'Output size' diaporamas de présentation

Image Filtering in the Spatial Domain

Image Filtering in the Spatial Domain. Overview: Linear filtering. Linear filters Applications Denoising Non-linear filters: median filter Sharpening Subsampling. Images as functions. Source: S. Seitz. Images as functions.

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Linear Filters

736 views • 72 slides

Linear Filtering

Linear Filtering. CS 678 Spring 2018. Outline. Linear filtering Box filter vs. Gaussian filter Median filter Gaussian and Laplacian pyramids Sharpening images. Some slides from Lazebnik. Motivation: Noise reduction. Given a camera and a still scene, how can you reduce noise?.

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EEM 561 Machine Vision Lecture 5 Linear Filtering

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Predicting performance of applications and infrastructures

Predicting performance of applications and infrastructures. Tania Lorido 27th May 2011. Problem definition. Objective Predicting utilization of resources (memory , CPU, ...) on different computing systems in order to determine application behavior.

601 views • 44 slides

I/O-Algorithms

I/O-Algorithms. Lars Arge Spring 2012 February 14 , 2012. I/O-Model. Parameters N = # elements in problem instance B = # elements that fits in disk block M = # elements that fits in main memory K = # output size in searching problem We often assume that M>B 2

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CSE373: Data Structures & Algorithms Lecture 18: Network Flow, NP-Completeness, and More

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A Multi-resolution Topological Representation for Non-manifold Meshes

A Multi-resolution Topological Representation for Non-manifold Meshes. Leila De Floriani, Paola Magillo, Enrico Puppo, Davide Sobrero University of Genova Genova (Italy). Non-Manifold Multiresolution Modeling: Why Non-Manifold?.

473 views • 31 slides

Computational Geometry

Computational Geometry. Piyush Kumar (Lecture 5: Range Searching). Welcome to CIS5930. Range Searching : recap. 1D Range search kD trees Range Trees Fractional Cascading. Range Searching. Preprocess a set P of objects for efficiently answering queries.

446 views • 17 slides

COMP/EECE 7/8740 Neural Networks

COMP/EECE 7/8740 Neural Networks. Mapping Properties of Multi-Layer Perceptrons MLP February 20, 2001. Basic NN Architectures. Feed-forward NN Directed graph in which a path never visits the same node twice Relatively simple behavior

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Update By: Brian Klug, Li Fan

162 views • 5 slides