'Image noise' diaporamas de présentation

General structure of a CV problem

Analysis. Pre-processing. Image Acquisition. Noise Img enhancement Transform. Sensor Illumination. Lines Corners Contours Regions Optical flow. Feature Extraction. Knowledge Base. General structure of a CV problem. Problem. Interpretation. Digital Images.

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IAEA Post Graduate Educational Course Radiation Protection and Safe Use of Radiation Sources

Chapter 3 DIAGNOSTIC RADIOLOGY Image quality Dr M A Oghabian Medical Physics Group, Tehran University of Medical Sciences www.oghabian.net. IAEA Post Graduate Educational Course Radiation Protection and Safe Use of Radiation Sources. Aim:

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Local Disparity Estimation with Three-Moded Cross Census and Advanced Support Weight

Local Disparity Estimation with Three-Moded Cross Census and Advanced Support Weight. Zucheul Lee, Student Member, IEEE, Jason Juang, and Truong Q. Nguyen, Fellow, IEEE IEEE Transactions on Multimedia, June 2013. Outline. Introduction Related Works Propsed Method

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Stanford CS223B Computer Vision, Winter 2005 Lecture 3 Advanced Features

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Image Quality Radiographic Resolution.

Image Quality Radiographic Resolution. Demelza Green Jan 2004 Technologies for Imaging. Radiographic Resolution. The resolving power of a radiograph. How close structures can be and still appear separate - fine detail. Radiographic Resolution.

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Detecting Digital Image Forgeries Using Sensor Pattern Noise

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Image Noise

Image Noise. John Morris Department of Computer Science, Tamaki Campus The University of Auckland. Stereo Image Noise Sources. Signal noise Electromagnetic interference eg cross-talk Quantum behaviour of electronic devices eg resistor shot-noise

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Analog Imaging II Intensifying Screens

Analog Imaging II Intensifying Screens. By Professor Stelmark. SCREEN CONSTRUCTION Use of film to detect x-rays and to image anatomical structures is inefficient. In fact, less than 1% of the x-rays incident on radiographic film interact with the film and contribute to the latent image.

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Lecture 13

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Object Tracking/Recognition using Invariant Local Features

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AVERAGING

AVERAGING. BANDWIDTH. LARGE (WIDE). SMALL (NARROW). PELVIS. FOV 340 RFOV 65 Slice 7mm Matrix 256 Scan Percent 60 NSA 4 Time 5:54. KNEE. FOV 160 RFOV 80 Matrix 256 Scan Percent 90 Turbo Spin Echo Slice 4 NSA 2 Knee Coil Time 2:28. WRIST. Gradient Echo 3D Volume Acq.

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CVPR 2003 Tutorial Recognition and Matching Based on Local Invariant Features

CVPR 2003 Tutorial Recognition and Matching Based on Local Invariant Features. David Lowe Computer Science Department University of British Columbia. Object Recognition. Definition: Identify an object and determine its pose and model parameters Commercial object recognition

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IMAGE PROCESSING

IMAGE PROCESSING. AKSHAY P S3 EC ROLL NO. 9. What is an image?. We can think of an image as a function, f , from R 2 to R: f ( x, y ) gives the intensity at position ( x, y ) Realistically, we expect the image only to be defined over a rectangle, with a finite range:

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Wavelet denoising (Einstein first)

Wavelet denoising (Einstein first). Original image. Original image + noise. UWT + hard Threshold. DWT + Hard Threshold. Quantum dot image. Original image. Original image + noise. A different view. Original image. Original image + noise. Denoised image by UWT.

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Exposure ???? 4:

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