'Hough transform' presentation slideshows

Object Detection

1.16k views • 62 slides

Image alignment

Image alignment. A look into the past. http://blog.flickr.net/en/2010/01/27/a-look-into-the-past/. A look into the past. Leningrad during the blockade. http://komen-dant.livejournal.com/345684.html. Bing streetside images.

728 views • 61 slides

Chapter 10 Image Segmentation

Chapter 10 Image Segmentation. Image segmentation divides an image into regions that are connected and have some similarity within the region and some difference between adjacent regions. The goal is usually to find individual objects in an image.

2.78k views • 51 slides

Introduction to Medical Image Analysis

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Fitting: The Hough transform

630 views • 48 slides

764 views • 38 slides

Ge 116 Module 1: Scanning Electron Microscopy

Ge 116 Module 1: Scanning Electron Microscopy. Part 2: EDS X-ray analysis and EBSD. Continuum X-rays. Characteristic X-rays. Characteristic X-rays. Characteristic X-rays. X-ray counting: EDS and WDS. X-ray counting: EDS and WDS.

625 views • 34 slides

Image Segmentation

Image Segmentation. What is Image Segmentation?. Segmentation: Split or separate an image into regions To facilitate recognition, understanding, and region of interests (ROI) processing Ill-defined problem The definition of a region is context-dependent. Outline. Discontinuity Detection

1.59k views • 32 slides

Non-destructive Measurement of Vegetable Seedling Leaf Area using Elliptical Hough Transform

Non-destructive Measurement of Vegetable Seedling Leaf Area using Elliptical Hough Transform. Chung-Fang Chien, Ta-Te Lin Department of Bio-Industrial Mechatronics Engineering, National Taiwan University, Taipei, Taiwan, ROC. INTRODUCTION. Traditionally

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Infrasonic Signal Detection Using The Hough Transform

Infrasonic Signal Detection Using The Hough Transform. D. J. Brown, B.L.N. Kennett, C. Tarlowski Research School of Earth Sciences, Australian National University, Canberra, 0200, Australia. Overview. Motivation The Hough Transform Three-Dimensional array processing

497 views • 23 slides

Utilization of a Combinatorial Hough Transform for Tracking in 3 Dimensions with a Drift Chamber

Utilization of a Combinatorial Hough Transform for Tracking in 3 Dimensions with a Drift Chamber. Stephen C. Johnson, Federica Ceretto, Axel Drees, Thomas K. Hemmick, Barbara Jacak, John Noe. The PHENIX Detector. Specs: Multi-subsystem (>10) experiment

334 views • 22 slides

Obstacle detection using v-disparity image

Obstacle detection using v-disparity image. Based on: Global Correlation Based Ground Plane Estimation Using V-Disparity Image, By: J. Zhao, J. Katupitiya and J. Ward, 2007

1.52k views • 20 slides

On Detection of Multiple Object Instances using Hough Transforms

On Detection of Multiple Object Instances using Hough Transforms. Olga Barinova Moscow State University. Victor Lempitsky University of Oxford. Pushmeet Kohli Microsoft Research Cambridge. Hough transforms. Object detection ? peaks identification in Hough images Beyond lines!!!

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CSSE463: Image Recognition Day 26

CSSE463: Image Recognition Day 26. This week Today: Finding lines and circles using the Hough transform ( Sonka 6.26) Next class: Applications of PCA Friday: Lab 7 (final one) Sunday night: Project plans and preliminary work due. See rubric Questions?. Finding lines in real images.

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Moments

Moments. describe the image content (or distribution) with respect to its axes. area of the object. center of mass. Centralized Moments. Moments are not invariant geometric transformations To achieve invariance under translation. Hu Moments.

707 views • 13 slides

Tuning the hierarchical procedure searching for the key where there is light

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Hough Transform

Hough Transform. 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. Finding lines in an image. Connection between image (x,y) and Hough (m,b) spaces

309 views • 11 slides

Hough Transform to Detect Circles

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Midterm review: Cameras

Midterm review: Cameras. Pinhole cameras Vanishing points, horizon line Perspective projection equation, weak perspective Lenses Human eye

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References

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