Antialiasing
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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.
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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.
HiPEAC 2019 Workshop - Use Cases
The document describes three use cases implemented on the Tulipp embedded platform:
1) Pedestrian detection for ADAS achieving 15 frames/s with 2-3 frame latency.
2) Stereo depth estimation for UAVs performing in real-time with streaming optimization.
3) Medical image processing reducing radiation dose by 4x and enhancing images at 30 frames/s.
The Tulipp tools and SDSoc/Vivado HLS were used to implement algorithms from C/C++ onto the FPGA-based platform while addressing challenges of memory, streaming, and hardware-suitability. Overall, the Tulipp platform performed well across different applications with high-level development.
Technologies Used In Graphics Rendering
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OpenCV.pdf
OpenCV is a Python library used for computer vision tasks like image classification, object detection, and face recognition. It processes images to understand their content. When analyzing images, OpenCV performs tasks like object classification to categorize objects, object identification to recognize specific instances, and edge detection using techniques like Canny edge detection. Key computer vision algorithms in OpenCV include SIFT for keypoint detection, template matching for finding areas of an image that match a template, and Viola-Jones for real-time object detection. OpenCV is useful for applications like driverless cars that require visual understanding of the environment.
OpenCV presentation series- part 4
Continuing the presentation series, the fourth part is about the blurring and sharpening of images. the manual method of doing the operations is given along with some functions for blurring. the next is about edge detection algorithms like Canny, Sobel, and Prewitt. also, the dilates and the eroded images are provided along with the canny ones.
I HAVE WORKED HARD FOR THIS PRESENTATION!! SO PLEASE SUPPORT GUYS!!!
Flatbed Scanner
A flatbed scanner optically scans images, text, or objects and converts them into a digital image. It works by placing the item to be scanned on a glass window. The quality of a scanner is judged by its color depth, resolution in pixels per inch (ppi), and density range (Drange). Higher-end scanners have greater color depth, resolution, and Drange, allowing them to capture more detail. Potential problems include scanner noise, dust, and improper operation, which can diminish image purity. The scanned image is converted to an RGB file that can be transferred and processed on a computer.
Depth Fusion from RGB and Depth Sensors by Deep Learning
deep learning, sensor fusion, depth fusion, camera, Lidar, depth sensor, sparse, CNN, encoder-decoder network, completion, mask, gradient, norm, loss, multiscale, RGB-D, prediction.
Online video object segmentation via convolutional trident network
발표자: 장원동 (고려대 박사과정)
발표일: 2017.8.
개요:
A semi-supervised online video object segmentation algorithm, which accepts user annotations about a target object at the first frame, will be presented. It propagates the segmentation labels at the previous frame to the current frame using optical flow vectors.
However, the propagation is error-prone. Therefore, I’ve developed the convolutional trident network, which has three decoding branches: separative, definite foreground, and definite background decoders.
Then, the algorithm performs Markov random field optimization based on outputs of the three decoders.
These process is sequentially carried out from the second to the last frames to extract a segment track of the target object.
Experimental results will demonstrate that this algorithm significantly outperforms the state-of-the-art conventional algorithms on the DAVIS benchmark dataset.
Wits presentation 6_28072015
This document provides an overview and tutorial on various techniques for object recognition, including cascading classifiers, convolutional neural networks (CNNs), and support vector machines (SVMs). It discusses the hierarchical concept formation problem and how these techniques can help a robot learn about its environment autonomously. For each technique, it covers the underlying concepts, example implementations in OpenCV or other libraries, and plans to analyze results through confusion matrices. The document serves as an introduction for researchers or students interested in object recognition and machine learning algorithms.
Final Thesis Presentation Licenseplaterecognitionincomplexscenes
Dhawal S Wazalwar presented his master's thesis on a license plate recognition system at Illinois Institute of Technology. The system uses image processing techniques like edge detection, morphological operations, and thresholding to detect license plates. It then segments characters and extracts features for recognition using a neural network classifier. Experimental results on over 1500 images showed average plate detection of 98.1% and character recognition of 97.05%. Further improvements could enhance performance for low contrast or non-standard plates.
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This document describes the capabilities of the VideometerLab 3 Multi-Spectral Imaging system. It captures images of samples at multiple narrow wavelength bands, allowing differentiation of materials based on their spectral signatures. Statistical analysis tools help identify subtle differences. Applications include quality control, forensic analysis, and medical diagnostics. The system provides high resolution spectral data without the complexity and cost of hyperspectral imaging.
Graphics a buffer
The A-buffer method is an extension of the depth-buffer method that allows for anti-aliasing and transparency. It works by building a pixel mask for each polygon fragment and determining the visible areas to average color values. The key data structure is the accumulation buffer, which stores color, opacity, depth, coverage, and other data for each pixel. It operates similar to a depth buffer but also considers opacity to determine the final pixel color.
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.
UNIT-4.pdf
- Autoencoders are unsupervised neural networks that compress input data into a latent space representation and then reconstruct the output from this representation. They aim to copy their input to their output with minimal loss of information.
- Autoencoders consist of an encoder that compresses the input into a latent space and a decoder that decompresses this latent space back into the original input space. The network is trained to minimize the reconstruction loss between the input and output.
- Autoencoders are commonly used for dimensionality reduction, feature extraction, denoising images, and generating new data similar to the training data distribution.
UNIT-4.pdf
- Autoencoders are unsupervised neural networks that compress input data into a latent space representation and then reconstruct the output from this representation. They aim to copy their input to their output with minimal loss of information.
- Autoencoders consist of an encoder that compresses the input into a latent space and a decoder that decompresses this latent space back into the original input space. The network is trained to minimize the reconstruction loss between the input and output.
- Autoencoders are commonly used for dimensionality reduction, feature extraction, denoising images, and generating new data similar to the training data distribution.
IMAGE PROCESSING
This document discusses real-time image processing. It begins with an introduction and definitions of real-time and non-real-time processing. It then discusses the requirements for a real-time image processing platform, including high resolution/frame rate video input and low latency. The document outlines some advantages of real-time image processing such as immediate results and automation. It then provides an overview of an object detection system using Viola-Jones detection with integral images, AdaBoost learning, and a cascade classifier structure. Experimental results show the cascade classifier can detect faces in real-time.
Digital Theory 1.pdf
This document discusses digital photography concepts related to color management from camera to print. It covers RAW files and their benefits over JPEGs in retaining greater dynamic range and flexibility for editing. It also discusses white balance, color temperature, calibration and profiling needed to accurately translate colors between devices like cameras, monitors and printers. Histograms and using them to analyze image tones and for quality control during editing are also covered.
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Mahmudul Hasan provides contact information and an overview of topics covered in the document, including software installation on Windows, the first Java program, data types in Java, variables in Java, type casting, and Java operators. The document appears to be an introduction to programming concepts in Java, explaining fundamental elements like data types, variables, and operators. It includes examples and explanations of primitive and non-primitive data types, local, instance, and static variables, and type casting in Java. The document covers a variety of operator types in Java such as arithmetic, relational, logical, bitwise, assignment, and conditional operators.
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Data Mining Zoo classification
A simple database containing 17 Boolean-valued attributes. The "type" attribute appears to be the class attribute. Here is a breakdown of which animals are in which type: (I find it unusual that there are 2 instances of "frog" and one of "girl"!)
Class# -- Set of animals:
====== ====================================================
1 -- (41) aardvark, antelope, bear, boar, buffalo, calf, cavy, cheetah, deer, dolphin, elephant, fruitbat, giraffe, girl, goat, gorilla, hamster, hare, leopard, lion, lynx, mink, mole, mongoose, opossum, oryx, platypus, polecat, pony, porpoise, puma, pussycat, raccoon, reindeer, seal, sealion, squirrel, vampire, vole, wallaby,wolf
2 -- (20) chicken, crow, dove, duck, flamingo, gull, hawk, kiwi, lark, ostrich, parakeet, penguin, pheasant, rhea, skimmer, skua, sparrow, swan, vulture, wren
3 -- (5) pitviper, seasnake, slowworm, tortoise, tuatara
4 -- (13) bass, carp, catfish, chub, dogfish, haddock, herring, pike, piranha, seahorse, sole, stingray, tuna
5 -- (4) frog, frog, newt, toad
6 -- (8) flea, gnat, honeybee, housefly, ladybird, moth, termite, wasp
7 -- (10) clam, crab, crayfish, lobster, octopus, scorpion, seawasp, slug, starfish, worm
Rgb&
The document discusses color models used in computer displays and printing. It describes the RGB and CMYK color models. The RGB color model uses additive color mixing of red, green, and blue light to produce colors and is used for computer displays. The CMYK color model uses subtractive color mixing of cyan, magenta, yellow, and black inks to produce colors and is used in color printing. Both models are described in terms of their primary colors and how combinations of those primary colors are used to produce other colors.
Curve modeling-bezier-curves
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3D Transformation
Transformation
Types of transformation
Why we use transformation
3D Transformation
3D Translation
3D Rotation
3D Scaling
3D Reflection
3D Shearing
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The document discusses different 3D display methods including parallel projection, perspective projection, depth cueing, visible line identification, visible surface identification, surface rendering, and material properties and shadows. Parallel projection preserves proportions but not realistic views, while perspective projection produces realistic views but not proportions. Depth cueing and visible surface identification are used to determine which surfaces are visible from a given viewing position. Surface rendering applies lighting effects to obscure hidden surfaces. Material properties and shadows can further enhance realism.
Arduino RFID Module (RC522) & Buzzer Access System
I just received my RC522 RFID reader and made this simple Arduino access control system that uses the reader, a buzzer for the alarm and a relay shield for the security system. The relay can be used to open a door lock and if you need a louder alarm then you can replace the small buzzer with a high power siren.
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Encoder
This presentation introduces encoders. It discusses that an encoder is a combinational circuit that performs the reverse operation of a decoder, with a maximum of 2n inputs and n outputs. The simplest encoder is a 2n-to-n binary encoder, where one of the 2n inputs is 1 and the output is an n-bit binary number representing the activated input. An example of an 8-to-3 binary encoder is shown, where only one of the 8 inputs can be activated at a time, and the 3 outputs represent the activated input in binary code.
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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...
ここ3000字までしか入らないけどタイトルの方がたくさん文字入ると思います。
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Antialiasing
- 2. Introducing Group Members Name ID 2
- 4. What does Antialiasing mean? “ Antialiasing is a software technique for diminishing jaggies- stair step -like lines that should be smooth. ” Even though the jagged appearance is reduced, it makes the image look fuzzier. (Anti-Aliasing (with and without))
- 5. Anti-aliasing: Definition Antialiasing is a technique used in digital imaging to reduce the visual defects that occur when high-resolution images are presented in a lower resolution output devices like the monitor or printer.
- 6. Before and After of Anti-Aliasing Aliased polygons (jagged edges) Anti-aliased polygons
- 7. Antialiasing Techniques • 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
- 8. 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
- 9. 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 adapted
- 10. Anti aliasing in OpenGL • 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.
Editor's Notes
- Anti-aliasing option in games Definition : “ Technology that removes jaggies & the staircase effect from images and video games in real-time making the scene smooth “ How jaggies & staircase effect occur Difference between straight line & a diagonal Preventing staircase effect Upgrade display device Anti-Aliasing
- Basic anti-aliasing methods Increase sample rate Render in higher resolution & down sample to display resolution Blur Edges (Post Processing) Done after rendering Methods MSAA & FXAA Work fundamentally the same way How hey work Take samples around missing data & fill with appropriate data