DIGITAL IMAGE PROCESSING
•Download as PPTX, PDF•
0 likes•230 views
Digital image processing refers to processing digital images using a computer. A digital image is composed of pixels that each have a location and value. Digital image processing originated in the 1920s with transmitting newspaper images via cable. It advanced with space missions in the 1960s and medical uses in the 1970s. Today, digital image processing is used widely for tasks like image enhancement, artistic effects, medical visualization, industrial inspection, and law enforcement.
Report
Share
Report
Share
Recommended
Digital Image Processing: An Introduction
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.
Digital image processing
Brief introduction to Digital Image Processing
Some common terminology such as Analog Image, Digital Image, Image Enhancement, Image Restoration, Segmentation
Convolutional Neural Network for Alzheimer’s disease diagnosis with Neuroim...
reviewing convolutional neural networks for image processing especially for medical diagnosis and introducing the new CNN architect is following.
Image enhancement techniques
The document discusses various image enhancement techniques in digital image processing. It describes point operations like image negative, contrast stretching, thresholding, brightness enhancement, log transformation, and power law transformation. Contrast stretching expands the range of intensity levels and can be done by multiplying pixels with a constant, using a transfer function, or histogram equalization. Thresholding converts an image to binary by assigning pixel values above a threshold to one level and below to another. Log and power law transformations compress high intensity values and expand low values to enhance an image. Matlab code examples are provided for each technique.
Digital Image Fundamentals
The digital imaging fundamentals including image acquisition, sampling and quantization etc are presented here.
Edge detection
The document discusses edge detection methods including gradient based approaches like Sobel and zero crossing based techniques like Laplacian of Gaussian. It proposes a new algorithm that applies fuzzy logic to the results of gradient and zero crossing edge detection on an image to more accurately identify edges. The algorithm calculates gradient and zero crossings, applies fuzzy rules to classify pixels, and thresholds to determine final edge pixels.
point operations in image processing
1. Point operations modify pixel values without changing the image size or structure. Each new pixel value depends only on the original value at that point.
2. Histogram equalization finds a point operation to make the modified image histogram approximate a uniform distribution. This can only shift and merge histogram entries, not split them.
3. Other point operations discussed include increasing brightness by adding a constant value to each pixel, inverting pixels by subtracting from 255, thresholding to make pixels above a level white and below black, and alpha blending images with transparency control.
4. Automatic contrast adjustment and increasing contrast by multiplying pixel values by 1.5 are also point operations described.
Fundamentals steps in Digital Image processing
This document provides an overview of key concepts in digital image processing, including:
1. It discusses fundamental steps like image acquisition, enhancement, color image processing, and wavelets and multiresolution processing.
2. Image enhancement techniques process images to make them more suitable for specific applications.
3. Color image processing has increased in importance due to more digital images on the internet. Wavelets allow images to be represented at various resolution levels.
Recommended
Digital Image Processing: An Introduction
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.
Digital image processing
Brief introduction to Digital Image Processing
Some common terminology such as Analog Image, Digital Image, Image Enhancement, Image Restoration, Segmentation
Convolutional Neural Network for Alzheimer’s disease diagnosis with Neuroim...
reviewing convolutional neural networks for image processing especially for medical diagnosis and introducing the new CNN architect is following.
Image enhancement techniques
The document discusses various image enhancement techniques in digital image processing. It describes point operations like image negative, contrast stretching, thresholding, brightness enhancement, log transformation, and power law transformation. Contrast stretching expands the range of intensity levels and can be done by multiplying pixels with a constant, using a transfer function, or histogram equalization. Thresholding converts an image to binary by assigning pixel values above a threshold to one level and below to another. Log and power law transformations compress high intensity values and expand low values to enhance an image. Matlab code examples are provided for each technique.
Digital Image Fundamentals
The digital imaging fundamentals including image acquisition, sampling and quantization etc are presented here.
Edge detection
The document discusses edge detection methods including gradient based approaches like Sobel and zero crossing based techniques like Laplacian of Gaussian. It proposes a new algorithm that applies fuzzy logic to the results of gradient and zero crossing edge detection on an image to more accurately identify edges. The algorithm calculates gradient and zero crossings, applies fuzzy rules to classify pixels, and thresholds to determine final edge pixels.
point operations in image processing
1. Point operations modify pixel values without changing the image size or structure. Each new pixel value depends only on the original value at that point.
2. Histogram equalization finds a point operation to make the modified image histogram approximate a uniform distribution. This can only shift and merge histogram entries, not split them.
3. Other point operations discussed include increasing brightness by adding a constant value to each pixel, inverting pixels by subtracting from 255, thresholding to make pixels above a level white and below black, and alpha blending images with transparency control.
4. Automatic contrast adjustment and increasing contrast by multiplying pixel values by 1.5 are also point operations described.
Fundamentals steps in Digital Image processing
This document provides an overview of key concepts in digital image processing, including:
1. It discusses fundamental steps like image acquisition, enhancement, color image processing, and wavelets and multiresolution processing.
2. Image enhancement techniques process images to make them more suitable for specific applications.
3. Color image processing has increased in importance due to more digital images on the internet. Wavelets allow images to be represented at various resolution levels.
Matlab Image Enhancement Techniques
The document discusses various image enhancement techniques in Matlab, including filtering, predefined filters, image enhancement tools, image restoration, dilation/erosion functions, and dithering. Filtering can be used to modify images through operations like smoothing, sharpening, and edge enhancement. Predefined filters like 'gaussian' and 'laplacian' can be applied to images with functions like fspecial and imfilter. Additional tools for operations such as histogram equalization, noise addition, and filtering are also covered.
03 digital image fundamentals DIP
The document discusses key concepts in digital image fundamentals including:
1. The electromagnetic spectrum and how light attributes like intensity and luminance are measured.
2. How digital images are acquired through image sensing and sampling/quantization.
3. Methods for representing digital images through matrices and binary values, and how resolution affects gray-level detail.
4. Digital zooming techniques like nearest neighbor, bilinear, and bicubic interpolation that control blurring and edge effects.
5. Concepts like pixel adjacency, connectivity, and distance measures between pixels.
Image feature extraction
The document discusses image representation and feature extraction techniques. It describes how representation makes image information more accessible for computer interpretation using either boundaries or pixel regions. Feature extraction quantifies these representations by extracting descriptors like geometric properties, statistical moments, and textures. Desirable properties for descriptors include being invariant to transformations, compact, robust to noise, and having low complexity. Various boundary and regional descriptors are defined, such as chain codes, shape numbers, and moments.
SPATIAL FILTERING IN IMAGE PROCESSING
It describes smoothing and sharpening techniques of spatial filtering with linear and non linear functions WIT00 H
Digital Image Processing
This document provides an overview of digital image processing. It discusses what digital images are composed of and how they are processed using computers. The key steps in digital image processing are described as image acquisition, enhancement, restoration, representation and description, and recognition. A variety of techniques can be used at each step like filtering, segmentation, morphological operations, and compression. The document also outlines common sources of digital images, such as from the electromagnetic spectrum, and applications like medical imaging, astronomy, security screening, and human-computer interfaces.
Lec8: Medical Image Segmentation (II) (Region Growing/Merging)
. Region Growing algorithm
• Homogeneity Criteria
• Split/Merge algorithm
• Examples from CT, MRI, PET
• Limitations
Image Filtering, Enhancement, Noise Reduction, and Signal Processing • MedicalImageRegistration • MedicalImageSegmentation • MedicalImageVisualization • Machine Learning in Medical Imaging • Shape Modeling/Analysis of Medical Images Deep Learning in Radiology
Digital Image Processing_ ch1 introduction-2003
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.
Image Restoration
The document discusses image restoration techniques. It describes how images can become degraded through phenomena like motion, improper camera focusing, and noise. The goal of image restoration is to recover the original high quality image from its degraded version using knowledge about the degradation process and types of noise. Common noise models include Gaussian, Rayleigh, Erlang, exponential, and impulse noise. Filtering techniques like mean, order statistics, and adaptive filters can be used for restoration by smoothing the image while preserving edges. The adaptive filters change based on local image statistics to better reduce noise with less blurring than regular filters.
Lec3: Pre-Processing Medical Images
2017 Spring, UCF Medical Image Computing
CAVA: Computer Aided Visualization and Analysis
• CAD: Computer Aided Diagnosis
• Definitions and Terminologies
• Coordinate Systems
• Pre-Processing Images – Volume of Interest
– RegionofInterest
– IntensityofInterest – ImageEnhancement
• Filtering
• Smoothing
• Introduction to Medical Image Computing and Toolkits • Image Filtering, Enhancement, Noise Reduction, and Signal Processing • MedicalImageRegistration • MedicalImageSegmentation • MedicalImageVisualization • Machine Learning in Medical Imaging • Shape Modeling/Analysis of Medical Images Deep Learning in Radiology
Digital image processing using matlab
introduction to image processing using Matlab ,point processing,filters,Fourier transform,neighborhood processing
3D Image visualization
This document discusses techniques for 3D image visualization. It begins with an introduction and covers topics like rendering techniques, MATLAB visualization, volume rendering, isocontouring, hole detection, and applications of stereoscopic visualization. The document outlines various methods for 3D output like projection and OpenGL libraries. It discusses advantages like hardware support for 3D graphics and disadvantages such as objects being drawn as 2D. The conclusion states that while techniques exist, more research is still needed for innovative 3D visualization of diverse data types.
Image processing fundamentals
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.
Frequency Domain Image Enhancement Techniques
The document discusses various techniques for enhancing digital images, including spatial domain and frequency domain methods. It describes how frequency domain techniques work by applying filters to the Fourier transform of an image, such as low-pass filters to smooth an image or high-pass filters to sharpen it. Specific filters discussed include ideal, Butterworth, and Gaussian filters. The document provides examples of applying low-pass and high-pass filters to images in the frequency domain.
Digital image processing
In computer science, digital image processing is the use of computer algorithms to perform image processing on digital images. As a subcategory or field of digital signal processing, digital image processing has many advantages over analog image processing.It allows a much wider range of algorithms to be applied to the input data and can avoid problems such as the build-up of noise and signal ...
digital image processing pdf
digital image processing books
digital image processing textbook pdf
digital image processing textbook
digital image processing pdf book
digital image processing gonzalez pdf
digital image processing 4th pdf
digital image processing 3rd pdf
digital image processing slides
history of image processing
digital image processing third edition
digital image processing pdf
digital image processing gonzalez ppt
digital image processing 3rd edition pdf
digital image processing third edition pdf
image processing basics
Image segmentation
Unit 3 discusses image segmentation techniques. Similarity based techniques group similar image components, like pixels or frames, for compact representation. Common applications include medical imaging, satellite images, and surveillance. Methods include thresholding and k-means clustering. Segmentation of grayscale images is based on discontinuities in pixel values, detecting edges, or similarities using thresholding, region growing, and splitting/merging. Region growing starts with seed pixels and groups neighboring pixels with similar properties. Region splitting starts with the full image and divides non-homogeneous regions, while region merging combines small similar regions.
Image noise reduction
Method to remove Noise from image,What is image noise,Image processing,Noise filtering Technique,matlab code
Spatial Filters (Digital Image Processing)
Image Enhancement: Introduction to Spatial Filters, Low Pass Filter and High Pass Filters. Here Discussed Image Smoothing and Image Sharping, Gaussian Filters
Introduction to Image Compression
This slide gives you the basic understanding of digital image compression.
Please Note: This is a class teaching PPT, more and detail topics were covered in the classroom.
Chapter10 image segmentation
The document discusses various techniques for image segmentation including discontinuity-based approaches, similarity-based approaches, thresholding methods, region-based segmentation using region growing and region splitting/merging. Key techniques covered include edge detection using gradient operators, the Hough transform for edge linking, optimal thresholding, and split-and-merge segmentation using quadtrees.
Color fundamentals and color models - Digital Image Processing
This presentation is based on Color fundamentals and Color models.
~ Introduction to Colors
~ Color in Image Processing
~ Color Fundamentals
~ Color Models
~ RGB Model
~ CMY Model
~ CMYK Model
~ HSI Model
~ HSI and RGB
~ RGB To HSI
~ HSI To RGB
Lecture 1 for Digital Image Processing (2nd Edition)
-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
1. digital image processing
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.
More Related Content
What's hot
Matlab Image Enhancement Techniques
The document discusses various image enhancement techniques in Matlab, including filtering, predefined filters, image enhancement tools, image restoration, dilation/erosion functions, and dithering. Filtering can be used to modify images through operations like smoothing, sharpening, and edge enhancement. Predefined filters like 'gaussian' and 'laplacian' can be applied to images with functions like fspecial and imfilter. Additional tools for operations such as histogram equalization, noise addition, and filtering are also covered.
03 digital image fundamentals DIP
The document discusses key concepts in digital image fundamentals including:
1. The electromagnetic spectrum and how light attributes like intensity and luminance are measured.
2. How digital images are acquired through image sensing and sampling/quantization.
3. Methods for representing digital images through matrices and binary values, and how resolution affects gray-level detail.
4. Digital zooming techniques like nearest neighbor, bilinear, and bicubic interpolation that control blurring and edge effects.
5. Concepts like pixel adjacency, connectivity, and distance measures between pixels.
Image feature extraction
The document discusses image representation and feature extraction techniques. It describes how representation makes image information more accessible for computer interpretation using either boundaries or pixel regions. Feature extraction quantifies these representations by extracting descriptors like geometric properties, statistical moments, and textures. Desirable properties for descriptors include being invariant to transformations, compact, robust to noise, and having low complexity. Various boundary and regional descriptors are defined, such as chain codes, shape numbers, and moments.
SPATIAL FILTERING IN IMAGE PROCESSING
It describes smoothing and sharpening techniques of spatial filtering with linear and non linear functions WIT00 H
Digital Image Processing
This document provides an overview of digital image processing. It discusses what digital images are composed of and how they are processed using computers. The key steps in digital image processing are described as image acquisition, enhancement, restoration, representation and description, and recognition. A variety of techniques can be used at each step like filtering, segmentation, morphological operations, and compression. The document also outlines common sources of digital images, such as from the electromagnetic spectrum, and applications like medical imaging, astronomy, security screening, and human-computer interfaces.
Lec8: Medical Image Segmentation (II) (Region Growing/Merging)
. Region Growing algorithm
• Homogeneity Criteria
• Split/Merge algorithm
• Examples from CT, MRI, PET
• Limitations
Image Filtering, Enhancement, Noise Reduction, and Signal Processing • MedicalImageRegistration • MedicalImageSegmentation • MedicalImageVisualization • Machine Learning in Medical Imaging • Shape Modeling/Analysis of Medical Images Deep Learning in Radiology
Digital Image Processing_ ch1 introduction-2003
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.
Image Restoration
The document discusses image restoration techniques. It describes how images can become degraded through phenomena like motion, improper camera focusing, and noise. The goal of image restoration is to recover the original high quality image from its degraded version using knowledge about the degradation process and types of noise. Common noise models include Gaussian, Rayleigh, Erlang, exponential, and impulse noise. Filtering techniques like mean, order statistics, and adaptive filters can be used for restoration by smoothing the image while preserving edges. The adaptive filters change based on local image statistics to better reduce noise with less blurring than regular filters.
Lec3: Pre-Processing Medical Images
2017 Spring, UCF Medical Image Computing
CAVA: Computer Aided Visualization and Analysis
• CAD: Computer Aided Diagnosis
• Definitions and Terminologies
• Coordinate Systems
• Pre-Processing Images – Volume of Interest
– RegionofInterest
– IntensityofInterest – ImageEnhancement
• Filtering
• Smoothing
• Introduction to Medical Image Computing and Toolkits • Image Filtering, Enhancement, Noise Reduction, and Signal Processing • MedicalImageRegistration • MedicalImageSegmentation • MedicalImageVisualization • Machine Learning in Medical Imaging • Shape Modeling/Analysis of Medical Images Deep Learning in Radiology
Digital image processing using matlab
introduction to image processing using Matlab ,point processing,filters,Fourier transform,neighborhood processing
3D Image visualization
This document discusses techniques for 3D image visualization. It begins with an introduction and covers topics like rendering techniques, MATLAB visualization, volume rendering, isocontouring, hole detection, and applications of stereoscopic visualization. The document outlines various methods for 3D output like projection and OpenGL libraries. It discusses advantages like hardware support for 3D graphics and disadvantages such as objects being drawn as 2D. The conclusion states that while techniques exist, more research is still needed for innovative 3D visualization of diverse data types.
Image processing fundamentals
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.
Frequency Domain Image Enhancement Techniques
The document discusses various techniques for enhancing digital images, including spatial domain and frequency domain methods. It describes how frequency domain techniques work by applying filters to the Fourier transform of an image, such as low-pass filters to smooth an image or high-pass filters to sharpen it. Specific filters discussed include ideal, Butterworth, and Gaussian filters. The document provides examples of applying low-pass and high-pass filters to images in the frequency domain.
Digital image processing
In computer science, digital image processing is the use of computer algorithms to perform image processing on digital images. As a subcategory or field of digital signal processing, digital image processing has many advantages over analog image processing.It allows a much wider range of algorithms to be applied to the input data and can avoid problems such as the build-up of noise and signal ...
digital image processing pdf
digital image processing books
digital image processing textbook pdf
digital image processing textbook
digital image processing pdf book
digital image processing gonzalez pdf
digital image processing 4th pdf
digital image processing 3rd pdf
digital image processing slides
history of image processing
digital image processing third edition
digital image processing pdf
digital image processing gonzalez ppt
digital image processing 3rd edition pdf
digital image processing third edition pdf
image processing basics
Image segmentation
Unit 3 discusses image segmentation techniques. Similarity based techniques group similar image components, like pixels or frames, for compact representation. Common applications include medical imaging, satellite images, and surveillance. Methods include thresholding and k-means clustering. Segmentation of grayscale images is based on discontinuities in pixel values, detecting edges, or similarities using thresholding, region growing, and splitting/merging. Region growing starts with seed pixels and groups neighboring pixels with similar properties. Region splitting starts with the full image and divides non-homogeneous regions, while region merging combines small similar regions.
Image noise reduction
Method to remove Noise from image,What is image noise,Image processing,Noise filtering Technique,matlab code
Spatial Filters (Digital Image Processing)
Image Enhancement: Introduction to Spatial Filters, Low Pass Filter and High Pass Filters. Here Discussed Image Smoothing and Image Sharping, Gaussian Filters
Introduction to Image Compression
This slide gives you the basic understanding of digital image compression.
Please Note: This is a class teaching PPT, more and detail topics were covered in the classroom.
Chapter10 image segmentation
The document discusses various techniques for image segmentation including discontinuity-based approaches, similarity-based approaches, thresholding methods, region-based segmentation using region growing and region splitting/merging. Key techniques covered include edge detection using gradient operators, the Hough transform for edge linking, optimal thresholding, and split-and-merge segmentation using quadtrees.
Color fundamentals and color models - Digital Image Processing
This presentation is based on Color fundamentals and Color models.
~ Introduction to Colors
~ Color in Image Processing
~ Color Fundamentals
~ Color Models
~ RGB Model
~ CMY Model
~ CMYK Model
~ HSI Model
~ HSI and RGB
~ RGB To HSI
~ HSI To RGB
What's hot (20)
Lec8: Medical Image Segmentation (II) (Region Growing/Merging)
Lec8: Medical Image Segmentation (II) (Region Growing/Merging)
Color fundamentals and color models - Digital Image Processing
Color fundamentals and color models - Digital Image Processing
Similar to DIGITAL IMAGE PROCESSING
Lecture 1 for Digital Image Processing (2nd Edition)
-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
1. digital image processing
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.
1_unit-1.1_Introduction to DIP.pptx
The document provides an introduction to digital image processing. It discusses that digital image processing deals with analyzing and manipulating digital images using computer algorithms and software. Digital images are represented as a matrix of pixels, where each pixel has a numeric value representing characteristics like brightness or color. Some key applications of digital image processing mentioned include medical imaging, remote sensing, machine vision, and image enhancement.
Digital Image Processing presentation
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.
Dip unit-i-ppt academic year(2016-17)
This document outlines the syllabus for the course IT6005 - Digital Image Processing. The syllabus is divided into 5 units that cover digital image fundamentals, image enhancement, image restoration and segmentation, wavelets and image compression, and image representation and recognition. Unit 1 introduces key concepts in digital image processing such as pixels, gray levels, sampling and quantization. It also provides a brief history of the origin and development of digital image processing.
CG Lecture0.pptx
This document provides information about the Computer Graphics and Visualization course CSE304. It includes details like the textbook, attendance and assessment criteria, course outcomes, program outcomes, and unit-wise course content. The textbook is Computer Graphics with OpenGL by Donald Hearn et al. Assessment includes assignments, mid-term and end-term exams. The course aims to classify and apply various computer graphics tools, algorithms, and techniques for 2D and 3D transformations, projections, visibility, and curve modeling.
Lec_1_Introduction.pdf
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.
Lec_1_Introduction.pdf
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.
Basics of digital image processing
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.
ARKA RAJ SAHA-27332020003..pptx
This document presents a student's presentation on digital image processing. It begins with definitions of digital images and digital image processing. It then provides a history of digital image processing from the 1920s to today. Key examples of digital image processing applications are discussed, including image enhancement, medical imaging, geographic information systems, industrial inspection, and human-computer interfaces. The main stages of a digital image processing system are outlined, including image acquisition, enhancement, restoration, segmentation, and object recognition. Finally, the document summarizes the student's work on a basic color-space based face detection system.
Basics of Computer Graphics
This document provides an introduction to computer graphics. It begins by defining computer graphics as the art of drawing pictures on computer screens through programming or software. Computer graphics involves computations and manipulation of data to generate and manipulate images. The document then discusses some common applications of computer graphics, including graphical user interfaces, weather maps, cartography, satellite imaging, medical imaging, engineering drawings, art, training, entertainment, and simulation/modeling.
COMPUTER GRAPHICS AND MULTIMEDIA: The Future is here, Today.
This paper primarily dwells on graphics created using computers. Computer Graphics could generally be described as graphics created using computers and, more generally, the representation and manipulation of image data by a computer. The development of computer graphics, or simply referred to as CG, has made computers easier to interact with, and better for understanding and interpreting many types of data. Developments in computer graphics have had a profound impact on many types of media and have revolutionized the animation and video game industry. This paper highlights the History, Advancement and the Future of Computer Graphics as it plays a huge role in our day to day activities. The paper also discusses some common terms often associated with Computer Graphics and tries to explain their various meanings. The future is here, today. We just have to embrace it.
DIPsadasdasfsdfsdfdfasdfsdfsdgsdgdsfgdfgfdg
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.
Introduction to Medical Imaging
Introduction to Medical Imaging, Basics of Medical Imaging, Fundamentals of Digital Image Processing, First chapter of Digital Image Processing Book by Rafael C. Gonzalez.
Chap1
Digital Image Processing involves representing and processing digital images. A digital image is composed of pixels with finite, discrete intensity values. It can represent grayscale images with one value per pixel or color images with multiple values per pixel. Digital image processing techniques are used in applications like face recognition, license plate reading, and tracking objects. They involve operations on images like filtering, restoration, and extracting attributes. Key components of an image processing system include image acquisition, preprocessing, enhancement/analysis, and output.
Metric Oblique Imagery Technology
This paper was presented at Map World Forum 2009 and reflects the current state of metric oblique imagery compared to historic usage.
Chapter01 lecture 1
The document discusses the history and fundamentals of digital image processing. It describes how digital image processing evolved from early analog image transmission and processing techniques. Key developments included the invention of transistors, integrated circuits, microprocessors, and personal computers which enabled the digitization and computer processing of images. The document outlines the basic components of a digital image processing system and provides examples of different application areas like medical imaging, remote sensing, and computer vision.
Chapter01 Lecture 1.ppt
The document discusses the history and fundamentals of digital image processing. It describes how digital image processing evolved from early analog image transmission and processing techniques. Key developments included the invention of transistors, integrated circuits, microprocessors, and personal computers which enabled the digitization and computer processing of images. The document outlines the basic components of a digital image processing system and provides examples of different application areas like medical imaging, remote sensing, and computer vision.
ImageProcessing1-Introduction.ppt
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.
Image processing1 introduction
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.
Similar to DIGITAL IMAGE PROCESSING (20)
Lecture 1 for Digital Image Processing (2nd Edition)
Lecture 1 for Digital Image Processing (2nd Edition)
COMPUTER GRAPHICS AND MULTIMEDIA: The Future is here, Today.
COMPUTER GRAPHICS AND MULTIMEDIA: The Future is here, Today.
More from KarthicaMarasamy
Roles of Datascience.pptx
Data science involves using automated methods to analyze massive amounts of data to extract knowledge from them. It is an interdisciplinary field that uses techniques from statistics, computer science, and domain knowledge to extract insights from data. Data science enables the creation of data products and uses new methods for making sense of new kinds of structured and unstructured data. A data scientist is responsible for modeling complex problems, discovering insights, and identifying opportunities through statistical analysis, machine learning, and data visualization.
DATASCIENCE.pptx
This document provides an overview of data science, including its components and processes. It discusses how data science involves gathering and analyzing data to find patterns and make predictions. The key components of data science include statistics, domain expertise, data engineering, visualization, machine learning, and advanced computing. The data science process involves framing a problem, collecting and processing data, exploring the data, performing in-depth analysis, and communicating results.
Software Testing 1.pptx
This document discusses tools and processes for performance testing. It notes that there are two main types of tools - functional performance tools that test with few machines, and load testing tools that can simulate high loads without many physical resources. Popular examples of each type of tool are provided. The document also outlines the key steps in a performance testing process, including creating test plans, designing automated test cases, and setting entry and exit criteria. It emphasizes that performance testing requires significant effort, resources, and a strong process to be effective.
powerpoint 1.pdf
The document provides instructions for using various visual elements in PowerPoint, including shapes, connectors, WordArt, tables, photos, clip art, SmartArt diagrams, backgrounds, and themes. The learning objectives are to create a concept map using drawing tools; customize slides with backgrounds, tables, graphics, and organization charts; and gain intermediate skills with visual elements in PowerPoint. It includes a scenario where students create a concept map about recycling waste products.
class 3.pptx
The document describes several common software development life cycle models: Waterfall Model, V-Model, Prototyping and RAD Model, and Spiral Model.
The Waterfall Model involves sequential phases of requirements, design, implementation, and testing. The V-Model is a variant of the Waterfall Model that emphasizes verification and validation testing at each phase. Prototyping and RAD Models focus on rapid development through iterative prototyping and time-boxed iterations with customer involvement. The Spiral Model incorporates risk analysis and prototyping into an iterative waterfall process.
class 2.pptx
This document discusses software testing and quality assurance. It covers topics like the software development life cycle (SDLC) phases, testing, verification, validation, quality control, and process models. Some key points include:
- Testing is done to find defects in software before customers receive it. The goal is not to prove no defects exist.
- Verification ensures the product meets specifications while validation ensures the right product is being built for customer needs.
- The ETVX (Entry Task Verification eXit) process model represents SDLC phases with entry/exit criteria and tasks to prevent defects.
- Life cycle models describe phase relationships and include waterfall, prototyping, spiral, and V-models.
Software Testing
Software testing is the process of analyzing software to identify differences between expected and actual results. It is important because software bugs can be expensive or dangerous, as demonstrated by examples where software failures caused monetary losses and human accidents. The benefits of testing include making software more cost-effective, secure, and high quality while improving customer satisfaction. The software development life cycle involves requirements gathering, planning, design, development, testing, and deployment/maintenance phases to systematically deliver a software product.
Network (Hub,switches)
Network devices such as hubs, repeaters, bridges, switches, routers, gateways, and brouters are used to establish connections between devices on a computer network. Hubs operate at the physical layer and send data to all connected devices. Repeaters regenerate weak signals between network segments. Bridges operate at the data link layer and filter packets between network segments. Switches operate at the data link and network layers, perform error checking, and improve performance over hubs. Routers operate at the network layer, connect LANs and WANs using IP addresses, and divide broadcast domains. Gateways connect networks using different protocols. Brouters combine the functions of bridges and routers to connect networks using different protocols.
Computer network layers
The document describes the seven layers of the OSI model: Physical, Data Link, Network, Transport, Session, Presentation, and Application. Each layer has a specific function, such as the Physical layer being responsible for transmission of bits across a network and the Transport layer ensuring reliable delivery of data between endpoints. The OSI model provides a standardized framework for network communication.
Presentation more c_programmingcharacter_and_string_handling_
The document discusses C string operations like strcpy, strcat, strcmp, and strlen. It provides examples of using each function, including copying one string into another with strcpy, appending strings with strcat, comparing strings alphabetically with strcmp, and getting the length of a string with strlen. It also covers basic string declarations and built-in functions for manipulating characters.
C programming
This document provides an introduction to programming in C. It discusses that C was created by Brian Kernighan and Dennis Ritchie to be a portable language for UNIX. C is a case-sensitive, structured programming language that is high-level yet allows programmers to concentrate on problems without worrying about specific hardware. C provides efficiency, flexibility, stability, and functionality through libraries. It is widely used for systems software, databases, operating systems, device drivers, and other applications. The document outlines C program structure, data types, variables, constants, and gives examples of declarations.
Network
A network consists of two or more connected computers that share resources like printers and CDs, exchange files, and allow electronic communication. The computers can be connected through cables, phone lines, radio waves, satellites, or infrared light and share common resources.
More from KarthicaMarasamy (12)
Presentation more c_programmingcharacter_and_string_handling_
Presentation more c_programmingcharacter_and_string_handling_
Recently uploaded
Hindi varnamala | hindi alphabet PPT.pdf
हिंदी वर्णमाला पीपीटी, hindi alphabet PPT presentation, hindi varnamala PPT, Hindi Varnamala pdf, हिंदी स्वर, हिंदी व्यंजन, sikhiye hindi varnmala, dr. mulla adam ali, hindi language and literature, hindi alphabet with drawing, hindi alphabet pdf, hindi varnamala for childrens, hindi language, hindi varnamala practice for kids, https://www.drmullaadamali.com
Temple of Asclepius in Thrace. Excavation results
The temple and the sanctuary around were dedicated to Asklepios Zmidrenus. This name has been known since 1875 when an inscription dedicated to him was discovered in Rome. The inscription is dated in 227 AD and was left by soldiers originating from the city of Philippopolis (modern Plovdiv).
Your Skill Boost Masterclass: Strategies for Effective Upskilling
Your Skill Boost Masterclass: Strategies for Effective UpskillingExcellence Foundation for South Sudan
Strategies for Effective Upskilling is a presentation by Chinwendu Peace in a Your Skill Boost Masterclass organisation by the Excellence Foundation for South Sudan on 08th and 09th June 2024 from 1 PM to 3 PM on each day.ANATOMY AND BIOMECHANICS OF HIP JOINT.pdf
it describes the bony anatomy including the femoral head , acetabulum, labrum . also discusses the capsule , ligaments . muscle that act on the hip joint and the range of motion are outlined. factors affecting hip joint stability and weight transmission through the joint are summarized.
Walmart Business+ and Spark Good for Nonprofits.pdf
"Learn about all the ways Walmart supports nonprofit organizations.
You will hear from Liz Willett, the Head of Nonprofits, and hear about what Walmart is doing to help nonprofits, including Walmart Business and Spark Good. Walmart Business+ is a new offer for nonprofits that offers discounts and also streamlines nonprofits order and expense tracking, saving time and money.
The webinar may also give some examples on how nonprofits can best leverage Walmart Business+.
The event will cover the following::
Walmart Business + (https://business.walmart.com/plus) is a new shopping experience for nonprofits, schools, and local business customers that connects an exclusive online shopping experience to stores. Benefits include free delivery and shipping, a 'Spend Analytics” feature, special discounts, deals and tax-exempt shopping.
Special TechSoup offer for a free 180 days membership, and up to $150 in discounts on eligible orders.
Spark Good (walmart.com/sparkgood) is a charitable platform that enables nonprofits to receive donations directly from customers and associates.
Answers about how you can do more with Walmart!"
Constructing Your Course Container for Effective Communication
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.
Pengantar Penggunaan Flutter - Dart programming language1.pptx
Pengantar Penggunaan Flutter - Dart programming language1.pptx
How to Make a Field Mandatory in Odoo 17
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.
clinical examination of hip joint (1).pdf
described clinical examination all orthopeadic conditions .
How to deliver Powerpoint Presentations.pptx
"How to make and deliver dynamic presentations by making it more interactive to captivate your audience attention"
ISO/IEC 27001, ISO/IEC 42001, and GDPR: Best Practices for Implementation and...
Denis is a dynamic and results-driven Chief Information Officer (CIO) with a distinguished career spanning information systems analysis and technical project management. With a proven track record of spearheading the design and delivery of cutting-edge Information Management solutions, he has consistently elevated business operations, streamlined reporting functions, and maximized process efficiency.
Certified as an ISO/IEC 27001: Information Security Management Systems (ISMS) Lead Implementer, Data Protection Officer, and Cyber Risks Analyst, Denis brings a heightened focus on data security, privacy, and cyber resilience to every endeavor.
His expertise extends across a diverse spectrum of reporting, database, and web development applications, underpinned by an exceptional grasp of data storage and virtualization technologies. His proficiency in application testing, database administration, and data cleansing ensures seamless execution of complex projects.
What sets Denis apart is his comprehensive understanding of Business and Systems Analysis technologies, honed through involvement in all phases of the Software Development Lifecycle (SDLC). From meticulous requirements gathering to precise analysis, innovative design, rigorous development, thorough testing, and successful implementation, he has consistently delivered exceptional results.
Throughout his career, he has taken on multifaceted roles, from leading technical project management teams to owning solutions that drive operational excellence. His conscientious and proactive approach is unwavering, whether he is working independently or collaboratively within a team. His ability to connect with colleagues on a personal level underscores his commitment to fostering a harmonious and productive workplace environment.
Date: May 29, 2024
Tags: Information Security, ISO/IEC 27001, ISO/IEC 42001, Artificial Intelligence, GDPR
-------------------------------------------------------------------------------
Find out more about ISO training and certification services
Training: ISO/IEC 27001 Information Security Management System - EN | PECB
ISO/IEC 42001 Artificial Intelligence Management System - EN | PECB
General Data Protection Regulation (GDPR) - Training Courses - EN | PECB
Webinars: https://pecb.com/webinars
Article: https://pecb.com/article
-------------------------------------------------------------------------------
For more information about PECB:
Website: https://pecb.com/
LinkedIn: https://www.linkedin.com/company/pecb/
Facebook: https://www.facebook.com/PECBInternational/
Slideshare: http://www.slideshare.net/PECBCERTIFICATION
BBR 2024 Summer Sessions Interview Training
Qualitative research interview training by Professor Katrina Pritchard and Dr Helen Williams
Chapter wise All Notes of First year Basic Civil Engineering.pptx
Chapter wise All Notes of First year Basic Civil Engineering
Syllabus
Chapter-1
Introduction to objective, scope and outcome the subject
Chapter 2
Introduction: Scope and Specialization of Civil Engineering, Role of civil Engineer in Society, Impact of infrastructural development on economy of country.
Chapter 3
Surveying: Object Principles & Types of Surveying; Site Plans, Plans & Maps; Scales & Unit of different Measurements.
Linear Measurements: Instruments used. Linear Measurement by Tape, Ranging out Survey Lines and overcoming Obstructions; Measurements on sloping ground; Tape corrections, conventional symbols. Angular Measurements: Instruments used; Introduction to Compass Surveying, Bearings and Longitude & Latitude of a Line, Introduction to total station.
Levelling: Instrument used Object of levelling, Methods of levelling in brief, and Contour maps.
Chapter 4
Buildings: Selection of site for Buildings, Layout of Building Plan, Types of buildings, Plinth area, carpet area, floor space index, Introduction to building byelaws, concept of sun light & ventilation. Components of Buildings & their functions, Basic concept of R.C.C., Introduction to types of foundation
Chapter 5
Transportation: Introduction to Transportation Engineering; Traffic and Road Safety: Types and Characteristics of Various Modes of Transportation; Various Road Traffic Signs, Causes of Accidents and Road Safety Measures.
Chapter 6
Environmental Engineering: Environmental Pollution, Environmental Acts and Regulations, Functional Concepts of Ecology, Basics of Species, Biodiversity, Ecosystem, Hydrological Cycle; Chemical Cycles: Carbon, Nitrogen & Phosphorus; Energy Flow in Ecosystems.
Water Pollution: Water Quality standards, Introduction to Treatment & Disposal of Waste Water. Reuse and Saving of Water, Rain Water Harvesting. Solid Waste Management: Classification of Solid Waste, Collection, Transportation and Disposal of Solid. Recycling of Solid Waste: Energy Recovery, Sanitary Landfill, On-Site Sanitation. Air & Noise Pollution: Primary and Secondary air pollutants, Harmful effects of Air Pollution, Control of Air Pollution. . Noise Pollution Harmful Effects of noise pollution, control of noise pollution, Global warming & Climate Change, Ozone depletion, Greenhouse effect
Text Books:
1. Palancharmy, Basic Civil Engineering, McGraw Hill publishers.
2. Satheesh Gopi, Basic Civil Engineering, Pearson Publishers.
3. Ketki Rangwala Dalal, Essentials of Civil Engineering, Charotar Publishing House.
4. BCP, Surveying volume 1
Recently uploaded (20)
NEWSPAPERS - QUESTION 1 - REVISION POWERPOINT.pptx
NEWSPAPERS - QUESTION 1 - REVISION POWERPOINT.pptx
Your Skill Boost Masterclass: Strategies for Effective Upskilling
Your Skill Boost Masterclass: Strategies for Effective Upskilling
spot a liar (Haiqa 146).pptx Technical writhing and presentation skills
spot a liar (Haiqa 146).pptx Technical writhing and presentation skills
Walmart Business+ and Spark Good for Nonprofits.pdf
Walmart Business+ and Spark Good for Nonprofits.pdf
Constructing Your Course Container for Effective Communication
Constructing Your Course Container for Effective Communication
Pengantar Penggunaan Flutter - Dart programming language1.pptx
Pengantar Penggunaan Flutter - Dart programming language1.pptx
ISO/IEC 27001, ISO/IEC 42001, and GDPR: Best Practices for Implementation and...
ISO/IEC 27001, ISO/IEC 42001, and GDPR: Best Practices for Implementation and...
Chapter wise All Notes of First year Basic Civil Engineering.pptx
Chapter wise All Notes of First year Basic Civil Engineering.pptx
DIGITAL IMAGE PROCESSING
- 1. “ONE PICTURE IS WORTH MORE THAN TEN THOUSAND WORDS” MK(06/07/2020)
- 3. INTRODUCTION WHAT IS DIGITAL IMAGE PROCESSING? An image may be defined as a two –dimensional function,f(x,y) X and Y are spatial(plane) coordinates. Amplitude of f at any pair of coordinates(x,y)is called the intensity or gray level of the image at the point. The field of digital image processing refers to processing digital images by means of a digital computer. Digital image is composed of a finite number of elements each of which has a particular location and value. MK(06/07/2020)
- 4. A digital image is a representation of a two –dimensional image as a finite set of digital values , called picture elements or pixels. MK(06/07/2020)
- 5. ORIGIN OF DIP • Early 1920s: one of the first application of digital imaging was in the newspaper industry. • The Bartlane cable picture transmission service • Image 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. MK(06/07/2020)
- 7. • Improvements to the Bartlane system resulted in higher qualityimages. • New reproduction processes based on photographic techniques. • Increased number of tones in reproduced images. MK(06/07/2020)
- 8. • 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 range 7 probe. • Such techniques were used in other space missions including the apollo landings. MK(06/07/2020)
- 9. • 1970s:Digital image processing begins to be used in medical applications. • 1979:Sirgodfrey N.Hounsfield & prof.Allan.M.Cormack share the nobel prize in medicine for the invention of tomography,the technology behind Computerised Axial Tomography(CAT) scans. MK(06/07/2020)
- 10. • Now a days :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 visualization • Industrial inspection • Law enforcement • Human computer interfaces. MK(06/07/2020)
- 11. MK(06/07/2020)
- 12. MK(06/07/2020)
- 13. ARTISTICS EFFECTS • Artistics effects are used to make images more visually appealing, to add special effects and to make composite images. MK(06/07/2020)
- 15. INDUSTRIAL INSPECTION • Human operators are expensive ,slow and unreliable. • Make machine do the job instead • Industrial vision system are used in all kinds of industries. MK(06/07/2020)
- 16. LAW ENFORCEMENT • Image processing techiques are used extensively by law enforcers. • Number plate recoginition for speed cameras/automated toll systems • Fingerprint recognition • Enhancement of CCTV images. MK(06/07/2020)
- 17. MK(06/07/2020)
- 18. MK(06/07/2020)
- 19. MK(06/07/2020)
- 20. MK(06/07/2020)
- 21. MK(06/07/2020)
- 22. MK(06/07/2020)
- 23. MK(06/07/2020)
- 24. MK(06/07/2020)
- 25. MK(06/07/2020)
- 26. MK(06/07/2020)
- 27. MK(06/07/2020)
- 28. MK(06/07/2020)
- 29. MK(06/07/2020)
- 30. MK(06/07/2020)
- 31. MK(06/07/2020)
- 32. MK(06/07/2020)
- 33. MK(06/07/2020)
- 34. COMPONENTS OF AN IMAGE PROCESSING SYSTEM MK(06/07/2020)
- 35. MK(06/07/2020)