PPT-Reverse-Projection Method for Measuring Camera MTF

Stan Birchfield MTF measures camera sharpness Optical transfer function OTF Fourier transform of impulse response Modulation transfer function MTF Magnitude of OTF Spatial frequency response SFR. Albrecht . Dürer. , . Mechanical creation of a perspective image. , 1525. Overview of next two lectures. The pinhole projection model. Qualitative properties. Perspective projection matrix. Cameras with lenses. cs129: Computational Photography. James Hays, Brown, Fall 2012. © Jeffrey Martin (jeffrey-martin.com). most slides from Steve Seitz, . Rick . Szeliski. , and Alexei A. Efros. Feature selection. Distribute points evenly over the image. Overview. The pinhole projection model. Qualitative properties. Perspective projection matrix. Cameras with lenses. Depth of focus. Field of view. Lens aberrations. Digital cameras. Sensors. Color. Artifacts. Computer Vision and Image Processing (CVIP). Ifeoma. Nwogu. inwogu@buffalo.edu. Lecture 4 – Image formation(part I). Schedule. Last class . linear algebra . overview . Today. Image formation and camera properties. Projection and . Epipolar. Lines. Introduction to Computer Vision. Ronen Basri. Weizmann Institute of Science. Perspectivity. Material covered. Pinhole . camera model, perspective projection. Two . view . Camera geometry and image alignment. Josef . Sivic. http://. www.di.ens.fr. /~josef. INRIA, . WILLOW, ENS/INRIA/CNRS UMR 8548. Laboratoire. . d’Informatique. , . Ecole. . Normale. . Supérieure. Albrecht . Dürer. , . Mechanical creation of a perspective image. , 1525. Overview of next two lectures. The pinhole projection model. Qualitative properties. Perspective projection matrix. Cameras with lenses. CS 776 Spring . 2014. Cameras & Photogrammetry . 1. Prof. . Alex Berg. (Slide credits to many folks on individual slides). Cameras & Photogrammetry 1. Albrecht . Dürer. . early 1500s. Brunelleschi, early 1400s. A. dvances . in . Geometric . C. omputer . V. ision . & . Recognition. Jan-Michael Frahm. Fall 2011. Introductions. 2. Class. Recent methods in computer vision. Broadness of the class depends on YOU!. Computer Vision and Image Processing (CVIP). Ifeoma. Nwogu. inwogu@buffalo.edu. Lecture 4 – Image formation(part I). Schedule. Last class . linear algebra . overview . Today. Image formation and camera properties. Linda Shapiro . 1. Müller-Lyer Illusion. http://www.michaelbach.de/ot/sze_muelue/index.html. . What do you know about perspective projection?. Vertical lines?. Other lines?. 2. Image formation. Let. CSE 455. Linda Shapiro. . 1. Projection. http://www.julianbeever.net/pave.htm. Do sizes, lengths seem accurate?. How do you know?. 2. Projection. http://www.julianbeever.net/pave.htm. What’s wrong?. KavitaBala. Source: S. Lazebnik. Announcements. Prelim . next Thu. Everything till Monday. Where are we?. Imaging: pixels, features, …. Scenes: geometry, material, lighting. Recognition: people, objects, …. ?. Pinhole camera. Pinhole camera model. linear projection in homogeneous coordinates!. Camera is a mapping between 3D world (object space) and 2D image;. Camera model: matrix representing camera mapping; interested in central projection.