Pixel transforms,
Color transforms,
Histogram processing & equalization ,
Filtering,
Convolution,
Fourier transformation and its applications in sharpening,
Blurring and noise removal
Features Detection
Edge Detection
Corner Detection
Line and Curve Detection
Active Contours
SIFT and HOG Descriptors
Shape Context Descriptors
Morphological Operations
Segmentation
Active Contours
Split and Merge
Watershed
Region Splitting and Merging
Graph-based Segmentation
Mean shift and Model finding
Normalized Cut
The document discusses the benefits of exercise for mental health. Regular physical activity can help reduce anxiety and depression and improve mood and cognitive functioning. Exercise causes chemical changes in the brain that may help protect against mental illness and improve symptoms.
This document discusses image segmentation techniques, specifically linking edge points through local and global processing. Local processing involves linking edge-detected pixels that are similar in gradient strength and direction within a neighborhood. Global processing uses the Hough transform to link edge points into lines by mapping points in the image space to the parameter space of slope-intercept or polar coordinates. Thresholding in parameter space identifies coherent lines composed of edge points. The Hough transform allows finding lines even if there are gaps or other defects in detected edge points.
Pixel transforms,
Color transforms,
Histogram processing & equalization ,
Filtering,
Convolution,
Fourier transformation and its applications in sharpening,
Blurring and noise removal
Features Detection
Edge Detection
Corner Detection
Line and Curve Detection
Active Contours
SIFT and HOG Descriptors
Shape Context Descriptors
Morphological Operations
Segmentation
Active Contours
Split and Merge
Watershed
Region Splitting and Merging
Graph-based Segmentation
Mean shift and Model finding
Normalized Cut
The document discusses the benefits of exercise for mental health. Regular physical activity can help reduce anxiety and depression and improve mood and cognitive functioning. Exercise causes chemical changes in the brain that may help protect against mental illness and improve symptoms.
This document discusses image segmentation techniques, specifically linking edge points through local and global processing. Local processing involves linking edge-detected pixels that are similar in gradient strength and direction within a neighborhood. Global processing uses the Hough transform to link edge points into lines by mapping points in the image space to the parameter space of slope-intercept or polar coordinates. Thresholding in parameter space identifies coherent lines composed of edge points. The Hough transform allows finding lines even if there are gaps or other defects in detected edge points.
Digital image processing img smoothningVinay Gupta
The document discusses image smoothing and sharpening techniques in digital image processing. It begins by defining what a digital image is and the goals of digital image processing. Then it discusses various applications of digital image processing like image enhancement, medical visualization, and human-computer interfaces. Key techniques covered include image smoothing using spatial filters to average pixel values in a neighborhood and image sharpening using spatial filters based on spatial differentiation to highlight edges. Examples of the Hubble space telescope and facial recognition are also mentioned.
This document discusses edge detection and image segmentation techniques. It begins with an introduction to segmentation and its importance. It then discusses edge detection, including edge models like steps, ramps, and roofs. Common edge detection techniques are described, such as using derivatives and filters to detect discontinuities that indicate edges. Point, line, and edge detection are explained through the use of filters like Laplacian filters. Thresholding techniques are introduced as a way to segment images into different regions based on pixel intensity values.
Setting the lower order bit plane to zero would have the effect of reducing the number of distinct gray levels by half. This would cause the histogram to become more peaked, with more pixels concentrated in fewer bins.
This document discusses image restoration techniques for noise removal, including:
- Spatial domain filtering techniques like mean, median, and order statistics filters to remove random noise.
- Frequency domain filtering like band reject filters to remove periodic noise.
- Adaptive filtering techniques where the filter size changes depending on image characteristics within the filter region to better handle impulse noise.
The Hough transform is a feature extraction technique used in image analysis and computer vision to detect shapes within images. It works by detecting imperfect instances of objects of a certain class of shapes via a voting procedure. Specifically, the Hough transform can be used to detect lines, circles, and other shapes in an image if their parametric equations are known, and it provides robust detection even under noise and partial occlusion. It works by quantizing the parameter space that describes the shape and counting the number of votes each parametric description receives from edge points in the image.
This document provides an introduction to image segmentation. It discusses how image segmentation partitions an image into meaningful regions based on measurements like greyscale, color, texture, depth, or motion. Segmentation is often an initial step in image understanding and has applications in identifying objects, guiding robots, and video compression. The document describes thresholding and clustering as two common segmentation techniques and provides examples of segmentation based on greyscale, texture, motion, depth, and optical flow. It also discusses region-growing, edge-based, and active contour model approaches to segmentation.
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.
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
1. The document discusses the key elements of digital image processing including image acquisition, enhancement, restoration, segmentation, representation and description, recognition, and knowledge bases.
2. It also covers fundamentals of human visual perception such as the anatomy of the eye, image formation, brightness adaptation, color fundamentals, and color models like RGB and HSI.
3. The principles of video cameras are explained including the construction and working of the vidicon camera tube.
Basic Introduction about Image Restoration (Order Statistics Filters)
Median Filter
Max and Min Filter
MidPoint Filter
Alpha-trimmed Mean filter.
and Brief Introduction to Periodic Noise
Any Question contact kalyan.acharjya@gmail.com
The document discusses object recognition in computer vision. It begins with an overview of object recognition, describing it as the task of finding and identifying objects in images. It then discusses several specific applications of object recognition, including fingerprint recognition and license plate recognition. Fingerprint recognition involves extracting features called minutiae from fingerprint images, which are ridge endings and bifurcations. License plate recognition uses an ALPR system to segment character images, normalize them, and recognize the characters.
This document discusses image segmentation techniques. It begins by introducing the goal of image segmentation as clustering pixels into salient image regions. Segmentation can be used for tasks like object recognition, image compression, and image editing. The document then discusses several bottom-up image segmentation approaches, including clustering pixels in feature space using mixtures of Gaussians models or K-means, mean-shift segmentation which models feature density non-parametrically, and graph-based segmentation methods which construct similarity graphs between pixels. It provides examples and discusses assumptions and limitations of each approach. The key approaches discussed are clustering in feature space, mean-shift segmentation, and graph-based similarity methods like the local variation algorithm.
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 digital image fundamentals and operations. It defines what a digital image is, how it is represented as a matrix, and common image types like RGB, grayscale, and binary. Pixels, resolution, neighborhoods, and basic relationships between pixels are discussed. The document also covers different types of image operations including point, local, and global operations as well as examples like arithmetic, logical, and geometric transformations. Finally, it introduces concepts of linear and nonlinear operations and announces the topic of the next lecture on image enhancement in the spatial domain.
This document discusses image enhancement techniques in the spatial domain. It begins by introducing intensity transformations and spatial filtering as the two principal categories of spatial domain processing. It then describes the basics of intensity transformations, including how they directly manipulate pixel values in an image. The document focuses on different types of basic intensity transformation functions such as image negation, log transformations, power law transformations, and piecewise linear transformations. It provides examples of how these transformations can be used to enhance images. Finally, it discusses histogram processing and how the histogram of an image provides information about the distribution of pixel intensities.
Tracking is the problem of estimating the trajectory of an object as it moves around a scene. Motion tracking involves collecting data on human movement using sensors to control outputs like music or lighting based on performer actions. Motion tracking differs from motion capture in that it requires less equipment, is less expensive, and is concerned with qualities of motion rather than highly accurate data collection. Optical flow estimates the pixel-wise motion between frames in a video by calculating velocity vectors for each pixel.
This document discusses various mathematical tools used in digital image processing (DIP), including array versus matrix operations, linear versus nonlinear operations, arithmetic operations, set and logical operations, spatial operations, vector and matrix operations, and image transforms. Key points include:
- Array operations are performed on a pixel-by-pixel basis, while matrix operations consider relationships between pixels.
- Linear operators preserve scaling and addition properties, while nonlinear operators like max do not.
- Spatial operations include single-pixel, neighborhood, and geometric transformations of pixel locations and intensities.
- Images can be represented as vectors and transformed using matrix operations.
- Common transforms like Fourier use separable, symmetric kernels to decompose images into frequency domains.
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 ...
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This document discusses various intensity transformation and spatial filtering techniques for digital image enhancement. It covers single pixel operations like negative image and contrast stretching. It also discusses neighborhood operations such as averaging and median filters. Finally, it discusses geometric spatial transformations like scaling, rotation and translation. The document provides details on basic intensity transformation functions including log, power law, and piecewise linear transformations. It also covers histogram processing techniques like histogram equalization, matching and local histogram processing. Spatial filtering and its mechanics are explained.
3D holographic projection is the technology that records and reproduces objects in a real 3D image. Tremendous effects on all fields of life including business, education, science, art, and healthcare. Holographic projection is a kind of 3D technology without wearing glasses, and viewers can see the three-dimensional virtual character.
Digital image processing img smoothningVinay Gupta
The document discusses image smoothing and sharpening techniques in digital image processing. It begins by defining what a digital image is and the goals of digital image processing. Then it discusses various applications of digital image processing like image enhancement, medical visualization, and human-computer interfaces. Key techniques covered include image smoothing using spatial filters to average pixel values in a neighborhood and image sharpening using spatial filters based on spatial differentiation to highlight edges. Examples of the Hubble space telescope and facial recognition are also mentioned.
This document discusses edge detection and image segmentation techniques. It begins with an introduction to segmentation and its importance. It then discusses edge detection, including edge models like steps, ramps, and roofs. Common edge detection techniques are described, such as using derivatives and filters to detect discontinuities that indicate edges. Point, line, and edge detection are explained through the use of filters like Laplacian filters. Thresholding techniques are introduced as a way to segment images into different regions based on pixel intensity values.
Setting the lower order bit plane to zero would have the effect of reducing the number of distinct gray levels by half. This would cause the histogram to become more peaked, with more pixels concentrated in fewer bins.
This document discusses image restoration techniques for noise removal, including:
- Spatial domain filtering techniques like mean, median, and order statistics filters to remove random noise.
- Frequency domain filtering like band reject filters to remove periodic noise.
- Adaptive filtering techniques where the filter size changes depending on image characteristics within the filter region to better handle impulse noise.
The Hough transform is a feature extraction technique used in image analysis and computer vision to detect shapes within images. It works by detecting imperfect instances of objects of a certain class of shapes via a voting procedure. Specifically, the Hough transform can be used to detect lines, circles, and other shapes in an image if their parametric equations are known, and it provides robust detection even under noise and partial occlusion. It works by quantizing the parameter space that describes the shape and counting the number of votes each parametric description receives from edge points in the image.
This document provides an introduction to image segmentation. It discusses how image segmentation partitions an image into meaningful regions based on measurements like greyscale, color, texture, depth, or motion. Segmentation is often an initial step in image understanding and has applications in identifying objects, guiding robots, and video compression. The document describes thresholding and clustering as two common segmentation techniques and provides examples of segmentation based on greyscale, texture, motion, depth, and optical flow. It also discusses region-growing, edge-based, and active contour model approaches to segmentation.
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.
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
1. The document discusses the key elements of digital image processing including image acquisition, enhancement, restoration, segmentation, representation and description, recognition, and knowledge bases.
2. It also covers fundamentals of human visual perception such as the anatomy of the eye, image formation, brightness adaptation, color fundamentals, and color models like RGB and HSI.
3. The principles of video cameras are explained including the construction and working of the vidicon camera tube.
Basic Introduction about Image Restoration (Order Statistics Filters)
Median Filter
Max and Min Filter
MidPoint Filter
Alpha-trimmed Mean filter.
and Brief Introduction to Periodic Noise
Any Question contact kalyan.acharjya@gmail.com
The document discusses object recognition in computer vision. It begins with an overview of object recognition, describing it as the task of finding and identifying objects in images. It then discusses several specific applications of object recognition, including fingerprint recognition and license plate recognition. Fingerprint recognition involves extracting features called minutiae from fingerprint images, which are ridge endings and bifurcations. License plate recognition uses an ALPR system to segment character images, normalize them, and recognize the characters.
This document discusses image segmentation techniques. It begins by introducing the goal of image segmentation as clustering pixels into salient image regions. Segmentation can be used for tasks like object recognition, image compression, and image editing. The document then discusses several bottom-up image segmentation approaches, including clustering pixels in feature space using mixtures of Gaussians models or K-means, mean-shift segmentation which models feature density non-parametrically, and graph-based segmentation methods which construct similarity graphs between pixels. It provides examples and discusses assumptions and limitations of each approach. The key approaches discussed are clustering in feature space, mean-shift segmentation, and graph-based similarity methods like the local variation algorithm.
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 digital image fundamentals and operations. It defines what a digital image is, how it is represented as a matrix, and common image types like RGB, grayscale, and binary. Pixels, resolution, neighborhoods, and basic relationships between pixels are discussed. The document also covers different types of image operations including point, local, and global operations as well as examples like arithmetic, logical, and geometric transformations. Finally, it introduces concepts of linear and nonlinear operations and announces the topic of the next lecture on image enhancement in the spatial domain.
This document discusses image enhancement techniques in the spatial domain. It begins by introducing intensity transformations and spatial filtering as the two principal categories of spatial domain processing. It then describes the basics of intensity transformations, including how they directly manipulate pixel values in an image. The document focuses on different types of basic intensity transformation functions such as image negation, log transformations, power law transformations, and piecewise linear transformations. It provides examples of how these transformations can be used to enhance images. Finally, it discusses histogram processing and how the histogram of an image provides information about the distribution of pixel intensities.
Tracking is the problem of estimating the trajectory of an object as it moves around a scene. Motion tracking involves collecting data on human movement using sensors to control outputs like music or lighting based on performer actions. Motion tracking differs from motion capture in that it requires less equipment, is less expensive, and is concerned with qualities of motion rather than highly accurate data collection. Optical flow estimates the pixel-wise motion between frames in a video by calculating velocity vectors for each pixel.
This document discusses various mathematical tools used in digital image processing (DIP), including array versus matrix operations, linear versus nonlinear operations, arithmetic operations, set and logical operations, spatial operations, vector and matrix operations, and image transforms. Key points include:
- Array operations are performed on a pixel-by-pixel basis, while matrix operations consider relationships between pixels.
- Linear operators preserve scaling and addition properties, while nonlinear operators like max do not.
- Spatial operations include single-pixel, neighborhood, and geometric transformations of pixel locations and intensities.
- Images can be represented as vectors and transformed using matrix operations.
- Common transforms like Fourier use separable, symmetric kernels to decompose images into frequency domains.
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 ...
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history of image processing
digital image processing third edition
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image processing basics
This document discusses various intensity transformation and spatial filtering techniques for digital image enhancement. It covers single pixel operations like negative image and contrast stretching. It also discusses neighborhood operations such as averaging and median filters. Finally, it discusses geometric spatial transformations like scaling, rotation and translation. The document provides details on basic intensity transformation functions including log, power law, and piecewise linear transformations. It also covers histogram processing techniques like histogram equalization, matching and local histogram processing. Spatial filtering and its mechanics are explained.
3D holographic projection is the technology that records and reproduces objects in a real 3D image. Tremendous effects on all fields of life including business, education, science, art, and healthcare. Holographic projection is a kind of 3D technology without wearing glasses, and viewers can see the three-dimensional virtual character.
This document discusses image restoration techniques for images degraded by space-variant blurs. It describes running sinusoidal transforms as a method for space-variant image restoration. Running transforms involve applying a short-time orthogonal transform within a moving window, allowing approximately stationary processing. This addresses limitations of methods that assume space-invariance or require coordinate transformations. The chapter presents running discrete sinusoidal transforms as a way to perform the space-variant restoration by modifying orthogonal transform coefficients within the window to estimate pixel values.
Holography is a technique that uses the interference of light waves to record three-dimensional images. It involves splitting a laser beam into an object beam and a reference beam, with the object beam reflected from the object onto a photographic plate, where it interferes with the reference beam to create an interference pattern encoding the image. During reconstruction, an image can be retrieved by illuminating the hologram with light identical to the reference beam. Current applications of holography include security devices, medical imaging, and optical storage, while future uses may involve holographic televisions, night vision goggles, and computers utilizing holographic memory.
A comparison between scilab inbuilt module and novel method for image fusionEditor Jacotech
Image fusion is one of the important embranchments of data fusion. Its purpose is to synthesis multi-image information in one scene to one image which is more suitable to human vision and computer vision or more adapt to further image processing such as target identification.
This paper mainly compares the Scilab inbuilt module and novel method for image fusion. By using scilab as experimental platform, we approved the feasibility and validity of method. The result indicate that the fused image quality would be very effective and clear.
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.
Demonstration of Multi-image Switchable Visual Displays Using Carpetssugiuralab
This document summarizes a demonstration of a multi-image switchable visual display using carpets. The display is able to present two different images by controlling the direction of fibers in the carpet. Software allows users to input two images, which are then represented in the carpet by drawing the fibers in different directions to create light and dark areas. By changing the position of light or a viewer's viewpoint, the display is able to switch between the two input images without redrawing the carpet. This technique could turn existing carpets into reconfigurable visual displays.
Recently image morphing is becoming a forefront subject and is attracting the attention of researchers. The motivation underpinning in exploring mage morphing is that it is producing wonderful effects on photographs and in film industries. Various morphing algorithms are been devised to cater for the challenges posed by new image requirements. So far in literature, warping algorithm has been applied individually to produce pleasing effects. However, the amalgamation of several algorithms using appropriate proportions has been put aside. In this paper, analysis of the mixture of morphing techniques has been applied on images to produce caricatures where the contours are cautiously preserved. The aesthetic effects of this newly devised amalgam algorithm is desirable to produce outstanding effects on face images.
This document discusses different types of projections used in 3D viewing pipelines, including perspective and parallel projections. Perspective projections use a center of projection to project 3D points onto a 2D view plane, resulting in effects like foreshortening and vanishing points. Parallel projections project points parallel to a viewing direction, preserving scale and shape. Specific types of parallel projections discussed include orthographic, oblique, isometric, and axonometric projections.
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.
This document discusses techniques for achieving visual realism in geometric modeling. It covers topics like hidden line removal, hidden surface determination, shading models, transparency, reflection, and camera models. The goal of visual realism is to generate images that capture effects of light interacting with physical objects similarly to how we see the real world. This involves modeling objects and lighting conditions, determining visible surfaces, assigning color to pixels, and creating animated sequences. Realistic images find applications in simulation, design, entertainment, research, and control.
Getting 3D right requires providing all six primary 3D perceptual cues, including binocular fusion, convergence, accommodation, motion parallax, and ensuring consistency between cues to minimize visual discomfort. While holograms can theoretically provide all cues perfectly, practical holographic displays face challenges related to the number of pixels needed to render full parallax scenes in real-time. Researchers are working to overcome these challenges through techniques like computing holograms at the display from alternative 3D data like light fields or arrays of 2D views. Upcoming experiments will evaluate tasks that benefit from stereo vision under different display conditions.
1. control of real time traffic with the help of image processingNitish Kotak
This document describes a real-time traffic control system using image processing. The system uses a camera to capture images of a four-road intersection. The images are processed to detect vehicles and calculate the area covered to determine traffic density. Based on density, different times are assigned to the traffic lights of each road. When the time for a road elapses, its light turns green while the camera moves to the next road. This allows lights to change adaptively based on real-time traffic conditions compared to traditional fixed timing systems. The approach aims to reduce congestion by allocating more time to busier roads.
Adversarial Photo Frame: Concealing Sensitive Scene Information in a User-Acc...multimediaeval
Paper: http://ceur-ws.org/Vol-2670/MediaEval_19_paper_24.pdf
Youtube: https://www.youtube.com/watch?v=keLM9fmKJSI
Zhuoran Liu and Zhengyu Zhao, Adversarial Photo Frame: Concealing Sensitive Scene Information of Social Images in a User-Acceptable Manner. Proc. of MediaEval 2018, 27-29 October 2019, Sophia Antipolis, France.
Abstract:
Personal privacy protection has become more and more crucial in the era of big multimedia data and artificial intelligence. This paper presents our submission to pixel privacy task, where we propose to fool the deep visual classification model that is for recognition of sensitive scenes by adding adversarial frame to the image. Experimental results indicate that our method can achieve strong adversarial effects while maintaining the visual appeal and social function of the transformed images.
Presented by Zhengyu Zhao
This document discusses geometric transformations of images. It explains that geometric transformations map points from one image to another, changing the visual impression through rotations, magnifications or other alterations. Specifically, it describes how transformations require changing the image's geometry through complex algorithms. The document also notes that magnification algorithms using geometric transformations are important for applications like cameras, military equipment, and 3D computer graphics used in entertainment.
Computer vision enables machines to understand and interpret visual content like images in the same way humans can. It involves techniques from fields like artificial intelligence and machine learning to allow computers to identify and process objects, scenes, and activities in digital images and videos. Some key challenges in computer vision include processing image data, requiring large datasets to train models, and making real-time decisions from images like detecting unsafe situations. Computer vision has applications across many domains like forestry, healthcare, autonomous vehicles, and more.
This document provides an overview of digital image processing fundamentals. It discusses applications of image processing like character recognition and machine vision. It defines what an image is and describes how images are digitized spatially through sampling and in amplitude through quantization. The document outlines the fundamental steps in image processing like acquisition, preprocessing, segmentation, and representation. It also describes the basic elements of an image processing system, including acquisition devices, storage, processing units, and displays. Color models for images and video are also introduced, such as RGB, CMY, YIQ, and YUV.
Digital image processing refers to processing digital images using computers. It involves representing images digitally using matrices of pixels with discrete intensity values, and processing these digital images. Some key applications of image processing include digital photography, medical imaging, video editing, and computer vision for autonomous systems. The history of digital image processing began in the 1920s with transmitting newspaper pictures via submarine cable, and early techniques involved coding brightness levels for transmission. Modern image processing relies on digital computer techniques to process and analyze digitized images.
The document provides an introduction to digital image processing. It discusses the nature of images and how they are represented digitally. The major applications of image processing are described, including diagnosis, industrial inspection, forensics, and remote sensing. The different types of images are outlined based on attributes, color, dimensions, and data types. The fundamental steps and classes of image processing operations are introduced, including image enhancement, restoration, compression, and analysis.
Similar to CV_1 Introduction of Computer Vision and its Application (20)
Chap.3 Knowledge Representation Issues Chap.4 Inference in First Order LogicKhushali Kathiriya
Knowledge–based agents,
The Wumpus world Logic,
Propositional logic,
Propositional theorem proving
Effective propositional model checking,
Agents based on propositional logic,
First Order Logic,
Forward Chaining/ Resolution,
Backward Chaining/ Resolution,
Unification Algorithm, Resolution,
Clausal Normal Form (CNF)
The document discusses the benefits of exercise for mental health. Regular physical activity can help reduce anxiety and depression and improve mood and cognitive functioning. Exercise boosts blood flow and levels of neurotransmitters and endorphins which elevate and stabilize mood.
The document discusses the benefits of exercise for mental health. Regular physical activity can help reduce anxiety and depression and improve mood and cognitive functioning. Exercise stimulates the production of endorphins in the brain which can help alleviate feelings of stress or sadness.
The document discusses understanding in artificial intelligence. It defines understanding as the process of simulating human intelligence through machine learning and algorithms. Machine learning can be supervised, involving labeled training examples to minimize error, or unsupervised, where the system finds common characteristics in unlabeled data. Understanding is also discussed in relation to constraint satisfaction problems, where conditions must be met, like in map coloring or Sudoku puzzles. Backtracking is used to systematically search for solutions that satisfy all constraints.
This document discusses weak slot and filler structures in artificial intelligence. It describes semantic net representation, which represents knowledge as a graphical network of nodes and arcs. It provides examples of representing statements about a cat named Jerry in a semantic net. The document also discusses frame representation, which organizes knowledge into structured records called frames that contain slots and slot values. An example frame is provided for a person named Ram. Advantages and disadvantages of both semantic nets and frames are outlined.
This document provides an overview of uncertainty in artificial intelligence and probabilistic reasoning. It discusses sources of uncertainty like uncertain input, knowledge, and output. Probabilistic reasoning uses probability to represent uncertain knowledge. The document introduces basic probability notation including propositions, atomic events, unconditional probability, conditional probability, independence, and Bayes' rule. It explains how to perform inference using full joint probability distributions and marginalization. The document was prepared by Prof. Khushali B Kathiriya and provides an introduction to representing and reasoning with uncertainty in AI systems.
This document discusses symbolic reasoning under uncertainty. It introduces monotonic reasoning, where conclusions remain valid even when new information is added, and non-monotonic reasoning, where conclusions can be invalidated by new information. For non-monotonic reasoning, it provides an example where concluding a bird can fly is invalidated by learning the bird is a penguin. The document is presented by Prof. Khushali B Kathiriya and outlines introduction to monotonic reasoning, introduction to non-monotonic reasoning, and an example of non-monotonic reasoning logic.
The document discusses knowledge representation using rules in artificial intelligence. It covers procedural versus declarative knowledge, forward chaining and backward chaining. Forward chaining starts with known facts and applies rules to reach a goal, working from bottom to top. Backward chaining starts at the goal and works backwards through rules to find supporting facts, using a top-down approach. An example of selling missiles to prove someone is a criminal is used to illustrate both forward and backward chaining techniques.
This document discusses knowledge representation in artificial intelligence. It covers various techniques for knowledge representation including logical representation using propositional logic and first-order predicate logic, semantic network representation, frame representation, and production rules. It also discusses issues in knowledge representation such as representing important attributes, relationships, and granularity of knowledge. Propositional logic is introduced as the simplest form of logic where statements are represented by propositions that can be either true or false. The syntax and semantics of propositional logic are also covered.
Here are the key AI techniques discussed in the document:
- Tree searching techniques like depth-first search, breadth-first search, uniform cost search, A* search, and heuristic search methods.
- Rule-based systems that apply rules to deduce conclusions.
- Constraint satisfaction techniques that find solutions that satisfy constraints.
- Generate and test approaches that generate candidate solutions and test them against requirements.
- Description and matching techniques that describe states and match them to goals.
- Goal reduction techniques that hierarchically reduce goals to subgoals.
The document discusses these techniques as common approaches used to solve different types of AI problems. It provides examples but does not go into detailed explanations of each technique.
Comparative analysis between traditional aquaponics and reconstructed aquapon...bijceesjournal
The aquaponic system of planting is a method that does not require soil usage. It is a method that only needs water, fish, lava rocks (a substitute for soil), and plants. Aquaponic systems are sustainable and environmentally friendly. Its use not only helps to plant in small spaces but also helps reduce artificial chemical use and minimizes excess water use, as aquaponics consumes 90% less water than soil-based gardening. The study applied a descriptive and experimental design to assess and compare conventional and reconstructed aquaponic methods for reproducing tomatoes. The researchers created an observation checklist to determine the significant factors of the study. The study aims to determine the significant difference between traditional aquaponics and reconstructed aquaponics systems propagating tomatoes in terms of height, weight, girth, and number of fruits. The reconstructed aquaponics system’s higher growth yield results in a much more nourished crop than the traditional aquaponics system. It is superior in its number of fruits, height, weight, and girth measurement. Moreover, the reconstructed aquaponics system is proven to eliminate all the hindrances present in the traditional aquaponics system, which are overcrowding of fish, algae growth, pest problems, contaminated water, and dead fish.
Optimizing Gradle Builds - Gradle DPE Tour Berlin 2024Sinan KOZAK
Sinan from the Delivery Hero mobile infrastructure engineering team shares a deep dive into performance acceleration with Gradle build cache optimizations. Sinan shares their journey into solving complex build-cache problems that affect Gradle builds. By understanding the challenges and solutions found in our journey, we aim to demonstrate the possibilities for faster builds. The case study reveals how overlapping outputs and cache misconfigurations led to significant increases in build times, especially as the project scaled up with numerous modules using Paparazzi tests. The journey from diagnosing to defeating cache issues offers invaluable lessons on maintaining cache integrity without sacrificing functionality.
Using recycled concrete aggregates (RCA) for pavements is crucial to achieving sustainability. Implementing RCA for new pavement can minimize carbon footprint, conserve natural resources, reduce harmful emissions, and lower life cycle costs. Compared to natural aggregate (NA), RCA pavement has fewer comprehensive studies and sustainability assessments.
Presentation of IEEE Slovenia CIS (Computational Intelligence Society) Chapte...University of Maribor
Slides from talk presenting:
Aleš Zamuda: Presentation of IEEE Slovenia CIS (Computational Intelligence Society) Chapter and Networking.
Presentation at IcETRAN 2024 session:
"Inter-Society Networking Panel GRSS/MTT-S/CIS
Panel Session: Promoting Connection and Cooperation"
IEEE Slovenia GRSS
IEEE Serbia and Montenegro MTT-S
IEEE Slovenia CIS
11TH INTERNATIONAL CONFERENCE ON ELECTRICAL, ELECTRONIC AND COMPUTING ENGINEERING
3-6 June 2024, Niš, Serbia
Redefining brain tumor segmentation: a cutting-edge convolutional neural netw...IJECEIAES
Medical image analysis has witnessed significant advancements with deep learning techniques. In the domain of brain tumor segmentation, the ability to
precisely delineate tumor boundaries from magnetic resonance imaging (MRI)
scans holds profound implications for diagnosis. This study presents an ensemble convolutional neural network (CNN) with transfer learning, integrating
the state-of-the-art Deeplabv3+ architecture with the ResNet18 backbone. The
model is rigorously trained and evaluated, exhibiting remarkable performance
metrics, including an impressive global accuracy of 99.286%, a high-class accuracy of 82.191%, a mean intersection over union (IoU) of 79.900%, a weighted
IoU of 98.620%, and a Boundary F1 (BF) score of 83.303%. Notably, a detailed comparative analysis with existing methods showcases the superiority of
our proposed model. These findings underscore the model’s competence in precise brain tumor localization, underscoring its potential to revolutionize medical
image analysis and enhance healthcare outcomes. This research paves the way
for future exploration and optimization of advanced CNN models in medical
imaging, emphasizing addressing false positives and resource efficiency.
Literature Review Basics and Understanding Reference Management.pptxDr Ramhari Poudyal
Three-day training on academic research focuses on analytical tools at United Technical College, supported by the University Grant Commission, Nepal. 24-26 May 2024
Understanding Inductive Bias in Machine LearningSUTEJAS
This presentation explores the concept of inductive bias in machine learning. It explains how algorithms come with built-in assumptions and preferences that guide the learning process. You'll learn about the different types of inductive bias and how they can impact the performance and generalizability of machine learning models.
The presentation also covers the positive and negative aspects of inductive bias, along with strategies for mitigating potential drawbacks. We'll explore examples of how bias manifests in algorithms like neural networks and decision trees.
By understanding inductive bias, you can gain valuable insights into how machine learning models work and make informed decisions when building and deploying them.
CHINA’S GEO-ECONOMIC OUTREACH IN CENTRAL ASIAN COUNTRIES AND FUTURE PROSPECTjpsjournal1
The rivalry between prominent international actors for dominance over Central Asia's hydrocarbon
reserves and the ancient silk trade route, along with China's diplomatic endeavours in the area, has been
referred to as the "New Great Game." This research centres on the power struggle, considering
geopolitical, geostrategic, and geoeconomic variables. Topics including trade, political hegemony, oil
politics, and conventional and nontraditional security are all explored and explained by the researcher.
Using Mackinder's Heartland, Spykman Rimland, and Hegemonic Stability theories, examines China's role
in Central Asia. This study adheres to the empirical epistemological method and has taken care of
objectivity. This study analyze primary and secondary research documents critically to elaborate role of
china’s geo economic outreach in central Asian countries and its future prospect. China is thriving in trade,
pipeline politics, and winning states, according to this study, thanks to important instruments like the
Shanghai Cooperation Organisation and the Belt and Road Economic Initiative. According to this study,
China is seeing significant success in commerce, pipeline politics, and gaining influence on other
governments. This success may be attributed to the effective utilisation of key tools such as the Shanghai
Cooperation Organisation and the Belt and Road Economic Initiative.
Batteries -Introduction – Types of Batteries – discharging and charging of battery - characteristics of battery –battery rating- various tests on battery- – Primary battery: silver button cell- Secondary battery :Ni-Cd battery-modern battery: lithium ion battery-maintenance of batteries-choices of batteries for electric vehicle applications.
Fuel Cells: Introduction- importance and classification of fuel cells - description, principle, components, applications of fuel cells: H2-O2 fuel cell, alkaline fuel cell, molten carbonate fuel cell and direct methanol fuel cells.
A SYSTEMATIC RISK ASSESSMENT APPROACH FOR SECURING THE SMART IRRIGATION SYSTEMSIJNSA Journal
The smart irrigation system represents an innovative approach to optimize water usage in agricultural and landscaping practices. The integration of cutting-edge technologies, including sensors, actuators, and data analysis, empowers this system to provide accurate monitoring and control of irrigation processes by leveraging real-time environmental conditions. The main objective of a smart irrigation system is to optimize water efficiency, minimize expenses, and foster the adoption of sustainable water management methods. This paper conducts a systematic risk assessment by exploring the key components/assets and their functionalities in the smart irrigation system. The crucial role of sensors in gathering data on soil moisture, weather patterns, and plant well-being is emphasized in this system. These sensors enable intelligent decision-making in irrigation scheduling and water distribution, leading to enhanced water efficiency and sustainable water management practices. Actuators enable automated control of irrigation devices, ensuring precise and targeted water delivery to plants. Additionally, the paper addresses the potential threat and vulnerabilities associated with smart irrigation systems. It discusses limitations of the system, such as power constraints and computational capabilities, and calculates the potential security risks. The paper suggests possible risk treatment methods for effective secure system operation. In conclusion, the paper emphasizes the significant benefits of implementing smart irrigation systems, including improved water conservation, increased crop yield, and reduced environmental impact. Additionally, based on the security analysis conducted, the paper recommends the implementation of countermeasures and security approaches to address vulnerabilities and ensure the integrity and reliability of the system. By incorporating these measures, smart irrigation technology can revolutionize water management practices in agriculture, promoting sustainability, resource efficiency, and safeguarding against potential security threats.
Embedded machine learning-based road conditions and driving behavior monitoringIJECEIAES
Car accident rates have increased in recent years, resulting in losses in human lives, properties, and other financial costs. An embedded machine learning-based system is developed to address this critical issue. The system can monitor road conditions, detect driving patterns, and identify aggressive driving behaviors. The system is based on neural networks trained on a comprehensive dataset of driving events, driving styles, and road conditions. The system effectively detects potential risks and helps mitigate the frequency and impact of accidents. The primary goal is to ensure the safety of drivers and vehicles. Collecting data involved gathering information on three key road events: normal street and normal drive, speed bumps, circular yellow speed bumps, and three aggressive driving actions: sudden start, sudden stop, and sudden entry. The gathered data is processed and analyzed using a machine learning system designed for limited power and memory devices. The developed system resulted in 91.9% accuracy, 93.6% precision, and 92% recall. The achieved inference time on an Arduino Nano 33 BLE Sense with a 32-bit CPU running at 64 MHz is 34 ms and requires 2.6 kB peak RAM and 139.9 kB program flash memory, making it suitable for resource-constrained embedded systems.
4. Introduction of Computer Vision
• Computer Vision is a field of Artificial Intelligence and Computer Science that
aims at giving computers a visual understanding of the world, and is the heart of
Hayo’s powerful algorithms.
• It is one of the main components of machine understanding :
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7. Application of Computer Vision
• The good news is that computer vision is being used today in a wide variety of
real-world applications, which include:
• Optical character recognition (OCR): reading handwritten postal codes on
letters (Figure 1.4a) and automatic number plate recognition (ANPR);
• Machine inspection: rapid parts inspection for quality assurance using stereo
vision with specialized illumination to measure tolerances on aircraft wings
or auto body parts (Figure 1.4b) or looking for defects in steel castings using
X-ray vision;
• Retail: object recognition for automated checkout lanes (Figure 1.4c);
• 3D model building (photogrammetry): fully automated construction of 3D
models from aerial photographs used in systems such as Bing Maps;
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8. Application of Computer Vision (Cont.)
• Medical imaging: registering pre-operative and intra-operative imagery (Figure 1.4d) or
performing long-term studies of people’s brain morphology as they age;
• Automotive safety: detecting unexpected obstacles such as pedestrians on the street,
under conditions where active vision techniques such as radar or lidar do not work well
(Figure 1.4e; see also Miller, Campbell, Huttenlocher et al. (2008); Montemerlo, Becker,
Bhat et al. (2008); Urmson, Anhalt, Bagnell et al. (2008) for examples of fully automated
driving);
• Match move: merging computer-generated imagery (CGI) with live action footage by
tracking feature points in the source video to estimate the 3D camera motion and shape of
the environment. Such techniques are widely used in Hollywood (e.g., in movies such as
Jurassic Park) (Roble 1999; Roble and Zafar 2009); they also require the use of precise
matting to insert new elements between foreground and background elements (Chuang,
Agarwala, Curless et al. 2002).
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9. Application of Computer Vision (Cont.)
• Motion capture (mocap): using retro-reflective markers viewed from multiple
cameras or other vision-based techniques to capture actors for computer
animation;
• Surveillance: monitoring for intruders, analyzing highway traffic (Figure 1.4f),
and monitoring pools for drowning victims;
• Fingerprint recognition and biometrics: for automatic access authentication as
well as forensic applications.
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10. • Some industrial applications of computer vision:
• (a) optical character recognition (OCR)
http://yann.lecun.com/exdb/lenet/;
• (b) mechanical inspection http://www.cognitens.
com/;
• (c) retail http://www.evoretail.com/;
• (d)medical imaging http://www.clarontech.com/;
• (e) automotive safety http://www.mobileye.com/;
• (f) surveillance and traffic monitoring http:
//www.honeywellvideo.com/, courtesy of
Honeywell International Inc
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11. Application of Computer Vision (Cont.)
• Now, we focus more on broader consumer-level applications, such as fun things
you can do with your own personal photographs and video. These include:
• Stitching: turning overlapping photos into a single seamlessly stitched
panorama (Figure 1.5a)
• Exposure bracketing: merging multiple exposures taken under challenging
lighting conditions (strong sunlight and shadows) into a single perfectly exposed
image (Figure 1.5b)
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13. Application of Computer Vision (Cont.)
• Morphing: turning a picture of one of your friends into another, using a
seamless morph transition (Figure 1.5c);
• 3D modeling: converting one or more snapshots into a 3D model of the object or
person you are photographing (Figure 1.5d),
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16. Image Formation
• In modeling any image formation process, geometric primitives and
transformations are crucial to project 3-D geometric features into 2-D features.
However, apart from geometric features, image formation also depends on
discrete color and intensity values.
• It needs to know the lighting of the environment, camera optics, sensor
properties, etc. Therefore, while talking about image formation in Computer
Vision.
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(200,100,50):RGB
(122): Gray Scale
17. 1. Photometric Image Formation
• Fig. gives a simple explanation of image formation. The light from a source is
reflected on a particular surface. A part of that reflected light goes through an
image plane that reaches a sensor plane via optics.
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18. 1. Photometric Image Formation (Cont.)
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19. 1. Photometric Image Formation (Cont.)
• Some factors that affect image formation are:
• The strength and direction of the light emitted from the source.
• The material and surface geometry along with other nearby surfaces.
• Sensor Capture properties
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20. 1. Photometric Image Formation (Cont.)
• Reflection and Scattering
• Images cannot exist without light. Light sources can be a point or an area light
source. When the light hits a surface, three major reactions might occur-
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21. 1. Photometric Image Formation (Cont.)
• Color
• From a viewpoint of color, we know visible light is only a small portion of a large
electromagnetic spectrum.
• Two factors are noticed when a colored light arrives at a sensor:
1. Color of the light
2. Color of the surface
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23. 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
24. What is a Digital Image? (Cont.)
• Pixel values typically represent gray levels, colors, heights, opacities etc.
• Remember digitization implies that a digital image is an approximation of a real
scene
1 pixel
25. 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)
• 4 samples per point (Red, Green, Blue, and “Alpha”, a.k.a.
Opacity)
27. Image Representation
• After getting an image, it is important to devise ways to represent the image.
There are various ways by which an image can be represented. Let’s look at the
most common ways to represent an image.
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28. Image Representation (Cont.)
• Image as a matrix
• The simplest way to represent the
image is in the form of a matrix.
• In fig. 6, we can see that a part of the
image, i.e., the clock, has been
represented as a matrix. A similar
matrix will represent the rest of the
image too.
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29. Image Representation (Cont.)
• It is commonly seen that people use up to a byte to represent every pixel of the image.
This means that values between 0 to 255 represent the intensity for each pixel in the
image where 0 is black and 255 is white. For every color channel in the image, one such
matrix is generated.
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30. Image Representation (Cont.)
• Image as a function
• An image can also be represented as a function. An image (grayscale) can be thought of as a
function that takes in a pixel coordinate and gives the intensity at that pixel.
• It can be written as function f: ℝ² → ℝ that outputs the intensity at any input point (x,y). The
value of intensity can be between 0 to 255 or 0 to 1 if values are normalized
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32. Image Geometric/Spatial Transformation
• Image geometric that means changing the geometry of an image.
• Geometric transforms permit the elimination of geometric distortion that occurs
when an image is captured.
• A spatial transformation of an image is a geometric transformation of the image
coordinate system.
• In spatial transformation each point (x,y ) of image A is mapped to a point (u, v)
in a new coordinate system.
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33. Image Geometric/Spatial Transformation
• Why it is used?
• Some person clicking the pictures of the same place at different times of the day and
year to visualize the changes. every time he clicks the picture, it’s not necessary that
he clicks the picture at the exact same angle. So for better visualization, he can align
all the images at the same angle using geometric transformation.
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34. Why geometric transformation in required?
• Image registration is the process of transforming different sets of data into one
coordinate system.
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37. Types of Geometric Transformation
1. Translation
• Translation is the shifting of the object’s location. If you know the shift in (x, y)
direction, let it be, you can create the transformation matrix as follows:
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38. Types of Geometric Transformation
1. Translation (Cont.)
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Actual Position
Updated Position
39. Types of Geometric Transformation
2. Rotation (Cont.)
• This technique rotates an image by a specified angle and by the given axis or
point.
• The points that lie outside the boundary of an output image are ignored.
Rotation about the origin by an angle 𝜃 is given by,
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40. Types of Geometric Transformation
2. Rotation (Cont.)
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41. Types of Geometric Transformation
3. Scaling
• Scaling means resizing an image which means an image is made bigger or
smaller in x/y direction.
• We can resize an image in terms of scaling factor.
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42. Types of Geometric Transformation
3. Scaling (Cont.)
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(x,y) = (300, 400)
• 300 row and 400 col.
• 300= height 400= width
43. Types of Geometric Transformation
4. Shearing
• Shearing an image means shifting the pixel values either horizontally or
vertically.
• Basically, this shits some part of an image to one direction an other part to some
other direction. Horizontal shearing will shift the upper part to the right and
lower part to the left.
• Here you can see in gif. That upper part has shifted to the right and the lower
part to the left.
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44. Types of Geometric Transformation
4. Shearing (Cont.)
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45. Types of Geometric Transformation
5. Rigid Transformation
• Rigid = Translations + Rotations
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46. Types of Geometric Transformation
6. Similarity Transformation
• Similarity = Translations + Rotations + Scale
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47. Types of Geometric Transformation
7. Affine Transformation (IMP)
• Affine = Translations + Rotations + Scale + shear
• An affine transformation is a transformation that preserves co-linearity and the
ratio of distances.
• The parallel lines in an original image will be parallel in the output image.
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48. Types of Geometric Transformation
7. Affine Transformation (Cont.)
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52. What is Radiometry?
• Radiometry is the part of image formation concerned with the relation among
the amounts of light energy emitted from light sources, reflected from surfaces,
and registered by sensors
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53. What is Radiometry? (Cont.)
• Concerned with the relationship between the amount of light radiating from a
surface and the amount incident at its image.
• In other words, what the brightness of the point will be.
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54. From 3D to 2D
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55. Image Intensity
• Image Intensity understanding is under-constrained.
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56. Concept of Angle (2D) : d𝜃
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57. Solid Angle (3D) : d𝜔
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1. Area of sphere 𝐴 = 4𝜋𝑟2
• Solid angle of sphere = 4𝜋
2. Area of hemisphere 𝐴 = 2𝜋𝑟2
• Solid angle of sphere = 2𝜋
58. Light Flux : d𝜙
• Luminous flux (in lumens) is a measure of the total amount of light a lamp puts
out.
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59. Radiant Intensity : J
• Brightness of source.
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76. Cameras
• A camera is an optical instrument used to capture an image. At their most basic,
cameras are sealed boxes (the camera body) with a small hole (the aperture) that
allows light in to capture an image on a light-sensitive surface
(usually photographic film or a digital sensor).
• Cameras have various mechanisms to control how the light falls onto the light-
sensitive surface. Lenses focus the light entering the camera, the size of the
aperture can be widened or narrowed to let more or less light into the camera,
and a shutter mechanism determines the amount of time the photo-sensitive
surface is exposed to the light.
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77. Pinhole Camera Model
• A pinhole camera is "a simple camera without a lens and with a single small
aperture." Many pinhole cameras are as a simple as a box with a hole in the
side.
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78. Pinhole Camera Model
• With a pinhole camera, this image is usually upside down and varies in clarity. Some
people use a pinhole camera to study the movement of the sun over time
(Solargraphy). A type of pinhole camera is often used to view an eclipse. Another
type of pinhole camera, the camera obscura, was once used by artists.
• The device allowed the artist to view a scene through a different perspective. The
artist would point the lens of the camera at the still life scene they wanted to
paint. The camera would frame the image in smaller perspective thus allowing the
artist to see the scene as it might appear painted. (Of course, this would eventually
lead to photography). The person using a camera obscura might even trace the
image on a piece of paper and achieve a very accurate copy of the scene.
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Reference: https://youtu.be/4jbjolpz2BQ
84. Projections
1. Forward Projection
• We want to mathematical model to describe how 3D world points get projected
into 2D pixel coordinates.
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87. Intrinsic Camera Parameters (Cont.)
• Describes coordinate transformation between film coordinates (projected image)
and pixel array
• Film cameras: scanning/digitization
• CCD cameras: grid of photosensors
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