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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/

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Computer Representation of Images

Computer Representation of Images. A picture function f(x,y) is a real-valued function of two variables, having values that are nonnegative and bounded, 0 ? f(x,y) ? L-1 for all (x,y)

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Edge Detection

Edge Detection. Edge detection. Convert a 2D image into a set of curves Extracts salient features of the scene More compact than pixels. Origin of Edges. Edges are caused by a variety of factors. surface normal discontinuity. depth discontinuity. surface color discontinuity.

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Edge Detection

Edge Detection. Edge detection. Convert a 2D image into a set of curves Extracts salient features of the scene More compact than pixels. Origin of Edges. Edges are caused by a variety of factors. surface normal discontinuity. depth discontinuity. surface color discontinuity.

652 views • 55 slides

Edge Detection

Edge Detection. Edge detection. Convert a 2D image into a set of curves Extracts salient features of the scene More compact than pixels. Origin of Edges. Edges are caused by a variety of factors. surface normal discontinuity. depth discontinuity. surface color discontinuity.

585 views • 55 slides

Edge Detection

Edge Detection. Edge detection. Convert a 2D image into a set of curves Extracts salient features of the scene More compact than pixels. Origin of Edges. Edges are caused by a variety of factors. surface normal discontinuity. depth discontinuity. surface color discontinuity.

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Color Invariance

Color Invariance. Shadow Removal Seminar. What we passed till now. Cause to shadows, and what shadows means for us (the interpretation of shadows in human brain). How to create shadows graphically. Some shadow detection techniques. This lecture Overview. Intro Shadows

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H.264 ?????????????. ??????? ???????? ??. ?????????? http://image.cse.nsysu.edu.tw/. Outline. H.264/AVC Mode Decision Frame-Based Fast Mode Decision Method Experimental Results. H.264/AVC. Description Advantage & Disadvantage. H.264/AVC Description. ITU-T VCEG

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Binary Morphology

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Mathematical Morphology

Mathematical Morphology. Christopher Chedeau Gauthier Lemoine. Overview. Goals Segmentation Edge detection Skeletonization Image compression Noise reduction Feature Detection. Algorithms Erosion & Dilation Opening & Closing Gradient Hit & Miss Thinning Top Hat Convolution

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CS 440 / ECE 448 Introduction to Artificial Intelligence Fall 2006

CS 440 / ECE 448 Introduction to Artificial Intelligence Fall 2006. Instructor: Eyal Amir TAs: Deepak Ramachandran (head TA), Jaesik Choi. Today: Computer Vision. Vision tasks Given an image or a set of images formed by a camera or another sensor (e.g., laser) Identify objects

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Lecture 2: Convolution and edge detection

CS4670: Computer Vision. Noah Snavely. Lecture 2: Convolution and edge detection. From Sandlot Science. Gaussian Kernel. Source: C. Rasmussen. Mean vs. Gaussian filtering. Gaussian filters. = 1 pixel. = 10 pixels. = 30 pixels. = 5 pixels. Gaussian filter.

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Edge Detection

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Announcements

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Announcements

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CS 223B Assignment 1 Help Session

CS 223B Assignment 1 Help Session. Dan Maynes-Aminzade. What Makes a Car a Car?. NOT CARS. CARS. Object Detection. Goal: find an object of a pre-defined class in a static image or video frame. Approach Extract certain image features, such as edges, color regions, textures, contours, etc.

306 views • 22 slides