steps in Digital Image Processing
-Components of an Image Processing System
The document provides a history of digital image processing from the early 1920s to present day. It discusses some of the earliest applications including transmitting newspaper images via submarine cable. Major developments occurred in the 1960s with improved computing enabling enhanced images from space missions. Digital image processing began being used for medical applications in the 1970s. The field has since expanded significantly with uses in areas like astronomy, art, medicine, law enforcement, and more. The document also defines digital images and digital image processing, and outlines some key stages in processing including acquisition, restoration, segmentation, and representation.
The document is an introduction to a course on digital image processing. It begins with definitions of digital images and digital image processing. It then provides a brief history of digital image processing, highlighting early applications in newspapers and space exploration. It also gives examples of current applications in areas like medicine, mapping, industrial inspection, and human-computer interfaces. Finally, it outlines some key stages in digital image processing pipelines like image acquisition, enhancement, restoration, segmentation, and compression.
This document provides an introduction to digital image processing. It defines a digital image as a finite set of pixels representing attributes like color or brightness. Digital image processing involves improving images for human interpretation or machine perception. The history of digital image processing is traced from early applications in newspapers to modern uses in medicine, satellites, and law enforcement. Key stages of digital image processing include acquisition, enhancement, restoration, segmentation, and compression.
This document provides an overview of digital image processing. It defines what an image is, noting that an image is a spatial representation of a scene represented as an array of pixels. Digital image processing refers to processing digital images on a computer. The key steps in digital image processing are image acquisition, enhancement, restoration, compression, morphological processing, segmentation, representation, and recognition. Digital image processing has many applications including medical imaging, traffic monitoring, biometrics, and computer vision.
This document provides an overview of computer imaging, which can be separated into digital image processing and computer vision. Digital image processing involves examining image data to solve problems and typically outputs images for human consumption, covering topics like image restoration, enhancement, and compression. Computer vision is intended to analyze images for computer use, outputting attributes rather than images, and covers topics like segmentation, recognition, and 3D reconstruction. The document outlines several applications of computer imaging in fields like medicine, security, and robotics, and discusses the current state of the art in areas like object recognition, medical imaging, and vision-based human-computer interaction.
Digital Image Processing_ ch1 introduction-2003Malik obeisat
The document provides an introduction to digital image processing. It defines a digital image as a finite set of digital values representing a two-dimensional image. Digital image processing focuses on improving images for human interpretation and processing images for machine perception. The document outlines the history of digital image processing and provides examples of its use in applications such as image enhancement, medical imaging, satellite imagery, and industrial inspection. It also describes common stages in digital image processing like image acquisition, enhancement, restoration, segmentation, and compression.
This document provides an introduction to digital image processing. It defines a digital image as a finite set of digital values representing a 2D image. Digital image processing focuses on improving images for human interpretation and processing images for machine perception. The document traces the history of digital image processing from the 1920s to its widespread use today. It provides examples of applications in fields like enhancement, medicine, mapping, inspection, law enforcement and human-computer interfaces. Finally, it outlines the key stages of digital image processing systems including acquisition, restoration, processing, analysis and compression.
Digital image processing has evolved significantly since the early 20th century. Some key developments include the first use of digital images in newspapers in the 1920s, improvements to space imagery in the 1960s that aided NASA missions, and the growth of medical applications like CAT scans in the 1970s. Today, digital image processing is used widely across many domains like enhancement, artistic effects, medicine, mapping, industrial inspection, security, and human-computer interfaces. It involves fundamental steps such as acquisition, enhancement, restoration, segmentation, and compression.
Digital image processing involves manipulating digital images using computer algorithms and software. It has two major applications: improving images for human interpretation and processing images for storage, transmission and machine perception. Some key aspects of digital image processing include image acquisition, enhancement, restoration, compression and color processing. It has a variety of uses in fields like medicine, law enforcement, and space applications. The American Jet Propulsion Laboratory was an early successful adopter in the 1960s for processing lunar photos sent by the Ranger 7 space probe.
The document discusses digital image processing and provides an overview of key concepts. It defines digital and analog images and explains how digital images are represented by pixels. It outlines fundamental steps in digital image processing like image acquisition, enhancement, restoration, morphological processing, segmentation, representation, compression and object recognition. It also discusses applications in areas like remote sensing, medical imaging, film and video effects.
This document provides an overview of digital image processing. It defines a digital image as a finite set of pixels arranged in rows and columns, where each pixel represents attributes like gray level or color. Digital image processing techniques can be used to improve image quality, extract useful information, and enable computer vision. Examples of applications discussed include enhancing medical images, processing satellite imagery for GIS, industrial inspection, and enabling more natural human-computer interfaces.
This document provides an overview of digital image processing and human vision. It discusses the key stages of digital image processing including image acquisition, enhancement, restoration, morphological processing, segmentation, representation and description, object recognition, and compression. It also covers the anatomy of the human eye, photoreceptors, color perception, image formation in the eye, brightness adaptation, and the Weber ratio relating the just noticeable difference in light intensity to background intensity. The document uses images and diagrams from the textbook "Digital Image Processing" to illustrate concepts in digital images and the human visual system.
The document discusses a computer vision workshop that covered topics including what a digital image is, what digital image processing is, examples of digital image processing, and key stages in digital image processing. It defines a digital image as a finite set of pixels representing properties like gray levels or color. Digital image processing focuses on improving images for interpretation and processing images for storage, transmission and machine perception. Examples covered include image enhancement, medical imaging, geographic information systems, law enforcement, and object segmentation. Key stages discussed include image acquisition, restoration, enhancement, representation and description, segmentation, and compression.
Presentation on Digital Image ProcessingSalim Hosen
Digital image processing is the use of a digital computer to process digital images through an algorithm. As a subcategory or field of digital signal processing, digital image processing has many advantages over analog image processing.
Computer vision and image processing are closely related fields that use AI techniques to extract information from visual inputs.
Image processing involves transforming images into digital form and performing operations to extract useful information. It includes steps like image acquisition, enhancement, restoration, representation, and recognition. Common applications of image processing include improving medical and satellite images.
Computer vision enables computers to interpret and understand visual inputs like images and videos. It seeks to develop techniques that help computers "see" and derive meaningful information from visual content. Key computer vision tasks include image classification, object detection, and image segmentation. Computer vision has many applications in industries like automotive, healthcare, and agriculture.
This document provides an overview of digital image processing, including:
- It defines what a digital image is and how images are digitized through sampling and quantization.
- It discusses the history of digital image processing from the 1920s to today, highlighting early applications and key advances like CAT scans.
- It gives examples of current uses like image enhancement, medical imaging, industrial inspection, and computer vision tasks like face and object recognition.
- It outlines the main stages of digital image processing pipelines including image acquisition, enhancement, restoration, segmentation, and compression.
- It provides context on the related field of computer vision and its goals of interpreting and understanding images.
Digital images are representations of images using discrete pixel values. Vision is a complex natural process, and digital image processing aims to perform tasks like improving images for human interpretation and machine perception. Key stages in processing include acquisition, enhancement, restoration, morphological operations, segmentation, and representation. Digital image processing has a long history and is now widely used in applications such as medicine, geospatial analysis, industrial inspection, and law enforcement. Examples demonstrate how techniques are applied to tasks like medical imaging, satellite imagery analysis, and printed circuit board inspection.
This document outlines the syllabus for a digital image processing course. It introduces key concepts like what a digital image is, areas of digital image processing like low-level, mid-level and high-level processes, a brief history of the field, applications in different domains, and fundamental steps involved. The course will cover topics in digital image fundamentals and processing techniques like enhancement, restoration, compression and segmentation. It will be taught using MATLAB and C# in the labs. Assessment will include homework, exams, labs and a final project.
This document provides an introduction to computer vision. It begins by defining computer vision as allowing computers to see and understand scenes. It then discusses some of the goals of computer vision, such as recognizing objects and people, improving photos, and its applications in areas like robotics, medical imaging, and human-computer interaction. The document notes that while humans can understand scenes from little information, computer vision remains difficult due to challenges like viewpoint and illumination variations, scale changes, and background clutter. It concludes by stating that it is an exciting time for computer vision and provides an overview of the project requirements for the course, including forming groups and submitting a written proposal.
Lecture 1 for Digital Image Processing (2nd Edition)Moe Moe Myint
-What is Digital Image Processing?
-The Origins of Digital Image Processing
-Examples of Fields that Use Digital Image Processing
-Fundamentals Steps in Digital Image Processing
-Components of an Image Processing System
The document provides a history of digital image processing from the early 1920s to present day. It discusses some of the earliest applications including transmitting newspaper images via submarine cable. Major developments occurred in the 1960s with improved computing enabling enhanced images from space missions. Digital image processing began being used for medical applications in the 1970s. The field has since expanded significantly with uses in areas like astronomy, art, medicine, law enforcement, and more. The document also defines digital images and digital image processing, and outlines some key stages in processing including acquisition, restoration, segmentation, and representation.
The document is an introduction to a course on digital image processing. It begins with definitions of digital images and digital image processing. It then provides a brief history of digital image processing, highlighting early applications in newspapers and space exploration. It also gives examples of current applications in areas like medicine, mapping, industrial inspection, and human-computer interfaces. Finally, it outlines some key stages in digital image processing pipelines like image acquisition, enhancement, restoration, segmentation, and compression.
This document provides an introduction to digital image processing. It defines a digital image as a finite set of pixels representing attributes like color or brightness. Digital image processing involves improving images for human interpretation or machine perception. The history of digital image processing is traced from early applications in newspapers to modern uses in medicine, satellites, and law enforcement. Key stages of digital image processing include acquisition, enhancement, restoration, segmentation, and compression.
This document provides an overview of digital image processing. It defines what an image is, noting that an image is a spatial representation of a scene represented as an array of pixels. Digital image processing refers to processing digital images on a computer. The key steps in digital image processing are image acquisition, enhancement, restoration, compression, morphological processing, segmentation, representation, and recognition. Digital image processing has many applications including medical imaging, traffic monitoring, biometrics, and computer vision.
This document provides an overview of computer imaging, which can be separated into digital image processing and computer vision. Digital image processing involves examining image data to solve problems and typically outputs images for human consumption, covering topics like image restoration, enhancement, and compression. Computer vision is intended to analyze images for computer use, outputting attributes rather than images, and covers topics like segmentation, recognition, and 3D reconstruction. The document outlines several applications of computer imaging in fields like medicine, security, and robotics, and discusses the current state of the art in areas like object recognition, medical imaging, and vision-based human-computer interaction.
Digital Image Processing_ ch1 introduction-2003Malik obeisat
The document provides an introduction to digital image processing. It defines a digital image as a finite set of digital values representing a two-dimensional image. Digital image processing focuses on improving images for human interpretation and processing images for machine perception. The document outlines the history of digital image processing and provides examples of its use in applications such as image enhancement, medical imaging, satellite imagery, and industrial inspection. It also describes common stages in digital image processing like image acquisition, enhancement, restoration, segmentation, and compression.
This document provides an introduction to digital image processing. It defines a digital image as a finite set of digital values representing a 2D image. Digital image processing focuses on improving images for human interpretation and processing images for machine perception. The document traces the history of digital image processing from the 1920s to its widespread use today. It provides examples of applications in fields like enhancement, medicine, mapping, inspection, law enforcement and human-computer interfaces. Finally, it outlines the key stages of digital image processing systems including acquisition, restoration, processing, analysis and compression.
Digital image processing has evolved significantly since the early 20th century. Some key developments include the first use of digital images in newspapers in the 1920s, improvements to space imagery in the 1960s that aided NASA missions, and the growth of medical applications like CAT scans in the 1970s. Today, digital image processing is used widely across many domains like enhancement, artistic effects, medicine, mapping, industrial inspection, security, and human-computer interfaces. It involves fundamental steps such as acquisition, enhancement, restoration, segmentation, and compression.
Digital image processing involves manipulating digital images using computer algorithms and software. It has two major applications: improving images for human interpretation and processing images for storage, transmission and machine perception. Some key aspects of digital image processing include image acquisition, enhancement, restoration, compression and color processing. It has a variety of uses in fields like medicine, law enforcement, and space applications. The American Jet Propulsion Laboratory was an early successful adopter in the 1960s for processing lunar photos sent by the Ranger 7 space probe.
The document discusses digital image processing and provides an overview of key concepts. It defines digital and analog images and explains how digital images are represented by pixels. It outlines fundamental steps in digital image processing like image acquisition, enhancement, restoration, morphological processing, segmentation, representation, compression and object recognition. It also discusses applications in areas like remote sensing, medical imaging, film and video effects.
This document provides an overview of digital image processing. It defines a digital image as a finite set of pixels arranged in rows and columns, where each pixel represents attributes like gray level or color. Digital image processing techniques can be used to improve image quality, extract useful information, and enable computer vision. Examples of applications discussed include enhancing medical images, processing satellite imagery for GIS, industrial inspection, and enabling more natural human-computer interfaces.
This document provides an overview of digital image processing and human vision. It discusses the key stages of digital image processing including image acquisition, enhancement, restoration, morphological processing, segmentation, representation and description, object recognition, and compression. It also covers the anatomy of the human eye, photoreceptors, color perception, image formation in the eye, brightness adaptation, and the Weber ratio relating the just noticeable difference in light intensity to background intensity. The document uses images and diagrams from the textbook "Digital Image Processing" to illustrate concepts in digital images and the human visual system.
The document discusses a computer vision workshop that covered topics including what a digital image is, what digital image processing is, examples of digital image processing, and key stages in digital image processing. It defines a digital image as a finite set of pixels representing properties like gray levels or color. Digital image processing focuses on improving images for interpretation and processing images for storage, transmission and machine perception. Examples covered include image enhancement, medical imaging, geographic information systems, law enforcement, and object segmentation. Key stages discussed include image acquisition, restoration, enhancement, representation and description, segmentation, and compression.
Presentation on Digital Image ProcessingSalim Hosen
Digital image processing is the use of a digital computer to process digital images through an algorithm. As a subcategory or field of digital signal processing, digital image processing has many advantages over analog image processing.
Computer vision and image processing are closely related fields that use AI techniques to extract information from visual inputs.
Image processing involves transforming images into digital form and performing operations to extract useful information. It includes steps like image acquisition, enhancement, restoration, representation, and recognition. Common applications of image processing include improving medical and satellite images.
Computer vision enables computers to interpret and understand visual inputs like images and videos. It seeks to develop techniques that help computers "see" and derive meaningful information from visual content. Key computer vision tasks include image classification, object detection, and image segmentation. Computer vision has many applications in industries like automotive, healthcare, and agriculture.
This document provides an overview of digital image processing, including:
- It defines what a digital image is and how images are digitized through sampling and quantization.
- It discusses the history of digital image processing from the 1920s to today, highlighting early applications and key advances like CAT scans.
- It gives examples of current uses like image enhancement, medical imaging, industrial inspection, and computer vision tasks like face and object recognition.
- It outlines the main stages of digital image processing pipelines including image acquisition, enhancement, restoration, segmentation, and compression.
- It provides context on the related field of computer vision and its goals of interpreting and understanding images.
Digital images are representations of images using discrete pixel values. Vision is a complex natural process, and digital image processing aims to perform tasks like improving images for human interpretation and machine perception. Key stages in processing include acquisition, enhancement, restoration, morphological operations, segmentation, and representation. Digital image processing has a long history and is now widely used in applications such as medicine, geospatial analysis, industrial inspection, and law enforcement. Examples demonstrate how techniques are applied to tasks like medical imaging, satellite imagery analysis, and printed circuit board inspection.
This document outlines the syllabus for a digital image processing course. It introduces key concepts like what a digital image is, areas of digital image processing like low-level, mid-level and high-level processes, a brief history of the field, applications in different domains, and fundamental steps involved. The course will cover topics in digital image fundamentals and processing techniques like enhancement, restoration, compression and segmentation. It will be taught using MATLAB and C# in the labs. Assessment will include homework, exams, labs and a final project.
This document provides an introduction to computer vision. It begins by defining computer vision as allowing computers to see and understand scenes. It then discusses some of the goals of computer vision, such as recognizing objects and people, improving photos, and its applications in areas like robotics, medical imaging, and human-computer interaction. The document notes that while humans can understand scenes from little information, computer vision remains difficult due to challenges like viewpoint and illumination variations, scale changes, and background clutter. It concludes by stating that it is an exciting time for computer vision and provides an overview of the project requirements for the course, including forming groups and submitting a written proposal.
This document provides an introduction to computer vision. It begins by defining computer vision as allowing computers to see and understand scenes. It then discusses some of the goals of computer vision, such as recognizing objects and people, improving photos, and its applications in areas like robotics, medical imaging, and human-computer interaction. The document notes that while humans can understand scenes from little information, computer vision remains difficult due to challenges like viewpoint and illumination variations, scale changes, and background clutter. It concludes by listing the requirements for the final project, which involves submitting a written proposal in PDF format individually or in groups of 3-4 students.
Symmetric key cryptography uses the same key for encryption and decryption. The key types include substitution ciphers that replace plaintext letters and transposition ciphers that reorder plaintext letters. Common symmetric algorithms are AES and DES. Caesar cipher is a simple substitution cipher that shifts each letter by the key amount (such as 3 places). Playfair cipher encrypts plaintext letter pairs using a 5x5 grid keyed from a word. Vigenère cipher uses a table to encrypt plaintext letters based on a repeated keyword. The one-time pad perfectly encrypts by combining a random key with the plaintext.
1. The document provides instructions for post-graduate students at HIT on submitting their thesis online.
2. It explains that students must submit both a printed and electronic copy of their thesis according to Ministry of Education and university regulations.
3. The submission process involves registering online, converting documents to PDF, uploading the file, and having it reviewed by the library.
security introduction and overview lecture1 .pptxnagwaAboElenein
This document provides an introduction to computer security concepts. It discusses how computer security aims to protect hardware, software, data, users and information from unauthorized access and use. The key concepts of confidentiality, integrity and availability (CIA) are explained. Principles of secure design are outlined, including least privilege, fail-safe defaults, defense in depth, and separation of privilege. Computer security challenges like ensuring systems are not simple to attack and require regular monitoring are also covered.
This document outlines the objectives and contributions of a research project on brain tumor segmentation. The objectives include developing Recurrent Convolutional Neural Network (RCNN) and Recurrent Residual Convolutional Neural Network (RRCNN) models based on U-Net, proposing a hybrid two-track U-Net to address class imbalance, and proposing a Multi Inception Residual Nested U-Net to enhance segmentation. The contributions discussed are a Hybrid Two Track U-Net (HTTU-Net) and a Multi Inception Residual Nested U-Net (MIResU-Net++) for brain tumor segmentation, which show improved performance over standard U-Net on benchmark datasets. Experimental results demonstrate the effectiveness of the proposed approaches.
Morphological image processing uses simple operations like erosion and dilation to remove imperfections from segmented images. Erosion shrinks objects while dilation enlarges them. More advanced techniques include opening, which erodes and then dilates, and closing, which dilates and then erodes. Boundary extraction outlines objects, while region filling takes a starting point and floods the interior of boundaries. Together these techniques provide tools for analyzing image shape and structure.
This document discusses various methods of image compression. It begins by defining image compression as reducing the amount of data required to represent a digital image. The main methods of compression are by removing redundant data from the image.
The document then discusses three main types of data redundancy that can be reduced: coding redundancy, spatial/temporal redundancy, and irrelevant information redundancy. Coding redundancy refers to inefficient coding of pixel values and can be reduced using techniques like Huffman coding or arithmetic coding. Spatial/temporal redundancy occurs from correlations between neighboring pixel values, while irrelevant information redundancy refers to data that is ignored by the human visual system.
The document provides examples and formulas for measuring compression ratio, data redundancy, and estimating the information content of
This document summarizes a research paper that proposes a new model called mmsDCNN-DenseCRF for semantic segmentation of remote sensing imagery. The mmsDCNN-DenseCRF model combines a modified multiscale deformable convolutional neural network (mmsDCNN) with a dense conditional random field (DenseCRF). The mmsDCNN generates a preliminary segmentation map capturing multiscale features. A multi-level DenseCRF then optimizes the mmsDCNN output using superpixel-level and pixel-level context to produce the final segmentation result. Experiments on standard datasets demonstrate the model achieves state-of-the-art performance for remote sensing image semantic segmentation.
This document provides an introduction to virtual reality (VR) including its history, components, applications, and research areas. It defines VR as a synthetically generated 3D environment experienced through sensory feedback from input devices. The basic components of a VR system are computing, displays, tracking, and input. Input devices discussed include mice, joysticks, data gloves, and motion capture. Output devices render visual, audio, and haptic feedback. Requirements for an effective VR system include high frame rates, low latency, data size, processing power, and networking capabilities. Current research focuses on improving realism, response times, tracking, field of view, audio, and hardware.
1) The Discrete Fourier Transform (DFT) transforms an image from the spatial domain to the frequency domain. Low frequencies are located in the corners of the DFT while high frequencies are located towards the center.
2) The convolution theorem states that convolving two signals in the spatial domain is equivalent to multiplying their Fourier transforms in the frequency domain. This allows for efficient filtering by working in the frequency domain.
3) Ideal filters like lowpass, highpass, bandpass and bandreject can be designed to operate directly on an image's Fourier transform, retaining or eliminating desired frequencies.
This document provides an agenda and overview of topics related to intensity transformations and spatial filtering for image enhancement. It discusses piecewise-linear transformation functions including contrast stretching, intensity-level slicing, and bit-plane slicing. It also covers histogram processing techniques such as histogram equalization, histogram matching, and using histogram statistics. Finally, it outlines fundamentals of spatial filtering including the mechanics of spatial filtering, spatial correlation and convolution, and generating smoothing and sharpening spatial filters.
This document provides an overview of key concepts in digital image fundamentals. It discusses the human visual system and image formation in the eye. It also covers image acquisition, sampling, quantization, and representation. Additionally, it defines concepts like spatial and intensity resolution and describes basic image processing operations and transforms. The goal is to introduce fundamental digital image processing concepts.
This document discusses various point operations that can be performed on digital images. Point operations modify pixel values without considering neighboring pixels. The key point operations covered are arithmetic operations like addition/subtraction and multiplication/division, color operations like changing lighting color and swapping/eliminating channels, multiple image operations like combining two images, and histograms which show pixel value distributions. Point operations are useful for image pre-processing tasks like contrast adjustment and color corrections.
Image Segmentation Techniques for Remote Sensing Satellite Images.pdfnagwaAboElenein
The use of satellite imagery has become an integral aspect in the planning of
multiple domains that include disaster management and analysis of natural calamity
images, snow cover mapping, smart city development, etc. Extraction of urban
information like linear features(roads), structured features( buildings, dams, manmade
structures), boundaries of water bodies) from satellite images has now
become an important area in remote sensing studies.
The whole part of a digital image is not useful for a particular purpose hence
the image needs to be segmented. Various methods for image segmentation have
been proposed but the choice of a particular method depends upon our requirement.
Fundamentals_of_Digital image processing_A practicle approach with MatLab.pdfnagwaAboElenein
This document provides an overview and table of contents for the book "Fundamentals of Digital Image Processing: A Practical Approach with Examples in Matlab" by Chris Solomon and Toby Breckon. The book covers fundamental concepts in digital image processing including image representation, formation, pixel-based operations, enhancement, Fourier transforms, image restoration, geometry, and morphological processing. It uses examples in Matlab code to illustrate key concepts and is intended to provide both theoretical background and practical examples.
This document provides an introduction to digital image processing. It defines what a digital image is as a finite set of pixels representing a two-dimensional scene. Digital image processing is described as focusing on improving images for human interpretation and processing images for machine perception. The history of digital image processing is outlined from early applications in newspapers to current uses in fields like medicine, space exploration, and more. Key stages of digital image processing are identified as image acquisition, enhancement, restoration, morphological processing, segmentation, representation, object recognition, compression, and color processing.
Communicating effectively and consistently with students can help them feel at ease during their learning experience and provide the instructor with a communication trail to track the course's progress. This workshop will take you through constructing an engaging course container to facilitate effective communication.
How to Make a Field Mandatory in Odoo 17Celine George
In Odoo, making a field required can be done through both Python code and XML views. When you set the required attribute to True in Python code, it makes the field required across all views where it's used. Conversely, when you set the required attribute in XML views, it makes the field required only in the context of that particular view.
Leveraging Generative AI to Drive Nonprofit InnovationTechSoup
In this webinar, participants learned how to utilize Generative AI to streamline operations and elevate member engagement. Amazon Web Service experts provided a customer specific use cases and dived into low/no-code tools that are quick and easy to deploy through Amazon Web Service (AWS.)
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LAND USE LAND COVER AND NDVI OF MIRZAPUR DISTRICT, UPRAHUL
This Dissertation explores the particular circumstances of Mirzapur, a region located in the
core of India. Mirzapur, with its varied terrains and abundant biodiversity, offers an optimal
environment for investigating the changes in vegetation cover dynamics. Our study utilizes
advanced technologies such as GIS (Geographic Information Systems) and Remote sensing to
analyze the transformations that have taken place over the course of a decade.
The complex relationship between human activities and the environment has been the focus
of extensive research and worry. As the global community grapples with swift urbanization,
population expansion, and economic progress, the effects on natural ecosystems are becoming
more evident. A crucial element of this impact is the alteration of vegetation cover, which plays a
significant role in maintaining the ecological equilibrium of our planet.Land serves as the foundation for all human activities and provides the necessary materials for
these activities. As the most crucial natural resource, its utilization by humans results in different
'Land uses,' which are determined by both human activities and the physical characteristics of the
land.
The utilization of land is impacted by human needs and environmental factors. In countries
like India, rapid population growth and the emphasis on extensive resource exploitation can lead
to significant land degradation, adversely affecting the region's land cover.
Therefore, human intervention has significantly influenced land use patterns over many
centuries, evolving its structure over time and space. In the present era, these changes have
accelerated due to factors such as agriculture and urbanization. Information regarding land use and
cover is essential for various planning and management tasks related to the Earth's surface,
providing crucial environmental data for scientific, resource management, policy purposes, and
diverse human activities.
Accurate understanding of land use and cover is imperative for the development planning
of any area. Consequently, a wide range of professionals, including earth system scientists, land
and water managers, and urban planners, are interested in obtaining data on land use and cover
changes, conversion trends, and other related patterns. The spatial dimensions of land use and
cover support policymakers and scientists in making well-informed decisions, as alterations in
these patterns indicate shifts in economic and social conditions. Monitoring such changes with the
help of Advanced technologies like Remote Sensing and Geographic Information Systems is
crucial for coordinated efforts across different administrative levels. Advanced technologies like
Remote Sensing and Geographic Information Systems
9
Changes in vegetation cover refer to variations in the distribution, composition, and overall
structure of plant communities across different temporal and spatial scales. These changes can
occur natural.
3. References
“Digital Image Processing”,
Rafael C. Gonzalez & Richard E. Woods,
Addison-Wesley, 2008
And
“Computer Vision and image processing :
A practical approach using cvip tools
Scott E umbaugh, Prentice hall 1998
http://www.mathworks.com/help/images/index.html
3
4. Fundamentals of Digital
Image Processing
A Practical Approach
with Examples in Matlab
Chris Solomon
2011 by John Wiley & Sons,
Ltd
4
6. Course Outlines
Ch1 : Introduction
Ch2 : Digital Image Fundamentals
Ch3 : Intensity Transformation and Spatial
Filtering
Ch4 : Filtering in The Frequency Domain
Ch5 : Image Restoration and Reconstruction
Ch8 : Image Compression
Ch9 : Morphological Image Processing
Ch10: Image Segmentation
6
7. Contents
This lecture will cover:
What is a digital image?
What is digital image processing?
History of digital image processing
State of the art examples of digital image processing
Key stages in digital image processing
7
8. Computer imaging
It’s defined as the acquisition and processing
of visual information by computer.
The ultimate receiver of information is:
Computer
Human visual system
So we have two categories:-
Computer vision
Image processing
8
9. Computer vision and image processing
In computer vision:
The processed output images
are for use by computer.
In Image processing:
The output images are for
human consumption
9
10. Computer vision
One of the computer vision fields is image analysis.
It involves the examination of image data to
facilitate solving a vision problem.
Image analysis has 2 topics:
Feature extraction: acquiring higher level image
information
Pattern classification taking these higher level of
information and identifying objects within the image
10
11. Image Processing
image in → image out
Image Analysis
image in → measurements out
Image Understanding
image in → high-level description out
11
12. What is a Digital Image?
A digital image is a representation of a two-
dimensional image as a finite set of digital values,
called picture elements or pixels
12
13. What is a Digital Image? (cont…)
It is an approximation of a real scene.
It is a representation of a two-dimensional image.
It composed of a finite number of elements called pixels or
picture elements.
Pixel values represent gray levels (intensity).
Remember digitization implies that a digital image is an approximation
of a real scene
1 pixel
13
14. What is a Digital Image? (cont…)
Common image formats include:
1 sample per point (B&W or Grayscale)
3 samples per point (Red, Green, and Blue)
For most of this course we will focus on grey-scale
images
14
16. What is Digital Image Processing?
Digital image processing focuses on two major tasks
Improvement of pictorial information for human
interpretation
Processing of image data for storage, transmission and
representation for autonomous machine perception فهم
-
إدراك
Some argument about where image processing ends
and fields such as image analysis and computer
vision start
16
17. What is DIP? (cont…)
The continuum استمراريةfrom image processing to
computer vision can be broken up into low-, mid- and
high-level processes
Low Level Process
Input: Image
Output: Image
Examples: Noise
removal, image
sharpening
Mid Level Process
Input: Image
Output: Attributes
Examples: Object
recognition,
segmentation
High Level Process
Input: Attributes
Output: Understanding
Examples: Scene
understanding,
autonomous navigation
In this course we will
stop here
17
18. History of Digital Image Processing
Early 1920s: One of the first applications of
digital imaging was in the news-
paper industry (5 levels)
The Bartlane cable picture
transmission service
Images were transferred by submarine cable
between London and New York
Pictures were coded for cable transfer and
reconstructed at the receiving end on a telegraph
printer
Early digital image
18
19. History of DIP (cont…)
Mid to late 1920s: Improvements to the
Bartlane system resulted in higher quality
images
New reproduction
processes based
on photographic
techniques
Increased number
of tones in
reproduced images
Improved
digital image Early 15 tone digital
image
19
20. History of DIP (cont…)
1960s: Improvements in computing technology and
the onset of the space race led to a surge of work in
digital image processing
1964: Computers used to
improve the quality of
images of the moon taken
by the Ranger 7 probe
Such techniques were used
in other space missions
including the Apollo landings
A picture of the moon taken
by the Ranger 7 probe
minutes before landing
20
21. History of DIP (cont…)
1970s: Digital image processing begins to be
used in medical applications
1979: Sir Godfrey N.
Hounsfield & Prof. Allan M.
Cormack share the Nobel
Prize in medicine for the
invention of tomographyسطحى رسم,
the technology behind
Computerised Axial
Tomography (CAT) scans
A computerized axial tomography scan is an x-ray procedure that
combines many x-ray images with the aid of a computer to generate cross-
sectional views and, if needed, three-dimensional images of the internal
organs and structures of the body.
Typical head slice CAT
image
21
22. History of DIP (cont…)
1980s - Today: The use of digital image processing
techniques has exploded and they are now used for
all kinds of tasks in all kinds of areas
Image enhancement/restoration
Artistic effects
Medical visualisation
Industrial inspection
Law enforcement
Human computer interfaces
3
3
24. Examples: The Hubble Telescope
Launched in 1990 the Hubble
telescope can take images of
very distant objects
However, an incorrect mirror
made many of Hubble’s
images useless
Image processing
techniques were
used to fix this
24
25. Examples: Artistic Effects
Artistic effects are used
to make images more
visually appealing, to
add special effects and
to make composite
images
25
26. Examples: Medicine
Take slice from MRI scan of canine heart, and find
boundaries between types of tissue
Image with gray levels representing tissue density
Use a suitable filter to highlight edges
Original MRI Image of a Dog Heart Edge Detection Image
26
27. Examples: GIS
Geographic Information Systems
Digital image processing techniques are used extensively
to manipulate satellite imagery
Terrainتضاريس classification
Meteorology الجوية األرصاد
27
28. Examples: GIS (cont…)
Night-Time Lights of the
World data set
Global inventory of human
settlement
Not hard to imagine the
kind of analysis that might
be done using this data
28
29. Examples: Industrial Inspection
Human operators are
expensive, slow and
unreliable
Make machines do the
job instead
Industrial vision systems
are used in all kinds of
industries
Can we trust them?
29
30. Examples: PCB Inspection
Printed Circuit Board (PCB) inspection
Machine inspection is used to determine that all components
are present and that all solder joints are acceptable
Both conventional imaging and x-ray imaging are used
30
31. Examples: Law Enforcement
Image processing techniques
are used extensively by law
enforcers
Number plate recognition for
speed cameras/automated toll
systems
Fingerprint recognition
Enhancement of CCTV images
31
32. Examples: HCI
Try to make human computer
interfaces more natural
Face recognition
Gesture ايماءةrecognition
Does anyone remember the
user interface from “Minority
Report”?
These tasks can be extremely
difficult
32
33. Key Stages in Digital Image Processing
Image
Acquisition
Image
Restoration
Morphological
Processing
Segmentation
Representation
& Description
Image
Enhancement
Object
Recognition
Problem Domain
Colour Image
Processing
Image
Compression
33
35. Image Enhancement:
taking an image and improving it visually
Image
Acquisition
Image
Restoration
Morphological
Processing
Segmentation
Representation
& Description
Image
Enhancement
Object
Recognition
Problem Domain
Colour Image
Processing
Image
Compression
35
36. Image Restoration :
taking an image with some known or estimated degradation and restoring it to its
original appearing
Image
Acquisition
Image
Restoration
Morphological
Processing
Segmentation
Representation
& Description
Image
Enhancement
Object
Recognition
Problem Domain
Colour Image
Processing
Image
Compression
36
37. Key Stages in Digital Image Processing:
Morphological Processing
extracting image component that are useful in the representation and description of region shape, such
as boundaries, skeletons, and the convex hull
Image
Acquisition
Image
Restoration
Morphological
Processing
Segmentation
Representation
& Description
Image
Enhancement
Object
Recognition
Problem Domain
Colour Image
Processing
Image
Compression
37
38. Key Stages in Digital Image Processing:
Segmentation
subdivides an image into its constituent
Image
Acquisition
Image
Restoration
Morphological
Processing
Segmentation
Representation
& Description
Image
Enhancement
Object
Recognition
Problem Domain
Colour Image
Processing
Image
Compression
38
44. Summary
We have looked at:
What is a digital image?
What is digital image processing?
History of digital image processing
State of the art examples of digital image processing
Key stages in digital image processing
Next week we start to see how it all works…
44