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    Digital Image Processing: Advancing Machine Vision for Intelligent Automation
    Digital Image Processing: Advancing Machine Vision for Intelligent Automation
    Digital Image Processing: Advancing Machine Vision for Intelligent Automation
    Ebook349 pages3 hours

    Digital Image Processing: Advancing Machine Vision for Intelligent Automation

    By Fouad Sabry

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    About this ebook

    1: Digital image processing: Introduces the fundamental principles and techniques for manipulating digital images, setting the foundation for the following chapters.


    2: JPEG: Explores the JPEG format, detailing its compression techniques, applications, and importance in digital image storage.


    3: 2D computer graphics: Examines the creation and manipulation of 2D graphics, highlighting their relevance in robotics and visual representation.


    4: Affine transformation: Discusses geometric transformations, focusing on how affine transformations are used in image alignment and mapping.


    5: Image compression: Provides an indepth look at compression methods, optimizing image data storage and transmission for efficient processing.


    6: Motion compensation: Explains motion estimation techniques that help in the tracking and compensation of moving objects in video sequences.


    7: Discrete cosine transform: Describes the application of the discrete cosine transform in image compression, focusing on its impact in JPEG compression.


    8: Video camera: Investigates the role of video cameras in capturing and processing images, crucial for robotics and motion analysis.


    9: Canny edge detector: Analyzes the Canny edge detector, a powerful tool in identifying boundaries within images, vital for object recognition.


    10: Digital image: Delves into the essence of digital images, discussing their representation and processing in digital systems.


    11: Image segmentation: Covers the methods of segmenting images into meaningful regions, essential for object detection and classification in robotics.


    12: Quantization (image processing): Explores the quantization process in image compression and its effect on image quality and data size.


    13: Scaleinvariant feature transform: Investigates a technique for detecting and describing local image features, particularly useful in object recognition and matching.


    14: Motion estimation: Describes algorithms for estimating motion in video sequences, crucial for tracking and analyzing dynamic environments.


    15: Median filter: Explains the median filter, a key method in noise reduction in images, important for improving image quality in robotic applications.


    16: Image sensor: Provides insight into image sensors, their operation, and their critical role in capturing digital images for analysis.


    17: Camera resectioning: Examines the process of calibrating cameras to map 3D space to 2D images, vital for accurate visual data in robotics.


    18: Histogram matching: Discusses the technique of matching histograms to standardize image characteristics, improving consistency in image processing.


    19: Rigid motion segmentation: Analyzes methods for segmenting rigid motion in video sequences, essential for understanding object movement.


    20: Data compression: Covers various techniques for compressing data in both image and video formats, ensuring efficient storage and transmission.


    21: Lossy compression: Discusses the concept of lossy compression, its tradeoffs, and its applications in digital image storage and transfer.

    LanguageEnglish
    PublisherOne Billion Knowledgeable
    Release dateDec 18, 2024
    Digital Image Processing: Advancing Machine Vision for Intelligent Automation

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      Digital Image Processing - Fouad Sabry

      Chapter 1: Digital image processing

      To put it simply, digital image processing is the algorithmic manipulation of digital pictures on a digital computer. Digital image processing, a subfield of digital signal processing, offers many benefits over its analog predecessor. It enables the use of more algorithms on the input data and helps prevent issues like noise and distortion from accumulating. Digital image processing may be described as multidimensional systems due to the fact that pictures are specified across two (or more) dimensions. The advancement of computing technology, the growth of mathematics (particularly the development and improvement of discrete mathematics theory), and the rising demand for digital image processing in a variety of fields, including the environment, agriculture, the military, industry, and medicine, are the primary influences on its creation and growth.

      Digital image processing, or digital picture processing as it was formerly known, was pioneered in the 1960s by a number of research institutions including Bell Laboratories, the Jet Propulsion Laboratory, MIT, the University of Maryland, and others for use in fields such as satellite imaging, wire-photo standards conversion, medical imaging, videophone, character recognition, and photograph enhancement.

      However, given the technology available at the time, the processing cost was rather expensive. In the 1970s, however, digital image processing exploded as low-cost computers and specialized gear became widely accessible. As a result, challenges like converting television standards might be tackled in real time using image processing. General-purpose computers began to replace specialist hardware for all but the most computationally expensive tasks as their processing speeds increased. With the advent of powerful computers and signal processors in the 2000s, digital image processing quickly replaced analog techniques as the gold standard in the industry.

      Metal-oxide-semiconductor (MOS) technology is the foundation of current image sensors, The discrete cosine transform (DCT) was a key innovation in digital image compression technology that was initially presented by Nasir Ahmed in 1972.

      In the 1970s, the widespread use of MOS technology radically altered the field of electronic signal processing.

      The X-ray computed tomography (often known as CT) gadget for head diagnostics was created in 1972 by British business EMI Housfield's engineer (computer tomography). The CT nucleus technique relies on a projection of a cross-section of a human head, which is then processed digitally to recreate the cross-sectional picture. Tomographic images of different sections of the human body were produced clearly when EMI created a whole-body CT device in 1975. This method of diagnosis was awarded the Nobel Prize in 1979.

      As a result of the employment of more complicated algorithms, digital image processing may provide both improved performance on basic jobs and the introduction of techniques that would be difficult to do with analog equipment.

      Digital image processing, in particular, is both a useful application and technology that relies on:

      Classification

      Feature extraction

      Signal analysis on several scales

      Pattern recognition

      Projection

      Examples of methods used by digital image processing:

      Anisotropic diffusion

      Hidden Markov models

      Image editing

      Image restoration

      Independent component analysis

      Linear filtering

      Neural networks

      Partial differential equations

      Pixelation

      Point feature matching

      Principal components analysis

      Maps that organize themselves

      Wavelets

      To soften or sharpen digital photos, just apply a suitable filter. It is possible to filter data by:

      convolution in the spatial domain using a set of well crafted kernels (filters).

      frequency (Fourier) domain masking to hide certain frequencies

      The two approaches are seen below:

      Before being translated into the Fourier space, pictures are often padded; the highpass filtered images below show the effects of various padding methods:

      When compared to repeated edge padding, zero padding causes the highpass filter to display an additional set of edges.

      High-pass filtering in the spatial domain: a MATLAB demonstration.

      img=checkerboard(20); % generate checkerboard

      % ************************** SPATIAL DOMAIN ***************************

      klaplace=[0 -1 0; -1 5 -1; 0 -1 0]; % Laplacian filter kernel

      X=conv2(img,klaplace); % convolve test img with

      % 3x3 Laplacian kernel

      figure()

      imshow(X,[]) % show Laplacian filtered

      title('Laplacian Edge Detection')

      Examples of how affine transformations may be used to perform fundamental picture manipulations including scaling, rotation, translation, reflection, and shearing are shown below:

      The affine matrix is applied to a picture by first transforming it into a matrix where each entry represents the intensity of a single pixel. Once the row and column of a pixel in the image matrix are known, the position of a pixel may be represented as a vector showing the coordinates of that pixel in the picture, [x, y]. This makes it possible to multiply the coordinate by an affine-transformation matrix, which in turn yields the output image's pixel value at the desired location.

      However, 3D homogeneous coordinates are required for transformations that include translation transformations. The new coordinate system looks like this: [x, y, 1], with the third dimension assigned to a non-zero constant (typically 1). This permits translational shifts by multiplying the coordinate vector by a 3 by 3 matrix. So, because 1 is a constant, the third dimension permits

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