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Component labeling - PowerPoint PPT Presentation

Introduction to Computer Vision Lecture 8

Introduction to Computer Vision Lecture 8

Introduction to Computer Vision Lecture 8. Roger S. Gaborski. Graduate Student Projects. Pupil Detection Results. Pineapple and Orange Examples. Histograms. A histogram is just a method for summarizing data, for example, gray level pixel values Can also summarize edge data.

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842 views • 68 slides
???? Computer and Robot Vision I

???? Computer and Robot Vision I

???? Computer and Robot Vision I. Chapter2: Binary Machine Vision: Thresholding and Segmentation Instructor: Shih- Shinh Huang. Contents. Introduction Thresholding Connected Components Labeling Signature Segmentation and Analysis. Computer and Robot Vision I. Introduction.

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733 views • 54 slides
June 24, 2013 Jason Su

June 24, 2013 Jason Su

June 24, 2013 Jason Su. Technologies for C/C++/Fortran. Single machine, multi-core P(OSIX) threads: bare metal multi-threading OpenMP : compiler directives that implement various constructs like parallel-for Single machine, GPU CUDA/ OpenCL : bare metal GPU coding

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629 views • 50 slides
Digital Image Processing Lecture 12: Image Topology

Digital Image Processing Lecture 12: Image Topology

Digital Image Processing Lecture 12: Image Topology. Prof. Charlene Tsai. Introduction. Definition : The study of properties of a figure that are unaffected by any deformation, as long as there is no tearing or joining of the figure. A region with 3 connected components (objects).

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609 views • 25 slides
P09503 Electrophotographic Development and Transfer Station

P09503 Electrophotographic Development and Transfer Station

P09503 Electrophotographic Development and Transfer Station. Team Members. Project Description. The purpose of this project is to make an existing electrophotographic development and transfer station functional and to improve the usability, safety and sensing abilities. EDTS End Users

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326 views • 23 slides
Spatial transformations

Spatial transformations

Spatial transformations. T. tiepoints. f(w,z). g(x,y). distorted. original. Affine transform. MATLAB code:. T=[2 0 0; 0 3 0; 0 0 1]; tform=maketform('affine', T); tform.tdata.T tform.tdata.Tinv WZ=[1 1; 3 2]; XY= tformfwd (WZ, tform); WZ2= tforminv (XY, tform);. Exercise#1.

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383 views • 22 slides
Thresholding

Thresholding

Thresholding. Here, the right image is created from the left image by thresholding, assuming that object pixels are darker than background pixels. As you can see, the result is slightly imperfect (dark background pixels). Geometric Properties.

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384 views • 19 slides

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