CERTIFICATE IN COMPUTER VISION AND SECURITY SYSTEM

Description

Computer vision is an interdisciplinary scientific field that deals with how computers can gain high-level understanding from digital images or videos. From the perspective of engineering, it seeks to understand and automate tasks that the human visual system can do.

Syllabus

Beginners

  • Unit-1: Introduction
    • Components of Vision
    • Imaging Systems
    • Signal processing for computer vision
    • Pattern recognition for computer vision
    • Performance evaluation of algorithms
  • Unit-2: Imaging Optics
    • Introduction
    • Basic Concepts of Geometric Optics
    • Reflection and Refraction
    • Multimedia Refraction
    • Paraxial Optics
    • Lenses
    • Definitions: Optical Axis, Cardinal Planes, Focal Length
    • Spherical Lenses
    • Aspherical Lenses
    • Toroidal Lenses
    • Paraxial Lenses
    • Thick lenses
    • Systems of Lenses
    • Matrix Optics
    • Optical Properties of Glasses Dispersion
    • Technical Characterization of Dispersion
    • Aberrations
    • Spherical Aberrations
    • Coma
    • Astigmatism
    • Field Curvature
    • Distortions
    • Chromatic Aberrations
    • Reducing Aberrations
    • Optical Image Formation
    • Geometry of Image Formation
    • Depth-of-Field and Focus
    • Telecentric Optics
    • Wave and Fourier Optics
    • Linear Optical Systems
    • Optical Transfer Function

Intermediate

  • Unit-3: Solid-State Image Sensing
    • Introduction
    • Fundamentals of Solid-State Photosensing
    • Semiconductor photo sensors
    • Propagation of Photons in the Image Sensor
    • Generation of Photo charge Pairs
    • Photocurrent Processing
    • Photo charge integration in photodiodes CCDs
    • Programmable Offset Subtraction
    • Programmable Gain Pixels
    • Avalanche Photocurrent Multiplication
    • Nonlinear Photocurrent to Signal Voltage Conversion
    • Transportation of Photo signals
    • Charge-Coupled-Device Photocharge Transportation
    • Photodiode Photocharge Signal Transmission
    • Voltage Signal Transmission
    • Electronic Signal Detection
      • Signal-to-Noise (SNR) and Dynamic Range
    • The basic MOSFET Source Follower
    • Noise Sources in MOSFETs
    • Electronic Signal Detection
    • Architectures of Image Sensors
    • Frame-Transfer Charge-Coupled-Devices
    • Interline-Transfer Charge-Coupled-Devices
    • Field-Interline-Transfer Charged-Coupled-Devices
    • Conventional Photodiode (MOS) Arrays
    • Active Pixel Sensor Technology
    • Colour Vision and Colour Imaging
      • Human Colour Vision
      • Primary Colours
    • Colour Chips and Colour Cameras
    • Practical Limitations of Semiconductor Photosensors
    • Pixel No uniformity and Dead Pixels
    • Sensor Nonlinearity
  • Unit-4(Part-1):Representation of Multidimensional Signals
    • Introduction
    • Types of Signals
    • Continuous Signals
    • Some Types of Signals g Depending on D Parameters
    • Unified Description
    • Multichannel Signals

Professional

  • Unit-5: Fuzzy Image Processing
    • Introduction
    • Basics of Fuzzy Set Theory
    • Fuzzy Logic
    • Defuzzification
    • Fuzzy Logic versus Probability Theory
    • Images as an Array of Fuzzy Singletons.
    • Fuzzy Image Understanding
    • A New Image Definition: Images as Fuzzy Sets
    • Image Fuzzification: From Images to Memberships
    • Feature Fuzzification
    • Fuzzy Topology: Noncrisp Definitions of Topological Relationships
    • Fuzzy Image Processing Systems
    • Operations in Membership Plane
    • Fuzzification (coding of image information)
    • The Three Stages of Fuzzy Image Processing for a Modification-based Approach.
    • Defuzzification (decoding of the results)
    • Theoretical Components of Fuzzy Image Processing
    • Fuzzy geometry
    • Fuzzy Elongatedness
    • Measures of fuzziness and image information
    • Rule-based systems
    • Fuzzy/possibilistic clustering
    • Crisp Clustering
    • Fuzzy morphology
    • Fuzzy measure theory
    • Fuzzy Grammars
    • Selected application examples
    • Minimization of image fuzziness
    • Fuzzy histogram hyperbolization
    • Fuzzy rule-based approach
    • Rule-based edge detection
    • Image segmentation
    • Fuzzy Thresholding
    • Fuzzy clustering algorithms and rule-based
  • Unit-6:(Part-1)Neural Net Computing for Image Processing
    • Introduction
    • Features of Artificial Neural Networks
    • Components of Neural Models
    • Multilayer Perceptron (MLP)
    • Backpropagation-type Neural Networks
    • Convolution Neural Networks (CNN)
    • Self-organizing Neural Networks
    • Kohonen Maps
    • Self-organized Map
    • Learning Vector Quantization
  • Unit-6:(Part-2)Neural Net Computing for Image Processing
    • Radial-basis neural networks (RBNN)
    • Radial-basis Function (RBF)
    • Transformation Radial-basis Networks (TRBNN)
    • Hopfield Neural Networks
    • Modified Hopfield Network
    • Application Examples of Neural Networks
    • Region-based Classification
    • Pixel-based Classification and Image Segmentation
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