'Slide credit' diaporamas de présentation

Probabilistic Inference Lecture 3

Probabilistic Inference Lecture 3. M. Pawan Kumar pawan.kumar@ecp.fr. Slides available online http:// cvc.centrale-ponts.fr /personnel/ pawan /. Recap of lecture 1. Exponential Family. P( v ) = exp {- ? ? ? ? ? ? ( v ) - A( ? )}. Sufficient Statistics. Log-Partition Function.

1.41k views • 122 slides

Feature Detection

Feature Detection. Image features. Global features global properties of an image, including intensity histogram, frequency domain descriptors, covariance matrix and high order statistics, etc. Local features

1.22k views • 107 slides

Image Filtering

Image Filtering. Overview of Filtering. Convolution Gaussian filtering Median filtering. Overview of Filtering. Convolution Gaussian filtering Median filtering. Motivation: Noise reduction. Given a camera and a still scene, how can you reduce noise?.

1.02k views • 75 slides

Algorithms and Applications in Computer Vision

Algorithms and Applications in Computer Vision. Lihi Zelnik -Manor lihi@ee.technion.ac.il Lecture 6: Feature detection. Today: Feature Detection and Matching. Local features Pyramids for invariant feature detection Invariant descriptors Matching. Image matching. by Diva Sian.

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Class 3: Feature Coding and Pooling

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TP14 - Indexing local features

TP14 - Indexing local features. Computer Vision, FCUP , 2014 Miguel Coimbra Slides by Prof. Kristen Grauman. Matching local features. Kristen Grauman. Matching local features. ?. Image 2. Image 1.

533 views • 43 slides

TP14 - Indexing local features

TP14 - Indexing local features. Computer Vision, FCUP , 2014 Miguel Coimbra Slides by Prof. Kristen Grauman. Matching local features. Kristen Grauman. Matching local features. ?. Image 2. Image 1.

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Emancipating education Abraham Lincoln Presidential Library Springfield, IL

Emancipating education Abraham Lincoln Presidential Library Springfield, IL. September 28, 2013 Felix Ankel, MD. Watson. Human. Curate. From Hierarchies to Networks. Community of Practice. Community of practice. Create. All,

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Fitting : Voting and the Hough Transform

Fitting : Voting and the Hough Transform. Monday, Feb 14 Prof. Kristen Grauman UT-Austin. Now: Fitting. Want to associate a model with observed features. [Fig from Marszalek & Schmid, 2007]. For example, the model could be a line, a circle, or an arbitrary shape . Kristen Grauman.

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Space Weather Storms: From Sun to Earth

Space Weather Storms: From Sun to Earth. Bob Rutledge NOAA Space Weather Prediction Center Boulder, Colorado December 4 th , 2013. Cleveland WFO Space Weather Webinar. Outline. The Sun/Solar Cycle Sequence of Events Space Weather Phenomena/Impacts Solar Flares Radiation Storms

555 views • 35 slides

Review: Intro to recognition

Review: Intro to recognition. Recognition tasks Machine learning approach: training, testing, generalization Example classifiers Nearest neighbor Linear classifiers. Image features. Spatial support:. Pixel or local patch. Segmentation region. Bounding box. Whole image. Image features.

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Hidden Markov Models (HMMs)

Hidden Markov Models (HMMs). Steven Salzberg CMSC 828H, Univ. of Maryland Fall 2010. What are HMMs used for?. Real time continuous speech recognition (HMMs are the basis for all the leading products)

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Internetworking II: MPLS, Security, and Traffic Engineering

Internetworking II: MPLS, Security, and Traffic Engineering. 3035/GZ01 Networked Systems Kyle Jamieson Department of Computer Science University College London. Last time: Internetworking. IP interconnects many heterogeneous networks The Internet is a datagram network

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Edge detection

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Image classification

Image classification. Given the bag-of-features representations of images from different classes, how do we learn a model for distinguishing them?. Classifiers. Learn a decision rule assigning bag-of-features representations of images to different classes. Decision boundary. Zebra. Non-zebra.

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Introduction to Embedded Systems

Introduction to Embedded Systems. Objectives. Introduction to embedded systems Embedded system components Hardware Software Embedded system programming. Introduction to Embedded Systems Setha Pan-ngum . Contents. Introduction to embedded systems Software engineering

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Accommodating Students with Disabilities in a clinical setting

Accommodating Students with Disabilities in a clinical setting. Postsecondary Disability Training Institute June 11, 2014 Lynnett Van Slyke, CRC Director Disability Resources and Services University of Pittsburgh. DISABILITY OFFICE. STUDENT. CLINICAL FACULTY. ACCOMMODATION.

292 views • 14 slides

Embedded Processors

Embedded Processors. Processors. Microprocessors for PCs Embedded processors or Microcontrollers for embedded systems Often with lower clock speeds Integrated with memory and I/O devices e.g. A/D D/A PWM CAN Higher environmental specs.

453 views • 13 slides

Agenda

Agenda. Introduction Bag-of-words model Visual words with spatial location Part-based models Discriminative methods Segmentation and recognition Recognition-based image retrieval Datasets & Conclusions. Lazebnik, Schmid & Ponce, 2006. Slide credit: S. Lazebnik. Slide credit: S. Lazebnik.

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