'Hough space' diaporamas de présentation

CS 1699: Intro to Computer Vision Matching and Fitting

CS 1699: Intro to Computer Vision Matching and Fitting. Prof. Adriana Kovashka University of Pittsburgh September 29, 2015. Today. Fitting models (lines) to points, i.e. find the parameters of a model that best fits the data Least squares Hough transform RANSAC

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Morphological Operators

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Fitting: The Hough transform

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Computer Vision

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Computer Vision

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CSE 185 Introduction to Computer Vision

CSE 185 Introduction to Computer Vision. Feature Matching. Feature matching. Correspondence: matching points, patches, edges, or regions across images. ?. Keypoint matching. 1. Find a set of distinctive key- points. 2. Define a region around each keypoint. A 1. B 3.

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CSE 185 Introduction to Computer Vision

CSE 185 Introduction to Computer Vision. Feature Matching. Feature matching. Correspondence: matching points, patches, edges, or regions across images. ?. Keypoint matching. 1. Find a set of distinctive key- points. 2. Define a region around each keypoint. A 1. B 3.

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Status of Hough Transform Method

Status of Hough Transform Method. Cvetan Cheshkov CERN. Contents. Introduction New Hough space variables definition Results: Efficiency, fakes Investigation of inefficiency and fake track sources Resolution and its dependence upon event multiplicity Time consumption Conclusions & Plans.

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Understanding the TSL EBSD Data Collection System

Understanding the TSL EBSD Data Collection System. Harry Chien, Bassem El-Dasher, Anthony Rollett, Gregory Rohrer. Overview. Understanding the diffraction patterns Source of diffraction SEM setup per required data The makeup of a pattern Setting up the data collection system

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Status of Hough Transform TPC Tracker

Status of Hough Transform TPC Tracker. C.Cheshkov. Outline. Introduction Hough Transform (HT) approach: Description of the algorithm Practical implementation for fast filling of the Hough space Hough space variables Recent improvements Results: Efficiency, fakes Resolution

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Hough Transform Filter

Hough Transform Filter. D. A. Petyt July 1 st 2004. What? A fast and simple algorithm to find linear features (i.e. tracks) in event images Used previously to find narrow em showers in NO n A events and pion tracks in Soudan 2 events

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Hough Transform

Hough Transform. Reading Watt, 10.3-10.4. An edge is not a line. How can we detect lines ?. Finding lines in an image. Option 1: Search for the line at every possible position/orientation What is the cost of this operation? Option 2: Use a voting scheme: Hough transform .

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Analysis of Multi-Sensor Radar Detection based on the TBD-HT Approach in ECM Environment

T M. Yes. Target Detection - fix threshold - CFAR. Binary Integration in Hough Space. Hough Transform. Multi sensors. No. Analysis of Multi-Sensor Radar Detection based on the TBD-HT Approach in ECM Environment.

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