PPT-Local features & Harris corner detection

CS5670 Computer Vision Announcements Project 1 code due Thursday 225 at 1159pm Turnin via Github Classroom Project 1 artifact due Monday 31 at 1159pm Quiz this Wednesday 224 via Canvas. 9300 Harris Corners Pkwy, Charlotte, NC. Why extract features?. Motivation: panorama stitching. We have two images – how do we combine them?. Why extract features?. Motivation: panorama stitching. We have two images – how do we combine them?. Color . Camera models, camera calibration. Advanced image pre-processing . Line . detection. Corner detection. Maximally stable extremal regions. Mathematical Morphology. . binary. gray-scale. skeletonization. 9300 Harris Corners Pkwy, Charlotte, NC. Why extract features?. Motivation: panorama stitching. We have two images – how do we combine them?. Why extract features?. Motivation: panorama stitching. We have two images – how do we combine them?. . extraction: Corners. 9300 Harris Corners Pkwy, Charlotte, NC. Why extract . keypoints. ?. Motivation: panorama stitching. We have two images – how do we combine them?. Why extract . keypoints. ?. Color . Camera models, camera calibration. Advanced image pre-processing . Line . detection. Corner detection. Maximally stable extremal regions. Mathematical Morphology. . binary. gray-scale. skeletonization. Oscar . Danielsson. (osda02@csc.kth.se). Stefan . Carlsson. (. stefanc@csc.kth.se. ). Josephine Sullivan (. sullivan@csc.kth.se. ). DICTA08. The Problem. Object categories are often modeled by collections (bag-of-features) or constellations (pictorial structures) of local features . Source: D. Lowe, L. Fei-Fei. Canny edge detector. Filter image with x, y derivatives of Gaussian . Find magnitude and orientation of gradient. Non-maximum suppression:. Thin multi-pixel wide “ridges” down to single pixel width. Monday March . 7. Prof. Kristen . Grauman. UT-Austin. Midterm Wed.. Covers material up until 3/1. Solutions to practice exam handed out today. Bring a 8.5”x11” sheet of notes if you want. Review the outlines and notes on course website, accompanying reading in textbook. Jia-Bin Huang, Virginia Tech. Many slides from N Snavely, K. . Grauman. & . Leibe. , and D. Hoiem. Administrative Stuffs. HW 1 posted, due 11:59 PM Sept . 25. Submission through Canvas. Frequently Asked Questions for HW . Devi Parikh. Disclaimer: Many slides have been borrowed from Kristen . Grauman. , who may have borrowed some of them from others. Any time a slide did not already have a credit on it, I have credited it to Kristen. So there is a chance some of these credits are inaccurate.. The correspondence problem. A general pipeline for correspondence. If sparse correspondences are enough, . choose points for which we will search for correspondences (feature points). For each point (or every pixel if dense correspondence), describe point using a . Find a bottle:. 4. Categories. Instances. Find these two objects. Can’t do. unless you do not . care about few errors…. Can nail it. Building a Panorama. M. Brown and D. G. Low. e. . Recognising Panorama. Computer Vision, FCUP, . 2018/19. Miguel Coimbra. Slides by Prof. Kristen . Grauman. Today. Local . invariant . features. Detection of interest points. (Harris corner detection). Scale invariant blob detection: . Kevin Cheng. To detect and track key features needed to interpret events in a Soccer game from a video. Goal. Clip. Player Detector. Field Detector. Ball Detector. Overview. Frame Pre-Processing. Input Image.