'Gradient direction' presentation slideshows

Detectors and Descriptors

905 views • 90 slides

Lecture 2: Edge detection and resampling

CS6670: Computer Vision. Noah Snavely. Lecture 2: Edge detection and resampling. From Sandlot Science. Administrivia. New room starting Thursday: HLS B11. Administrivia. Assignment 1 (feature detection and matching) will be out Thursday Turning via CMS: https://cms.csuglab.cornell.edu/

841 views • 66 slides

Extracting Branching Object Geometry via Cores

Extracting Branching Object Geometry via Cores. Doctoral Dissertation Defense Yoni Fridman August 17, 2004 Advisor: Stephen Pizer. Outline. Motivation, Background, and Thesis Statement Cores in 3D Handling Branching Objects with Cores Ends of Cores Evaluation and Results

723 views • 59 slides

Grouping and Segmentation

603 views • 46 slides

Coherent Line Drawing

Coherent Line Drawing . ?? ??? ???? ??? ??? 2008.5.22. ??. Abstract 1. Introduction 1.1 Related work 1.2 Contribution and Overview 2. Flow construction 2.1 Edge Tangent Flow 3. Line construction 3.1 Flow-based Difference-of-Gaussians 3.2 Iterative FDoG filtering 4 . Results

649 views • 39 slides

Thursday, Oct 16

Thursday, Oct 16. Today. Pset1 examples Midterm solutions Homography recap, computing mosaics. Weak perspective. Approximation: treat magnification as constant Assumes scene depth << average distance to camera

447 views • 35 slides

The Segmentation Problem

The Segmentation Problem. Segmentation attempts to partition the pixels of an image into groups that strongly correlate with the objects in an image Typically the first step in any automated computer vision application. Image Segmentation.

766 views • 31 slides

Lines and Arcs Segmentation

Lines and Arcs Segmentation. In some image sets, lines, curves, and circular arcs are more useful than regions or helpful in addition to regions. Lines and arcs are often used in object recognition stereo matching document analysis. Parameter Estimation Methods Hough Transform.

383 views • 28 slides

1.72k views • 24 slides

240-373 Image Processing

240-373 Image Processing. Montri Karnjanadecha montri@coe.psu.ac.th http://fivedots.coe.psu.ac.th/~montri. Chapter 8. Edge Detection. Basic Edge Detection. Edge detection, ideally, identifies all the lines that outline the objects in an image. Template for edge detection:

346 views • 22 slides

Improved 3D Live-Wire Method with Application to 3D CT Chest Image Analysis

Improved 3D Live-Wire Method with Application to 3D CT Chest Image Analysis. Kongkuo Lu and William E. Higgins Penn State University Department of Electrical Engineering University Park, PA 16802, USA. SPIE Medical Imaging 2006: Image Processing, San Diego, CA, 14 Feb . 2006. Motivation.

358 views • 22 slides

CS B553: Algorithms for Optimization and Learning

CS B553: Algorithms for Optimization and Learning. Gradient descent. Key Concepts. Gradient descent Line search Convergence rates depend on scaling Variants: discrete analogues, coordinate descent Random restarts.

349 views • 21 slides

Multimedia Communications EG 371 and EE 348

Multimedia Communications EG 371 and EE 348. Dr Matt Roach Lecture 6 Image processing (filters). Need templates and convolution Elementary image filters are used enhance certain features de-enhance others edge detect smooth out noise discover shapes in images. Convolution of Images

288 views • 14 slides

Computational Intelligence Winter Term 2018/19

95 views • 9 slides

Basic Edge Detection

Basic Edge Detection. Edge detection, ideally, identifies all the lines that outline the objects in an image. Template for edge detection: Y_difference(x,y) = value(x,y) - value(x,y+1) This is equivalent to convolving with image with a 2x1 template 1 -1

227 views • 8 slides