DOCSLIB.ORG

3-D Scene Reconstruction from Line Correspondences Between Multiple Views

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
3-D Scene Reconstruction from Line Correspondences Between Multiple Views 3-D SCENE RECONSTRUCTION FROM LINE CORRESPONDENCES BETWEEN MULTIPLE VIEWS A dissertation proposal submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy in Computer Science and Engineering By Michael Linger Ph.D., Computer Science and Engineering, Wright State University, 2014 M.S., Computer Science, Wright State University, 2009 B.S., Computer Science, Wright State University, 2005 B.S., Mathematics, Wright State University, 2005 A.A.S., Electronics Engineering Technology, ITT Technical Institute, 1996 2014 Wright State University WRIGHT STATE UNIVERSITY GRADUATE SCHOOL December 1, 2014 I HEREBY RECOMMEND THAT THE DISSERTATION PREPARED UNDER MY SUPERVISION BY Michael Linger ENTITLED 3-D Scene Reconstruction from Line Correspondences between Multiple Views BE ACCEPTED IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF Doctor of Philosophy. Arthur A. Goshtasby, Ph.D. Dissertation Director and Director, Computer Science and Engineering Graduate Program Robert E. W. Fyffe, Ph.D. Vice President for Research and Dean of the Graduate School Committee on Final Examination Arthur A. Goshtasby, Ph.D. Mateen Rizki, Ph.D. Bin Wang, Ph.D. Thomas Wischgoll, Ph.D. Fred Garber, Ph.D. ABSTRACT Linger, Michael Ph.D. Computer Science and Engineering, Wright State University, 2014. 3-D Scene Reconstruction from Line Correspondences between Multiple Views. Three-dimensional scene reconstruction from 2-D images has many applications, such as surveillance, mission planning, autonomous navigation systems, cartography, and target recognition. Of specific interest to this research is the reconstruction of urban scenes containing man-made structures, such as roads and buildings, to support the burgeoning surveillance industry. Using 3-D maps to augment existing mission planning cartography products (DTED/SRTM, CADRG, CIB), mission and event planners will be able to compute strategic line- of-sight coverage for threat avoidance or threat prosecution. Forensic video analysts can use these models to recreate crime scenes, while law enforcement can build flight plans to minimize occlusions from tall structures in their persistent surveillance systems. Traditional methods of 3-D scene reconstruction leverage image points as a primitive element. Various approaches detect and correlate points for use in triangulation and 3-D reconstruction. Little work has been done in 3-D reconstruction using lines as primitives. In my research, I detect line segments and their associated planar surfaces. Lines detected in 2-D images are back projected to corresponding planar patches and triangulated via linear incident relations resulting in a reconstructed 3-D wireframe model. This research uses high resolution imagery at close range as could be collected by autonomous drones. It reduces data by an order of magnitude by exploiting the point-line duality of projective geometry. iii TABLE OF CONTENTS 1 Introduction .............................................................................................................................. 1 1.1 Purpose ............................................................................................................................. 1 1.2 Problem Description ........................................................................................................ 2 1.3 Scope ................................................................................................................................ 4 1.4 Document Overview ........................................................................................................ 5 2 Background .............................................................................................................................. 6 2.1 Introduction to Geometry ................................................................................................. 6 2.2 Geometric Primitives in Cartesian and Homogeneous Coordinates ................................ 7 2.3 Introduction to 2-D Projective Geometry ...................................................................... 10 2.4 Introduction to 1-D Projective Geometry ...................................................................... 12 2.5 Pinhole Camera Model .................................................................................................. 14 3 Method ................................................................................................................................... 16 3.1 Overview ........................................................................................................................ 16 3.2 Line Detection ................................................................................................................ 17 3.3 Camera Computation ..................................................................................................... 25 3.3.1 Calibration Box ...................................................................................................... 25 3.3.1.1 Edge Detection ................................................................................................... 26 3.3.1.2 Hough Transform ............................................................................................... 26 3.3.1.3 Connected Graph ............................................................................................... 27 3.3.1.4 Camera Parameters ............................................................................................ 30 3.3.1.5 Correlation ......................................................................................................... 31 iv 3.3.2 Surveyed Camera ................................................................................................... 32 3.4 Planar Surface Detection ................................................................................................ 35 3.5 Homographic Registration ............................................................................................. 38 3.5.1 Global Rotation ...................................................................................................... 39 3.5.2 Putative Correspondence........................................................................................ 40 3.5.3 Point Descriptor ..................................................................................................... 42 3.5.4 Template Matching Via Point Descriptor .............................................................. 43 3.5.5 2-D Homographic Registration from Correspondences ......................................... 45 3.5.5.1 Registration from Point Correspondences ......................................................... 45 3.5.5.2 Registration from Line Correspondences .......................................................... 47 3.5.5.3 Registration from Line Correspondences and Their 1-D Homographies ......... 49 3.6 Line Correspondence ..................................................................................................... 53 3.7 3-D Wireframe ............................................................................................................... 54 3.8 Polygon Boundary ......................................................................................................... 57 4 Results .................................................................................................................................... 58 4.1 Edge Detection ............................................................................................................... 58 4.2 Line Detection ................................................................................................................ 60 4.3 3-D Models .................................................................................................................... 62 4.3.1 Model Doll House .................................................................................................. 62 4.3.2 House Reconstruction ............................................................................................ 65 4.4 Error Analysis ................................................................................................................ 68 5 Conclusion ............................................................................................................................. 70 5.1 Summary of Contributions ............................................................................................. 70 v 5.2 Challenges Encountered ................................................................................................. 72 5.3 Recommendations for Future Research ......................................................................... 73 Appendix A Geometric Parameterization of a 2-D Homography .................................................. 75 Appendix B Orthogonal Regression .............................................................................................. 80 Appendix C Multiple Regression ................................................................................................... 84 Appendix D Bilinear Interpolation ................................................................................................ 85 Appendix E Line Templates .......................................................................................................... 87 Appendix F 2-D Image Registration with Points ........................................................................... 89 Appendix G 2-D image Registration with Lines
Load more

Recommended publications
  • Recovery of 3-D Shape from Binocular Disparity and Structure from Motion
    Perception & Psychophysics /993. 54 (2). /57-J(B Recovery of 3-D shape from binocular disparity and structure from motion JAMES S. TI'ITLE The Ohio State University, Columbus, Ohio and MYRON L. BRAUNSTEIN University of California, Irvine, California Four experiments were conducted to examine the integration of depth information from binocular stereopsis and structure from motion (SFM), using stereograms simulating transparent cylindri­ cal objects. We found that the judged depth increased when either rotational or translational motion was added to a display, but the increase was greater for rotating (SFM) displays. Judged depth decreased as texture element density increased for static and translating stereo displays, but it stayed relatively constant for rotating displays. This result indicates that SFM may facili­ tate stereo processing by helping to resolve the stereo correspondence problem. Overall, the re­ sults from these experiments provide evidence for a cooperative relationship betweel\..SFM and binocular disparity in the recovery of 3-D relationships from 2-D images. These findings indicate that the processing of depth information from SFM and binocular disparity is not strictly modu­ lar, and thus theories of combining visual information that assume strong modularity or indepen­ dence cannot accurately characterize all instances of depth perception from multiple sources. Human observers can perceive the 3-D shape and orien­ tion, both sources of information (along with many others) tation in depth of objects in a natural environment with im­ occur together in the natural environment. Thus, it is im­ pressive accuracy. Prior work demonstrates that informa­ portant to understand what interaction (if any) exists in the tion about shape and orientation can be recovered from visual processing of binocular disparity and SFM.
    [Show full text]
  • Abstract Computer Vision in the Space of Light Rays
    ABSTRACT Title of dissertation: COMPUTER VISION IN THE SPACE OF LIGHT RAYS: PLENOPTIC VIDEOGEOMETRY AND POLYDIOPTRIC CAMERA DESIGN Jan Neumann, Doctor of Philosophy, 2004 Dissertation directed by: Professor Yiannis Aloimonos Department of Computer Science Most of the cameras used in computer vision, computer graphics, and image process- ing applications are designed to capture images that are similar to the images we see with our eyes. This enables an easy interpretation of the visual information by a human ob- server. Nowadays though, more and more processing of visual information is done by computers. Thus, it is worth questioning if these human inspired “eyes” are the optimal choice for processing visual information using a machine. In this thesis I will describe how one can study problems in computer vision with- out reference to a specific camera model by studying the geometry and statistics of the space of light rays that surrounds us. The study of the geometry will allow us to deter- mine all the possible constraints that exist in the visual input and could be utilized if we had a perfect sensor. Since no perfect sensor exists we use signal processing techniques to examine how well the constraints between different sets of light rays can be exploited given a specific camera model. A camera is modeled as a spatio-temporal filter in the space of light rays which lets us express the image formation process in a function ap- proximation framework. This framework then allows us to relate the geometry of the imaging camera to the performance of the vision system with regard to the given task.
    [Show full text]
  • Stereopsis by a Neural Network Which Learns the Constraints
    Stereopsis by a Neural Network Which Learns the Constraints Alireza Khotanzad and Ying-Wung Lee Image Processing and Analysis Laboratory Electrical Engineering Department Southern Methodist University Dallas, Texas 75275 Abstract This paper presents a neural network (NN) approach to the problem of stereopsis. The correspondence problem (finding the correct matches between the pixels of the epipolar lines of the stereo pair from amongst all the possible matches) is posed as a non-iterative many-to-one mapping. A two-layer feed forward NN architecture is developed to learn and code this nonlinear and complex mapping using the back-propagation learning rule and a training set. The important aspect of this technique is that none of the typical constraints such as uniqueness and continuity are explicitly imposed. All the applicable constraints are learned and internally coded by the NN enabling it to be more flexible and more accurate than the existing methods. The approach is successfully tested on several random­ dot stereograms. It is shown that the net can generalize its learned map­ ping to cases outside its training set. Advantages over the Marr-Poggio Algorithm are discussed and it is shown that the NN performance is supe­ rIOr. 1 INTRODUCTION Three-dimensional image processing is an indispensable property for any advanced computer vision system. Depth perception is an integral part of 3-d processing. It involves computation of the relative distances of the points seen in the 2-d images to the imaging device. There are several methods to obtain depth information. A common technique is stereo imaging. It uses two cameras displaced by a known distance to generate two images of the same scene taken from these two different viewpoints.
    [Show full text]
  • Distance Estimation from Stereo Vision: Review and Results
    DISTANCE ESTIMATION FROM STEREO VISION: REVIEW AND RESULTS A Project Presented to the faculty of the Department of the Computer Engineering California State University, Sacramento Submitted in partial satisfaction of the requirements for the degree of MASTER OF SCIENCE in Computer Engineering by Sarmad Khalooq Yaseen SPRING 2019 © 2019 Sarmad Khalooq Yaseen ALL RIGHTS RESERVED ii DISTANCE ESTIMATION FROM STEREO VISION: REVIEW AND RESULTS A Project by Sarmad Khalooq Yaseen Approved by: __________________________________, Committee Chair Dr. Fethi Belkhouche __________________________________, Second Reader Dr. Preetham B. Kumar ____________________________ Date iii Student: Sarmad Khalooq Yaseen I certify that this student has met the requirements for format contained in the University format manual, and that this project is suitable for shelving in the Library and credit is to be awarded for the project. __________________________, Graduate Coordinator ___________________ Dr. Behnam S. Arad Date Department of Computer Engineering iv Abstract of DISTANCE ESTIMATION FROM STEREO VISION: REVIEW AND RESULTS by Sarmad Khalooq Yaseen Stereo vision is the one of the major researched domains of computer vision, and it can be used for different applications, among them, extraction of the depth of a scene. This project provides a review of stereo vision and matching algorithms, used to solve the correspondence problem [22]. A stereo vision system consists of two cameras with parallel optical axes which are separated from each other by a small distance. The system is used to produce two stereoscopic pictures of a given object. Distance estimation between the object and the cameras depends on two factors, the distance between the positions of the object in the pictures and the focal lengths of the cameras [37].
    [Show full text]
  • Depth Perception, Part II
    Depth Perception, part II Lecture 13 (Chapter 6) Jonathan Pillow Sensation & Perception (PSY 345 / NEU 325) Fall 2017 1 nice illusions video - car ad (2013) (anamorphosis, linear perspective, accidental viewpoints, shadows, depth/size illusions) https://www.youtube.com/watch?v=dNC0X76-QRI 2 Accommodation - “depth from focus” near far 3 Depth and scale estimation from accommodation “tilt shift photography” 4 Depth and scale estimation from accommodation “tilt shift photography” 5 Depth and scale estimation from accommodation “tilt shift photography” 6 Depth and scale estimation from accommodation “tilt shift photography” 7 Depth and scale estimation from accommodation “tilt shift photography” 8 more on tilt shift: Van Gogh http://www.mymodernmet.com/profiles/blogs/van-goghs-paintings-get 9 Tilt shift on Van Gogh paintings http://www.mymodernmet.com/profiles/blogs/van-goghs-paintings-get 10 11 12 countering the depth-from-focus cue 13 Monocular depth cues: Pictorial Non-Pictorial • occlusion • motion parallax relative size • accommodation shadow • • (“depth from focus”) • texture gradient • height in plane • linear perspective 14 • Binocular depth cue: A depth cue that relies on information from both eyes 15 Two Retinas Capture Different images 16 Finger-Sausage Illusion: 17 Pen Test: Hold a pen out at half arm’s length With the other hand, see how rapidly you can place the cap on the pen. First using two eyes, then with one eye closed 18 Binocular depth cues: 1. Vergence angle - angle between the eyes convergence divergence If you know the angles, you can deduce the distance 19 Binocular depth cues: 2. Binocular Disparity - difference between two retinal images Stereopsis - depth perception that results from binocular disparity information (This is what they’re offering in 3D movies…) 20 21 Retinal images in left & right eyes Figuring out the depth from these two images is a challenging computational problem.
    [Show full text]
  • Depth Perception 0
    Depth Perception 0 Depth Perception Suggested reading:! ! Stephen E. Palmer (1999) Vision Science. ! Read JCA (2005) Early computational processing in binocular vision and depth perception. Progress in Biophysics and Molecular Biology 87: 77-108 Depth Perception 1 Depth Perception! Contents: Contents: • The problem of depth perception • • WasDepth is lerning cues ? • • ZellulärerPanum’s Mechanismus fusional area der LTP • • UnüberwachtesBinocular disparity Lernen • • ÜberwachtesRandom dot Lernen stereograms • • Delta-LernregelThe correspondence problem • • FehlerfunktionEarly models • • GradientenabstiegsverfahrenDisparity sensitive cells in V1 • • ReinforcementDisparity tuning Lernen curve • Binocular engergy model • The role of higher visual areas Depth Perception 2 The problem of depth perception! Our 3 dimensional environment gets reduced to two-dimensional images on the two retinae. Viewing Retinal geometry image The “lost” dimension is commonly called depth. As depth is crucial for almost all of our interaction with our environment and we perceive the 2D images as three dimensional the brain must try to estimate the depth from the retinal images as good as it can with the help of different cues and mechanisms. Depth Perception 3 Depth cues - monocular! Texture Accretion/Deletion Accommodation (monocular, optical) (monocular, ocular) Motion Parallax Convergence of Parallels (monocular, optical) (monocular, optical) Depth Perception 4 Depth cues - monocular ! Position relative to Horizon Familiar Size (monocular, optical) (monocular, optical) Relative Size Texture Gradients (monocular, optical) (monocular, optical) Depth Perception 5 Depth cues - monocular ! ! Edge Interpretation (monocular, optical) ! Shading and Shadows (monocular, optical) ! Aerial Perspective (monocular, optical) Depth Perception 6 Depth cues – binocular! Convergence Binocular Disparity (binocular, ocular) (binocular, optical) As the eyes are horizontally displaced we see the world from two slightly different viewpoints.
    [Show full text]
  • Perception of Three-Dimensional Structure from Motion Richard A
    Review Anderson – Stereovision 17 Jones, J. and Malik, J. (1992) A computational framework for information in the stereo correspondence problem Vis. Res. 30, determining stereo correspondence from a set of linear spatial filters 1955–1970 Proc. Eur. Conference on Computer Vision, Genova, Italy, pp. 395–410 28 Smallman, H.S. and McKee, S.P. (1995) A contrast ratio constraint on 18 Anderson, B.L. and Nakayama, K. (1994) Towards a general theory of stereo matching Proc. R. Soc. London Ser. B 260, 265–271 stereopsis: binocular matching, occluding contours, and fusion 29 Rogers, B.J. and Bradshaw, M.F. (1993) Vertical disparities, differential Psychol. Rev. 101, 414–445 perspective and binocular stereopsis Nature 361, 253–255 19 Anderson, B.L. (1994) The role of partial occlusion in stereopsis Nature 30 Howard, I.P. and Rogers, B.J. (1995) Binocular Vision and Stereopsis, 367, 365–368 Oxford University Press 20 Anderson, B.L. and Julesz, B. (1995) A theoretical analysis of illusory 31 Gilchrist, A.L. (1994) Absolute versus relative theories of lightness contour formation in stereopsis Psychol. Rev. 102, 705–743 perception, in Lightness, Brightness and Transparency (Gilchrist, A.L., 21 Anderson, B.L. (1997) A theory of illusory lightness and transparency in ed.), pp. 1–34, Erlbaum monocular and binocular images: the role of contour junctions 32 Nakayama, K., Shimojo, S. and Ramachandran, V.S. (1990) Transparency: Perception 26, 419–453 relation to depth, subjective contours, luminance, and neon color 22 von Szilzy, A. (1921) Graefes Arch. Ophthalmol. 105, 964–972 spreading Perception 19, 497–513 23 Nakayama, K.
    [Show full text]
  • UC Berkeley UC Berkeley Electronic Theses and Dissertations
    UC Berkeley UC Berkeley Electronic Theses and Dissertations Title The role of focus cues in stereopsis, and the development of a novel volumetric display Permalink https://escholarship.org/uc/item/2nh189d9 Author Hoffman, David Morris Publication Date 2010 Peer reviewed|Thesis/dissertation eScholarship.org Powered by the California Digital Library University of California The role of focus cues in stereopsis, and the development of a novel volumetric display by David Morris Hoffman A dissertation submitted in partial satisfaction of the requirements for the degree of Doctor of Philosophy in Vision Science in the GRADUATE DIVISION of the UNIVERSITY OF CALIFORNIA, BERKELEY Committee in charge: Professor Martin S. Banks, Chair Professor Austin Roorda Professor John Canny Spring 2010 1 Abstract The role of focus cues in stereopsis, and the development of a novel volumetric display by David Morris Hoffman Doctor of Philosophy in Vision Science University of California, Berkeley Professor Martin S. Banks, Chair Typical stereoscopic displays produce a vivid impression of depth by presenting each eye with its own image on a flat display screen. This technique produces many depth signals (including disparity) that are consistent with a 3-dimensional (3d) scene; however, by using a flat display panel, focus information will be incorrect. The accommodation distance to the simulated objects will be at the screen distance, and blur will be inconsistent with the simulated depth. In this thesis I will described several studies investigating the importance of focus cues for binocular vision. These studies reveal that there are a number of benefits to presenting correct focus information in a stereoscopic display, such as making it easier to fuse a binocular image, reducing visual fatigue, mitigating disparity scaling errors, and helping to resolve the binocular correspondence problem.
    [Show full text]
  • Stereopsis Exam #1 5 Pts "-2.5 Any Computational Error, Error with Units, Etc"
    Stereopsis Exam #1 5 pts "-2.5 any computational error, error with units, etc" #2 2pts each all or nothing (tend to give credit unless answer is very far off) i. (answers for 'perspective projection' were uniformly weak, so I was unusually liberal with credit) iii must mention parallel lines iv. Partial credit unavoidable here. -.5 per missed vi. Must mention neighbors, or a neighborhood, etc. x. must say power per unit area, (Can use meters^2 instead of area, etc.) I write OK if their answer is less than great, but I'm not taking off credit #3 4 pts formula for convolution of filter H with image I. Don't care about if they put limits on the summation. 8 pts the actual output. 1 pt per pixel. (there are 8 pixels fully covered.) #4 9 pts -0 If they choose mask 1, regardless of reasoning' -4 if they choose mask 2 or mask 3, and give some reasonably plausible reasoning' -9 if they choose mask 4, or choose (2 or 3) with very bad reasoning 60 50 40 30 Series1 20 10 0 1 3 5 7 9 111315171921 Mark Twain at Pool Table", no date, UCR Museum of Photography Woman getting eye exam during immigration procedure at Ellis Island, c. 1905 - 1920 , UCR Museum of Phography Why Stereo Vision? • 2D images project 3D points into 2D: P Q P’’=Q’’ O • 3D Points on the same viewing line have the same 2D image: – 2D imaging results in depth information loss Stereo • Assumes (two) cameras. • Known positions.
    [Show full text]
  • Download Perspective Depth and Distance Free Ebook
    PERSPECTIVE DEPTH AND DISTANCE DOWNLOAD FREE BOOK Geoff Kersey | 96 pages | 30 Nov 2004 | Search Press Ltd | 9781844480142 | English | Tunbridge Wells, United Kingdom Creating the Illusion of Distance and Depth A thread of white or black silk or cotton laid upon the surface will serve your purpose. A coin seen upright and straight in front is a perfect circle ; a coin seen Perspective Depth and Distance down is a coin diminished Perspective Depth and Distance a coin foreshortened. Download as PDF Printable version. New York: Worth, Perspective Depth and Distance. July Learn how and when to remove this template message. Therefore it must be crystal-clear that such views offer no test of accurate perspective drawing. An optometrist or ophthalmologist will first asses your vision by measuring your visual acuity or the quality of your vision. Likewise, the cast Perspective Depth and Distance of the near horse will appear darker than that of the far horse. The pictures on the far wall exactly face the spectator, therefore they do not diminish. Did you not sometimes play at the game of hiding from your sight a house or a tree by putting your finger, or even a single hair, close to your eye? Open Preview See a Problem? But the rays from lamp or candle radiate on all sides and cannot be considered parallel. Was this page helpful? When the elbow is straight the arm is extended at its greatest length. All the pictures on the left-hand wall lie parallel with the wall and diminish as they recede. If you have embarked on an ambitious subject such as the drawing of a house, or a street, and you cannot ' make it look right '—" It won't go back," or equally possible " It won't come Perspective Depth and Distance "—then we must delve into the mysteries of perspective and apply common sense and plain argument.
    [Show full text]
  • Sensors for Impossible Stimuli May Solve the Stereo Correspondence Problem
    ARTICLES Sensors for impossible stimuli may solve the stereo correspondence problem Jenny C A Read1 & Bruce G Cumming2 One of the fundamental challenges of binocular vision is that objects project to different positions on the two retinas (binocular neuroscience disparity). Neurons in visual cortex show two distinct types of tuning to disparity, position and phase disparity, which are the results of differences in receptive field location and profile, respectively. Here, we point out that phase disparity does not occur in natural images. Why, then, should the brain encode it? We propose that phase-disparity detectors help to work out which feature in the left eye corresponds to a given feature in the right. This correspondence problem is plagued by false matches: regions of the image that look similar, but do not correspond to the same object. We show that phase-disparity neurons tend to be more strongly activated by false matches. Thus, they may act as ‘lie detectors’, enabling the true correspondence to be deduced by a process of elimination. Over the past 35 years, neurophysiologists have mapped the response to its spatial period. Physically, such a stimulus would correspond to a set properties of binocular neurons in primary visual cortex and elsewhere of transparent luminance gratings whose distance from the observer is a http://www.nature.com/nature in considerable detail. A mathematical model, the stereo energy function of their spatial frequency (Fig. 1a), a situation that never occurs model1, has been developed that successfully describes many of their naturally, and that we therefore characterize as ‘impossible’, even though properties.
    [Show full text]
  • Lecture 6 Depth Perception
    6 Space Perception and Binocular Vision Chapter 6 Space Perception and Binocular Vision Monocular Cues to Three-Dimensional Space Binocular Vision and Stereopsis Combining Depth Cues Development of Binocular Vision and Stereopsis Introduction Realism: The external world exists. Positivists: The world depends on the evidence of the senses; it could be a hallucination! • This is an interesting philosophical position, but for the purposes of this course, let’s just assume the world exists. Introduction Euclidian geometry: Parallel lines remain parallel as they are extended in space. • Objects maintain the same size and shape as they move around in space. • Internal angles of a triangle always add up to 180 degrees, etc. Introduction Notice that images projected onto the retina are non-Euclidean! • Therefore, our brains work with non- Euclidean geometry all the time, even though we are not aware of it. Figure 6.1 The Euclidean geometry of the three-dimensional world turns into something quite different on the curved, two-dimensional retina Introduction Probability summation: The increased probability of detecting a stimulus from having two or more samples. • One of the advantages of having two eyes that face forward. Introduction Binocular summation: The combination (or “summation”) of signals from each eye in ways that make performance on many tasks better with both eyes than with either eye alone. The two retinal images of a three- dimensional world are not the same! Figure 6.2 The two retinal images of a three-dimensional world are not the same Introduction Binocular disparity: The differences between the two retinal images of the same scene.
    [Show full text]