'Smoothing filter' diaporamas de présentation

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Computational Photography CSE 590

Computational Photography CSE 590

Computational Photography CSE 590. Tamara Berg Filtering & Pyramids. Image filtering. Image filtering: compute function of local neighborhood at each position Really important! Enhance images Denoise, resize, increase contrast, etc. Extract information from images

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649 views • 53 slides
EE5358 Computer Vision

EE5358 Computer Vision

EE5358 Computer Vision. Edge Detection Muhammad Saleem Koul, SM IEEE. Why Edge Detection?. Courtesy [Gonzalez & Woods]. Why do we use Edge Detection?.

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768 views • 50 slides
Convolution

Convolution

Basis beeldverwerking (8D040 ) d r. Andrea Fuster Prof.dr . Bart ter Haar Romeny dr. Anna Vilanova Prof.dr.ir . Marcel Breeuwer. Convolution. Contents. Spatial filtering Correlation Convolution Filters: Smoothing filters Sharpening filters Borders. Spatial filtering.

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914 views • 49 slides
Continuous Time Domain Filters

Continuous Time Domain Filters

Continuous Time Domain Filters. A few very simple active filters. C2. K. R1. R3. C4. 2nd order Sallen and Key low-pass filter. Continuous Time Domain LPF. Generic Architecture. Check Stability!. C. R. R. C. 2nd order Sallen and Key low-pass filter. Continuous Time Domain LPF.

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689 views • 35 slides
Gradients and edges

Gradients and edges

Points of sharp change in an image are interesting: change in reflectance change in object change in illumination noise Sometimes called edge points. General strategy determine image gradient

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479 views • 33 slides
A Robust Pedestrian Detection Approach Based on Shapelet Feature and Haar Detector Ensembles

A Robust Pedestrian Detection Approach Based on Shapelet Feature and Haar Detector Ensembles

A Robust Pedestrian Detection Approach Based on Shapelet Feature and Haar Detector Ensembles. Wentao Yao, Zhidong Deng. TSINGHUA SCIENCE AND TECHNOLOGY ISSNl l1007-0214l l04/12l lpp40-50 Volume 17, Number 1, February 2012. OUTLINE. Introduction Proposed Algorithm

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416 views • 27 slides
Gesture Based Interface to Robots using Kinect and Matlab

Gesture Based Interface to Robots using Kinect and Matlab

Gesture Based Interface to Robots using Kinect and Matlab. Team: Katherine Coley: krcoley@buffalo.edu Smita Chutke: smitachutke@gmail.com Johanna Ingrid Carolina Hellstrom: johannai@buffalo.edu. *PowerPoint compiled by Katherine Coley.

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536 views • 27 slides
Simulations: Cellular Automata

Simulations: Cellular Automata

Simulations: Cellular Automata . Robert Alvarez, Dakota Boyd, Hyder Hussain, Jonathan Martin. Mechanism. Defines rand() NxN domain. Generates MxM grid of filters based on an MxM tensor field.

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283 views • 18 slides
Digital Image Processing Week IV

Digital Image Processing Week IV

Digital Image Processing Week IV. Thurdsak LEAUHATONG. Neighborhood Processing. Linear vs non-linear operation. Neighborhood processing Define center point ( x,y ) Perform operations involving only pixels in the neighborhood Result of operation is response to process at that point

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324 views • 17 slides

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