Anti aliasing
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The document discusses anti-aliasing techniques used to reduce aliasing artifacts that occur during rasterization. It describes two general approaches: area sampling and super-sampling. Area sampling shades pixels based on the area covered by primitives, while super-sampling samples at a higher resolution then filters the image down. The document also discusses how to enable and control anti-aliasing effects in OpenGL using functions like glEnable(), glHint(), and by setting blending factors.
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Computer graphics
Aliasing refers to imperfect reconstruction of a signal when it is sampled at too low of a frequency, resulting in patterns that do not accurately represent the original signal. It occurs when high frequency components are treated as low frequencies during reconstruction. Anti-aliasing aims to minimize aliasing artifacts by removing high frequency components before sampling through techniques like analog filters, optical blurring in digital photography, or supersampling in computer graphics to smooth edges.
Antialiasing
This presentation introduces anti-aliasing techniques. It begins with defining anti-aliasing as a technique to reduce jagged edges by softening lines and edges. It then shows before and after examples of anti-aliased versus non-anti-aliased polygons. Finally, it discusses the two main anti-aliasing approaches of area sampling and super-sampling and provides details on how each technique works.
Anti aliasing Computer Graphics
Anti-aliasing is a technique used to reduce jagged or stair-stepped edges in digital images by adding subtle color variations around edges. It works by averaging pixel color values across edges to make them appear smoother. There are several techniques for anti-aliasing including increasing image resolution, prefiltering by calculating pixel color based on object overlap within a pixel area, and postfiltering through supersampling at a higher resolution and then averaging down. Unweighted area sampling draws lines as rectangles and sets pixel intensity proportional to the amount of overlap with the rectangle rather than distance from the pixel center.
Antialiasing
This document discusses various anti-aliasing techniques used to minimize aliasing artifacts when representing high-resolution images and signals at lower resolutions. It describes spatial anti-aliasing, super sampling, Nyquist frequency, pixel weighting masks, pixel phasing, compensating for line intensity differences, and anti-aliasing area boundaries. The key techniques are spatial anti-aliasing which removes high frequency signal components before resampling, super sampling which takes multiple samples inside each pixel and averages for smoother edges, and pixel weighting masks which assign different weights to pixel subsections.
Anti aliasing
The document discusses different techniques for anti-aliasing in digital images. It begins with an introduction to anti-aliasing and defines it as a technique used to reduce visual defects like jagged edges that occur when high-resolution images are displayed at lower resolutions. It then provides examples of images with and without anti-aliasing. The main body discusses the three main classes of anti-aliasing algorithms: increasing resolution, prefiltering, and postfiltering/supersampling. It focuses on explaining prefiltering and postfiltering/supersampling techniques in more detail.
Aliasing & anti aliasing Slide
Aliasing occurs when a signal is sampled at a rate less than twice its highest frequency, resulting in apparent frequencies below what is present. This causes jagged or stair-stepped lines in digital images. Anti-aliasing techniques aim to reduce these visual defects. There are three main anti-aliasing algorithms: increasing resolution, area sampling by considering intensities over a region, and supersampling which creates a high-resolution virtual image, applies a low-pass filter, and resamples at a lower resolution. Supersampling independently supports arbitrary symmetric filters and allows wider filter coverage than hardware methods alone.
Anti- aliasing computer graphics
Anti-aliasing is a technique used to reduce aliasing, which makes curved or slanted lines appear jagged when displayed on a lower resolution output device like a monitor. Aliasing occurs because the device lacks enough resolution to smoothly represent curved lines. Anti-aliasing works by adding subtle color changes around lines, which causes jagged edges to blur together when viewed from a distance. There are several anti-aliasing techniques, including increasing the display resolution, area sampling to shade pixels based on the area covered by thickened lines, and post-filtering by generating a higher resolution virtual image and averaging it down.
Antialiasing & Its different technique
This document discusses different anti-aliasing techniques used in computer graphics to smooth jagged edges. It describes pixel, aliased and anti-aliased images. The main anti-aliasing techniques discussed are supersampling, area sampling, multi-sampling anti-aliasing (MSAA) and fast-approximate anti-aliasing (FXAA). Supersampling takes color samples inside each pixel to calculate an average color, while area sampling computes pixel color based on object overlap with the pixel area. MSAA balances quality and performance using color manipulation, and FXAA applies blurring to obscure jagged edges without large computing power requirements.
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Computer graphics
Aliasing refers to imperfect reconstruction of a signal when it is sampled at too low of a frequency, resulting in patterns that do not accurately represent the original signal. It occurs when high frequency components are treated as low frequencies during reconstruction. Anti-aliasing aims to minimize aliasing artifacts by removing high frequency components before sampling through techniques like analog filters, optical blurring in digital photography, or supersampling in computer graphics to smooth edges.
Antialiasing
This presentation introduces anti-aliasing techniques. It begins with defining anti-aliasing as a technique to reduce jagged edges by softening lines and edges. It then shows before and after examples of anti-aliased versus non-anti-aliased polygons. Finally, it discusses the two main anti-aliasing approaches of area sampling and super-sampling and provides details on how each technique works.
Anti aliasing Computer Graphics
Anti-aliasing is a technique used to reduce jagged or stair-stepped edges in digital images by adding subtle color variations around edges. It works by averaging pixel color values across edges to make them appear smoother. There are several techniques for anti-aliasing including increasing image resolution, prefiltering by calculating pixel color based on object overlap within a pixel area, and postfiltering through supersampling at a higher resolution and then averaging down. Unweighted area sampling draws lines as rectangles and sets pixel intensity proportional to the amount of overlap with the rectangle rather than distance from the pixel center.
Antialiasing
This document discusses various anti-aliasing techniques used to minimize aliasing artifacts when representing high-resolution images and signals at lower resolutions. It describes spatial anti-aliasing, super sampling, Nyquist frequency, pixel weighting masks, pixel phasing, compensating for line intensity differences, and anti-aliasing area boundaries. The key techniques are spatial anti-aliasing which removes high frequency signal components before resampling, super sampling which takes multiple samples inside each pixel and averages for smoother edges, and pixel weighting masks which assign different weights to pixel subsections.
Anti aliasing
The document discusses different techniques for anti-aliasing in digital images. It begins with an introduction to anti-aliasing and defines it as a technique used to reduce visual defects like jagged edges that occur when high-resolution images are displayed at lower resolutions. It then provides examples of images with and without anti-aliasing. The main body discusses the three main classes of anti-aliasing algorithms: increasing resolution, prefiltering, and postfiltering/supersampling. It focuses on explaining prefiltering and postfiltering/supersampling techniques in more detail.
Aliasing & anti aliasing Slide
Aliasing occurs when a signal is sampled at a rate less than twice its highest frequency, resulting in apparent frequencies below what is present. This causes jagged or stair-stepped lines in digital images. Anti-aliasing techniques aim to reduce these visual defects. There are three main anti-aliasing algorithms: increasing resolution, area sampling by considering intensities over a region, and supersampling which creates a high-resolution virtual image, applies a low-pass filter, and resamples at a lower resolution. Supersampling independently supports arbitrary symmetric filters and allows wider filter coverage than hardware methods alone.
Anti- aliasing computer graphics
Anti-aliasing is a technique used to reduce aliasing, which makes curved or slanted lines appear jagged when displayed on a lower resolution output device like a monitor. Aliasing occurs because the device lacks enough resolution to smoothly represent curved lines. Anti-aliasing works by adding subtle color changes around lines, which causes jagged edges to blur together when viewed from a distance. There are several anti-aliasing techniques, including increasing the display resolution, area sampling to shade pixels based on the area covered by thickened lines, and post-filtering by generating a higher resolution virtual image and averaging it down.
Antialiasing & Its different technique
This document discusses different anti-aliasing techniques used in computer graphics to smooth jagged edges. It describes pixel, aliased and anti-aliased images. The main anti-aliasing techniques discussed are supersampling, area sampling, multi-sampling anti-aliasing (MSAA) and fast-approximate anti-aliasing (FXAA). Supersampling takes color samples inside each pixel to calculate an average color, while area sampling computes pixel color based on object overlap with the pixel area. MSAA balances quality and performance using color manipulation, and FXAA applies blurring to obscure jagged edges without large computing power requirements.
ResearchPaper_Final
1) The document discusses anti-aliasing techniques in computer graphics. Aliasing occurs when digitizing continuous images due to the discrete nature of pixels, causing jagged edges and moire patterns.
2) It explains how sampling works, where the continuous image is multiplied by a comb function to acquire discrete sample values at regular intervals. Reconstruction then interpolates between the samples.
3) The Nyquist sampling theorem states a signal must be sampled at least twice the highest frequency to avoid aliasing. Undersampling can cause frequencies to fold back and appear as lower frequencies.
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This document discusses various topics related to computer graphics including anti-aliasing, area sampling, the Koch curve, and the C curve. Anti-aliasing is a technique used to reduce jagged edges by blending pixels. Area sampling is an anti-aliasing method that treats pixels as areas and calculates color based on object overlap. The Koch curve is a fractal curve generated by recursively altering line segments. The C curve replaces lines with two shorter lines at 90 degrees to form triangles at each iteration.
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The document discusses the concept of aliasing which occurs when a signal is discretely sampled at too low of a rate. It describes how aliasing can cause signals to take on a false presentation and provide misleading information. The Nyquist sampling theorem states that the sampling frequency must be at least twice the highest frequency contained in the signal to avoid aliasing. The document then provides examples of how aliasing occurs with things like filmed wagon wheels and subsampled text images. It explains that aliasing can be avoided by low-pass filtering or blurring the signal before sampling to reduce the highest frequency.
Anti-aliasing: A quick presentation
The document discusses different types of antialiasing algorithms: FXAA, SMAA, MSAA, and SSAA. FXAA is the simplest and fastest approach that blurs pixel groups with sharp brightness changes. SMAA recognizes geometric patterns to blur along lines and curves. MSAA increases samples along polygon edges for a smoother effect without blurring. SSAA renders at a higher resolution and downsamples for the highest quality antialiasing but is also the most demanding on resources.
Morphological antialiasing
This document summarizes the morphological anti-aliasing (MLAA) technique. MLAA is an image post-processing anti-aliasing algorithm that analyzes color discontinuities and classifies edge patterns to apply blending weights to reduce aliasing. It works by separating color areas, classifying edge types, and computing new pixel colors in aliasing areas. MLAA can handle color images and achieves reasonable performance without quality impacts. While efficient, it has limitations such as runtime depending on edge numbers and inability to respect geometric boundaries well. The document compares MLAA to other techniques and provides examples of its ability to reduce aliasing in text and 3D game images.
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The document provides an overview of an introductory computer graphics course. It outlines the course objectives of understanding fundamental graphical operations, recent advances in computer graphics, and user interface issues. It then lists and briefly describes the main topics that will be covered in the course, including basic raster graphics, 2D transformations, clipping, filling techniques, 3D graphics, visibility, and advanced topics like rendering, raytracing, antialiasing and fractals.
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This document provides an introduction to computer graphics. It defines computer graphics as the creation, storage, and manipulation of pictures and drawings using digital computers. Computer graphics is used across diverse fields such as engineering, medicine, education, entertainment, and more. The document discusses basic terms related to display devices such as pixels, resolution, color depth, and frame buffers. It also describes different types of display devices including raster scan displays, random scan displays, direct view storage tubes, flat panel displays, and stereoscopic displays. Applications of computer graphics such as design, image processing, animation, simulation, and medical imaging are also summarized.
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This document discusses computer graphics systems and their components. It describes video display devices like CRTs and how they work. Color is generated using techniques like beam penetration and shadow masks. Raster scan and random scan displays are covered. Input devices for graphics like mice, tablets, and gloves are also summarized. The document provides details on graphics hardware like frame buffers, refresh rates, and video controllers.
Super Resolution of Image
1. The document presents a method for super resolution of text images using ant colony optimization. It involves registering multiple low resolution images, fusing them, performing soft classification to assign pixel values to multiple classes, and using ant colony optimization for super resolution mapping to increase the resolution.
2. Key steps include SURF-based image registration, intensity-based and discrete wavelet transform fusion, decision tree-based soft classification, and ant colony optimization to assign pixel values based on pheromone updating to increase resolution.
3. Test cases on images with angular displacement, blurred text, etc. show that the method increases resolution successfully but can add some noise, though processing is faster than alternatives. Ant colony optimization
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ResearchPaper_Final
1) The document discusses anti-aliasing techniques in computer graphics. Aliasing occurs when digitizing continuous images due to the discrete nature of pixels, causing jagged edges and moire patterns.
2) It explains how sampling works, where the continuous image is multiplied by a comb function to acquire discrete sample values at regular intervals. Reconstruction then interpolates between the samples.
3) The Nyquist sampling theorem states a signal must be sampled at least twice the highest frequency to avoid aliasing. Undersampling can cause frequencies to fold back and appear as lower frequencies.
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This document discusses various topics related to computer graphics including anti-aliasing, area sampling, the Koch curve, and the C curve. Anti-aliasing is a technique used to reduce jagged edges by blending pixels. Area sampling is an anti-aliasing method that treats pixels as areas and calculates color based on object overlap. The Koch curve is a fractal curve generated by recursively altering line segments. The C curve replaces lines with two shorter lines at 90 degrees to form triangles at each iteration.
Aliasing
The document discusses the concept of aliasing which occurs when a signal is discretely sampled at too low of a rate. It describes how aliasing can cause signals to take on a false presentation and provide misleading information. The Nyquist sampling theorem states that the sampling frequency must be at least twice the highest frequency contained in the signal to avoid aliasing. The document then provides examples of how aliasing occurs with things like filmed wagon wheels and subsampled text images. It explains that aliasing can be avoided by low-pass filtering or blurring the signal before sampling to reduce the highest frequency.
Anti-aliasing: A quick presentation
The document discusses different types of antialiasing algorithms: FXAA, SMAA, MSAA, and SSAA. FXAA is the simplest and fastest approach that blurs pixel groups with sharp brightness changes. SMAA recognizes geometric patterns to blur along lines and curves. MSAA increases samples along polygon edges for a smoother effect without blurring. SSAA renders at a higher resolution and downsamples for the highest quality antialiasing but is also the most demanding on resources.
Morphological antialiasing
This document summarizes the morphological anti-aliasing (MLAA) technique. MLAA is an image post-processing anti-aliasing algorithm that analyzes color discontinuities and classifies edge patterns to apply blending weights to reduce aliasing. It works by separating color areas, classifying edge types, and computing new pixel colors in aliasing areas. MLAA can handle color images and achieves reasonable performance without quality impacts. While efficient, it has limitations such as runtime depending on edge numbers and inability to respect geometric boundaries well. The document compares MLAA to other techniques and provides examples of its ability to reduce aliasing in text and 3D game images.
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The document provides an overview of an introductory computer graphics course. It outlines the course objectives of understanding fundamental graphical operations, recent advances in computer graphics, and user interface issues. It then lists and briefly describes the main topics that will be covered in the course, including basic raster graphics, 2D transformations, clipping, filling techniques, 3D graphics, visibility, and advanced topics like rendering, raytracing, antialiasing and fractals.
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This document provides an introduction to computer graphics. It defines computer graphics as the creation, storage, and manipulation of pictures and drawings using digital computers. Computer graphics is used across diverse fields such as engineering, medicine, education, entertainment, and more. The document discusses basic terms related to display devices such as pixels, resolution, color depth, and frame buffers. It also describes different types of display devices including raster scan displays, random scan displays, direct view storage tubes, flat panel displays, and stereoscopic displays. Applications of computer graphics such as design, image processing, animation, simulation, and medical imaging are also summarized.
Introduction to computer graphics part 2
This document discusses cathode ray tubes (CRTs) and how they work as display devices for computer graphics. It explains that CRTs contain an electron gun that emits a stream of electrons which are focused into a beam and directed to specific points on the phosphor-coated front of the picture tube. When the electron beam hits a phosphor dot, it glows proportionally to the beam strength. Color CRTs use three electron guns and a shadow mask to separately excite red, green, and blue phosphor dots, allowing for color displays. The document also covers other properties of CRTs like resolution, persistence, and aspect ratio.
Overview of Graphics System
The document describes various types of computer display devices and their characteristics. It discusses raster and random scan displays, CRT monitors, color CRT technologies including beam penetration and shadow mask methods, and other display types such as direct view storage tubes. Input devices are also covered, including keyboards, mice, digitizers, and touch screens.
study Diffusion Curves: A Vector Representation for Smooth-Shaded Images
This document introduces diffusion curves, a new vector-based image representation. Diffusion curves represent smooth shaded images using curves that diffuse color on both sides. This allows for more complex gradients than previous methods. The document outlines how diffusion curves are created either manually, assisted via color sampling, or automatically from bitmaps. It also describes how diffusion curves are rendered by rasterizing color sources along curves, computing a gradient field, diffusing color, and reblurring. This new representation offers benefits over gradient meshes while being compact and enabling artistic control.
Introduction to computer graphics part 1
This document discusses computer graphics systems and their components. It describes video display devices like CRTs and how they work. Color is generated using techniques like beam penetration and shadow masks. Raster scan and random scan displays are covered. Input devices for graphics like mice, tablets, and gloves are also summarized. The document provides details on graphics hardware like frame buffers, refresh rates, and video controllers.
Super Resolution of Image
1. The document presents a method for super resolution of text images using ant colony optimization. It involves registering multiple low resolution images, fusing them, performing soft classification to assign pixel values to multiple classes, and using ant colony optimization for super resolution mapping to increase the resolution.
2. Key steps include SURF-based image registration, intensity-based and discrete wavelet transform fusion, decision tree-based soft classification, and ant colony optimization to assign pixel values based on pheromone updating to increase resolution.
3. Test cases on images with angular displacement, blurred text, etc. show that the method increases resolution successfully but can add some noise, though processing is faster than alternatives. Ant colony optimization
M.sc. m hassan
This document summarizes a framework for precise object tracking under image deformation. It discusses various types of image deformation including noise, scaling and rotation, blurring, and illumination changes. It proposes techniques to address each deformation type, including denoising filters, linear and radon transformations for rotation, deblurring algorithms, and using color models like YCbCr that are robust to illumination changes. It also describes morphological operations, geometrical modeling for 3D pose estimation, and using an FIR model for motion prediction to track objects under deformation. The framework is aimed at applications like robot vision, surveillance, medical imaging and more.
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This document summarizes an experiment on the correlation between images and depth maps in free viewpoint image coding. The experiment found that when using an accurate depth map, there is no need to consider correlation between the image and depth map. Various image codecs and post-filtering techniques were tested, and the best results were achieved using a post-filter set without a joint filter. Future work could optimize bit allocation between coded images and depth maps.
Understanding neural radiance fields
Neural Radiance Fields (NeRF) generates novel views of complex scenes by optimizing an underlying continuous volumetric scene function using a sparse set of input views. NeRF describes a continuous scene as a 5D vector-valued function that takes in a 3D location and 2D viewing direction, and outputs color and density. To render a novel view, NeRF marches camera rays through the scene to sample points, feeds those points into a neural network to produce colors and densities, and uses volume rendering to accumulate these properties into an image. In summary, NeRF reconstructs scenes by feeding multiple input images into a neural network that predicts color and density values used to render new views via volume rendering.
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The development of this image processing software will help editing process to be done effectively. It requires less space on hard disk; emphasizing only on the crucial image processing functions and the executable program will take less space.
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The document discusses different types of graphics display systems including raster scan displays, random scan displays, and flat panel displays. It describes the key components of cathode ray tube (CRT) displays such as the electron gun and phosphor screen and how they generate images. It also covers color reproduction methods for CRTs like beam penetration and three color guns.
Computer Graphics
The document discusses computer graphics and various display technologies. It describes raster and vector display systems used in computer graphics. Raster systems store and display images as a grid of pixels using a process called scan conversion. Each pixel is stored in a frame buffer and refreshed rapidly to display the image. Vector systems draw images as geometric objects and lines. Common display technologies discussed include cathode ray tubes (CRT), liquid crystal displays (LCD), and plasma displays. Applications of computer graphics mentioned include computer-aided design (CAD), multimedia, visualization, and entertainment.
Computer Graphics Introduction
This document summarizes the contents of Chapter 1 from the book "Computer Graphics Using OpenGL" by F. Hill. The chapter introduces computer graphics, describing where it is used in fields like art, entertainment and publishing. It outlines some basic elements of pictures created in CG like polylines, text, filled regions and raster images. It also discusses graphics display and input devices, covering line drawing, raster displays, color depth, video monitors, flat panel displays and hard-copy printers.
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3. Examples are provided applying each algorithm to draw specific geometric shapes given endpoint or radius values. Tables show the calculations and plotted points at each iteration.
Polygon clipping
Clipping polygons is more complex than clipping lines because it involves considering the polygon as a whole rather than just its individual line segments. The Sutherland-Hodgman algorithm clips polygons by considering each edge of the clipping region individually and clipping the subject polygon against that edge, resulting in a new clipped polygon. This process is repeated for each clipping region edge until the polygon is fully clipped. The algorithm takes as input a list of polygon vertices and outputs a new list of clipped polygon vertices.
06 clipping
The document describes several algorithms for clipping lines and polygons to a clip rectangle in computer graphics, including:
- The Cohen-Sutherland algorithm which uses outcodes to determine if lines can be trivially accepted or rejected from the clip rectangle without intersection calculations.
- The Cyrus-Back algorithm which clips lines by solving the simultaneous equations for the intersections of the line with the clip rectangle edges.
- It also discusses parametric line clipping which finds the intersection parameters t along the line segment to determine where it enters and exits the clip rectangle edges.
Computer graphics
The document provides information on algorithms for drawing basic geometric shapes like lines, circles, and ellipses digitally. It discusses:
1. The line drawing algorithm DDA which samples a line at integer intervals by determining corresponding y-values based on the slope.
2. Bressenham's line algorithm which determines which pixel to plot by comparing the distance of potential pixels to the true line using a decision parameter that is updated iteratively.
3. The midpoint circle algorithm which uses a decision variable to iteratively choose pixels on the circumference of a circle based on whether their midpoint is inside or outside the circle.
4. It provides the basic steps of the midpoint circle algorithm and discusses extending it to
Mid point line Algorithm - Computer Graphics
The document describes the midpoint line algorithm for plotting lines on a grid. It works by calculating the midpoint between each set of pixels and determining if it falls above or below the line to choose the next pixel. It only requires integer calculations, avoiding errors from division or multiplication. The algorithm is derived step-by-step and an example is provided to demonstrate how it is implemented to plot a line between two points.
Cohen-sutherland & liang-basky line clipping algorithm
The document describes two line clipping algorithms: Cohen-Sutherland and Liang-Barsky. Cohen-Sutherland assigns region codes to line endpoints and checks for complete visibility, invisibility, or partial visibility. It then finds intersection points if the line is partially visible. Liang-Barsky uses the parametric line equation and clipping window inequalities to determine intersection points u1 and u2, clipping the line between those points if u1 < u2. Liang-Barsky is generally more efficient as it requires only one division to update u1 and u2, while Cohen-Sutherland may repeatedly calculate unnecessary intersections.
Clipping
The document discusses different concepts related to clipping in computer graphics including 2D and 3D clipping. It describes how clipping is used to eliminate portions of objects that fall outside the viewing frustum or clip window. Various clipping techniques are covered such as point clipping, line clipping, polygon clipping, and the Cohen-Sutherland algorithm for 2D region clipping. The key purposes of clipping are to avoid drawing objects that are not visible, improve efficiency by culling invisible geometry, and prevent degenerate cases.
14 file handling
This document discusses reading from and writing to files in Java programs. It explains how to open a file using a Scanner or PrintWriter object, read/write data using methods like next() and println(), and close the file when finished. It recommends storing file data in memory structures, processing it, then writing the results back to improve efficiency over sequential file access. Proper file handling and exception handling are also emphasized.
Viewing transformation
The document discusses viewport transformations which map objects from one coordinate system to another, such as mapping from world coordinates to screen coordinates. It explains that viewport transformations are used for viewing transformations like zooming and moving drawings. The document also describes how a matrix M can be determined to map a window defined in world coordinates to a viewport defined in screen pixel coordinates.
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Final Thesis Presentation Licenseplaterecognitionincomplexscenes
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IRJET- Optical Character Recognition using Neural Networks by Classification ...
This document proposes a method for optical character recognition (OCR) using neural networks and character classification based on symmetry. It involves three main phases: 1) A classification phase that categorizes training characters by their symmetry properties (vertical, horizontal, total, none), 2) A training phase that trains separate neural networks on each classified group, and 3) A recognition phase that preprocesses documents, extracts lines and characters, and recognizes characters using the trained neural networks. Experimental results show the method achieves up to 99.2% accuracy on Arial font without punctuation, outperforming traditional OCR methods. However, it requires 14.01 seconds for full network training.
A NOBEL HYBRID APPROACH FOR EDGE DETECTION
The objective of this paper is to present the hybrid approach for edge detection. Under this technique, edge
detection is performed in two phase. In first phase, Canny Algorithm is applied for image smoothing and in
second phase neural network is to detecting actual edges. Neural network is a wonderful tool for edge
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JP INFOTECH is one of the leading Matlab projects provider in Chennai having experience faculties. We have list of image processing projects as our own and also we can make projects based on your own base paper concept also.
For more details:
http://jpinfotech.org/final-year-ieee-projects/2014-ieee-projects/matlab-projects/
Presentation shortstory
This document discusses an approach to single image denoising that takes into account aspects of the camera imaging pipeline. It first "unprocesses" an image to reverse common image processing steps and estimate the original raw image captured by the sensor. A neural network is then trained to denoise these synthetic raw images. Key steps in the image formation process that are modeled include demosaicing, digital gain, white balance, color correction, gamma compression, and tone mapping. The network architecture is a U-Net, and it achieves state-of-the-art results with a 14-25% reduction in error compared to other methods on both raw and sRGB image metrics.
Blind Source Camera Identification
This is a research area of digital forensic . how to identify which picture is taken by which camera based on its feature not watermark
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This Slide is an intro for the "fully progressive approach to single image super-resolution" Paper
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PR-217: EfficientDet: Scalable and Efficient Object Detection
TensorFlow Korea 논문읽기모임 PR12 217번째 논문 review입니다
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영상링크: https://youtu.be/11jDC8uZL0E
Hardware software co simulation of edge detection for image processing system...
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IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and TechnologyAutomatic Detection of Window Regions in Indoor Point Clouds Using R-CNN
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A new approach for generalised unsharp masking alogorithm
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New approach for generalised unsharp masking alogorithm
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology.
Deep Learning
This document provides an overview of artificial intelligence and machine learning techniques, including:
1. It defines artificial intelligence and lists some common applications such as gaming, natural language processing, and robotics.
2. It describes different machine learning algorithms like supervised learning, unsupervised learning, reinforced learning, and their applications in areas such as healthcare, finance, and retail.
3. It explains deep learning concepts such as neural networks, activation functions, loss functions, and architectures like convolutional neural networks and recurrent neural networks.
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The document summarizes an enhanced edge adaptive steganography approach using a threshold value for region selection. It aims to improve the quality and modification rate of a stego image compared to Sobel and Canny edge detection techniques. The proposed approach uses a threshold value to select high frequency pixels from the cover image for data embedding using LSBMR. Experimental results on 100 images show about a 0.2-0.6% improvement in image quality measured by PSNR and a 4-10% improvement in modification rate measured by MSE compared to Sobel and Canny edge detection.
A novel method for character segmentation of vehicle
The document presents a novel method for character segmentation of vehicle license plates written in two rows. It discusses how the license plate image is first preprocessed through steps like grayscale conversion, binarization, and noise removal. Horizontal and vertical projections are then used to segment the image into lines and words. Character segmentation is done by analyzing the spacing between characters. Finally, zone segmentation is performed to divide each character into four zones to extract features for recognition. The method aims to simplify license plate image analysis for applications like traffic monitoring and surveillance.
The International Journal of Engineering and Science (The IJES)
The International Journal of Engineering & Science is aimed at providing a platform for researchers, engineers, scientists, or educators to publish their original research results, to exchange new ideas, to disseminate information in innovative designs, engineering experiences and technological skills. It is also the Journal's objective to promote engineering and technology education. All papers submitted to the Journal will be blind peer-reviewed. Only original articles will be published.
Object detection at night
The document provides an overview of object detection methods for nighttime surveillance. It discusses two main approaches: (1) a Contrast Change method that detects objects based on changes in local contrast between frames, and (2) a Salient Contrast Analysis method that improves on the first by adding adaptability using machine learning and feedback from trajectory analysis. Experimental results showed the Salient Contrast Analysis method achieved better detection accuracy and lower tracking errors than the original Contrast Change method.
Image enhancement
This document provides an overview of various image enhancement techniques. It begins with an introduction to image enhancement and its objectives. It then outlines and describes several categories of enhancement methods, including spatial-frequency domain methods, point operations, histogram operations, spatial operations, and transform operations. Specific techniques discussed in detail include contrast stretching, clipping, thresholding, median filtering, unsharp masking, and principal component analysis for multispectral images. The document also covers color image enhancement and techniques for pseudocoloring.
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みなさんこんにちはこれ何文字まで入るの？40文字以下不可とか本当に意味わからないけどこれ限界文字数書いてないからマジでやばい文字数いけるんじゃないの？えこ...
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Anti aliasing
- 1. Siwan Lama M.Sc. IV Semester Dept. of Computer & System Science Visva-Bharati University India 2014
- 2. The process of removing one or more of the unwanted artifact is called anti-aliasing or smoothing. In particular, anti-aliasing has played a critical role in the quality of the quality of the texture mapped and ray traced image.
- 3. //The geometric transformation of digital images is inherently a sampling process. Anti-aliasing occurs when the input signal is under sampled. There are two solutions to this problem:- i) it requires a display resolution which is costly or unavailable. ii) the second solution forces the signal to conform to the low sampling rate by attenuating the high frequency components that give rise to the aliasing artifact. HERE, I WOULD LARGELY CONCENTRATE TO COUNTER THE ALIASING ARTIFACT IN A LIMITED MANNER AND USING THE OpenGL
- 4. The effect created when rasterization is performed over a discrete series of pixels. In particular, when lines or edges do not necessarily align directly with a row or column of pixels, that line may appear unsmooth and have a stair-step edge appearance. Anti aliasing utilizes blending techniques to blur the edges of the lines and provide the viewer with the illusion of a smoother line. Points, lines or polygons can be anti aliased.
- 5. Anti-Aliasing • Two general approaches: Area sampling and super- sampling • Area sampling approaches sample primitives with a box (or Gaussian, or whatever) rather than spikes 1. Requires primitives that have area (lines with width) 2. Sometimes referred to as pre-filtering •Super-sampling samples at higher resolution, then filters down the resulting image i. Sometimes called post-filtering ii. The prevalent form of anti-aliasing in hardware
- 6. Area Sampling • shade pixels according to the area covered by thickened line • this is un-weighted area sampling • a rough approximation formulated by dividing each pixel into a finer grid of pixels
- 7. Super-sampling •Sample at a higher resolution than required for display, and filter image down •Issues of which samples to take, and how to average them •4 to 16 samples per pixel is typical •Samples might be on a uniform grid, or randomly positioned, or other variants •Number of samples can be
- 8. •OpenGL calculates a coverage value for each fragment based on the fraction of the pixel square on the screen that it would cover. •In RGBA mode, OpenGL multiplies the fragment’s alpha value by its coverage. •Resulting alpha value is used to blend the fragment with the corresponding pixel already in the frame buffer.
- 9. Hints •With OpenGL, you can control the behavior of anti-aliasing effects by using the glHint() function: void glHint(GLenum target, GLenum hint); •target: parameter indicates which behavior is to be controlled. Specifies the desired sampling quality of points, lines or polygons during antialiasing operations •target parameter can be GL_POINT_SMOOTH_HINT GL_LINE_SMOOTH_HINT GL_POLYGON_SMOOTH_HINT GL_FOG_HINT GL_PERSPECTIVE_CORRECTION_HINT
- 10. glHint(target, hint) • hint: parameter specifies the approach • hint parameter can be GL_FASTEST (the most efficient option) GL_NICEST (the highest-quality option) GL_DONT_CARE (no preference)
- 11. Enabling Anti-aliasing • Anti aliasing is enabled using the glEnable() command, We can enable GL_POINT_SMOOTH or GL_LINE_SMOOTH modes. • With RGBA mode, you must also enable blending to utilize GL_SRC_ALPHA as the source factor and GL_ONE_MINUS_SRC_ALPHA as the destination factor. •Using a destination factor of GL_ONE will make intersection points a little brighter.