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Computer Vision Overview

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3-D Computer Vision

Abstract

Computer vision is an information subject/discipline that uses computers to realize the functions of human vision system (HVS). This book mainly introduces the high-level content of computer vision, which can be used as a textbook for in-depth study of computer vision. This chapter will introduce the characteristics of human vision, the brightness properties of vision, the spatial properties of vision, and the temporal properties of vision, as well as makes some discussions on visual perception. This chapter will discuss the research purpose, research tasks, and research methods of computer vision. It also introduces the visual computational theory proposed by Marr in more detail. Moreover, a combined presentation for some improvement ideas are provided. This chapter will give a general introduction to the 3-D vision system that obtains 3-D spatial information and realizes the understanding of the scene. It compares and discusses the layers of computer vision and image technology, leading to the main content of this book. Finally, this chapter will present the structure of the book and gives the brief summaries of each chapter in this book.

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Author information

Authors and Affiliations

  1. Department of Electronic Engineering, Tsinghua University, Beijing, China

    Yu-Jin Zhang

Authors
  1. Yu-Jin Zhang
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Self-Test Questions

Self-Test Questions

The following questions include both single-choice questions and multiple-choice questions, so each option must be judged.

  1. 1.1

    Human Vision and Characteristics

    1. 1.1.1

      Compare vision and other related concepts (·).

      1. (a)

        Vision and computer vision both perceive the objective world subjectively.

      2. (b)

        Vision and image generation both generate images from the abstract description of the scene.

      3. (c)

        The computer vision system and the machine vision system are comparable to the human vision system.

      4. (d)

        The vision process and the computer vision process are completely deterministic and predictable.

    [Hint] Consider the difference between vision and other concepts.

    1. 1.1.2

      Mach band effect (·).

      1. (a)

        Can be explained by simultaneous contrast

      2. (b)

        Shows the same fact as simultaneous contrast

      3. (c)

        Depends on the brightness adaptation level of the human visual system

      4. (d)

        Indicates that the actual brightness distribution on the strip will be affected by the subjective brightness curve

    [Hint] The Mach band effect shows that the brightness that people perceive is not only related to the light intensity of the scene.

    1. 1.1.3

      Subjective brightness (·).

      1. (a)

        Is only related to scene brightness

      2. (b)

        Is proportional to the illuminance of the object

      3. (c)

        Is possible independent to the absolute value of the object brightness

      4. (d)

        Determines the overall sensitivity of the human visual system

    [Hint] Subjective brightness refers to the brightness of the observed object, which is judged by the human eyes according to the intensity of the light stimulation of the retina.

  2. 1.2

    Computer Vision Theory and Model

    1. 1.2.1

      Computer vision (·).

      1. (a)

        Whose goal is to uncover all the mysteries of the visual process.

      2. (b)

        The research method refers to the structural principles of the human visual system.

      3. (c)

        It is a means to explore the working mechanism of human brain vision.

      4. (d)

        It is realized with the help of the understanding of the human visual system.

    [Hint] Analyze according to the definition of computer vision.

    1. 1.2.2

      Marr’s visual computational theory believes that (·).

      1. (a)

        The visual process is far more complicated than human imagination

      2. (b)

        The key to solve visual problems is the representation and processing of information

      3. (c)

        To complete the visual task, all the works must be combined

      4. (d)

        All visual information problems can be computed with modern computers.

    [Hint] See the five points of Marr’s visual computational theory.

    1. 1.2.3

      In the improved visual computational framework shown in Fig. 1.7, (·).

      1. (a)

        The image acquisition module provides the basis for qualitative vision.

      2. (b)

        The image acquisition module provides the basis for selective vision.

      3. (c)

        The vision purpose module should be constructed based on the purpose of active vision.

      4. (d)

        The function of the high-level knowledge module is to feed back the later result information to the early processing.

    [Hint] Analyze the shortcomings of Marr’s theory.

  3. 1.3

    Three-Dimensional Vision System and Image Technology

    1. 1.3.1

      According to the 3-D vision system flow chart, (·).

      1. (a)

        The 3-D reconstruction must use motion information.

      2. (b)

        The objective analysis of the scene is based on the interpretation of the scene.

      3. (c)

        Decisions can only be made based on the interpretation and understanding of the scene.

      4. (d)

        To obtain motion information, video images must be collected.

    [Hint] Analyze the connection between each step.

    1. 1.3.2

      For image understanding, (·).

      1. (a)

        Its abstraction is high, its operand is the object, and its semantic level is high level.

      2. (b)

        Its abstraction is high, its operand is the symbol, and its semantic level is middle level.

      3. (c)

        Its abstraction is high, its amount of data is small, and its semantic level is high level.

      4. (d)

        Its abstraction is high, its amount of data is large, and its operand is the symbol.

    [Hint] Refer to Fig. 1.9.

    1. 1.3.3

      Which of the following image technique(s) is/are image understanding technologies? (·).

      1. (a)

        Image segmentation

      2. (b)

        Scene restoration

      3. (c)

        Image matching and fusion

      4. (d)

        Extraction and analysis of object characteristics

    [Hint] Consider the input and output of each technology.

  4. 1.4

    Overview of the Structure and Content of This Book

    1. 1.4.1

      In the following content, the five modules in this book in turn are (·).

      1. (a)

        3-D image acquisition, video and motion, binocular stereo vision, scenery matching, scene interpretation

      2. (b)

        3-D image acquisition, binocular stereo vision, monocular multi-image restoration, scene matching, scene interpretation

      3. (c)

        Camera calibration, moving object detection and tracking, binocular stereo vision, monocular and single image restoration, and spatial-temporal behavior understanding

      4. (d)

        Camera calibration, video and motion, monocular multi-image restoration, moving object detection and tracking, and spatial-temporal behavior understanding

    [Hint] Refer to Fig. 1.10.

    1. 1.4.2

      Among the following statements, the correct one/ones is/are (·).

      1. (a)

        3-D image is a kind of depth image

      2. (b)

        Background modeling is a technique for detecting and tracking moving objects in videos

      3. (c)

        Recovering the shape of the object from the tonal change of the object surface is a method of recovering the scene by using multiple monocular images

      4. (d)

        The bag-of-words/bag of feature model is a model of spatial-temporal behavior understanding

    [Hint] Consider the content discussed in each chapter separately.

    1. 1.4.3

      Among the following statements, the incorrect one/ones is/are (·).

      1. (a)

        The region-based binocular stereo matching technology is a relatively abstract matching technology

      2. (b)

        It is possible to use a non-linear camera model for camera calibration

      3. (c)

        The classification of actions is a technique for detecting and tracking moving objects

      4. (d)

        The method of obtaining structure from motion based on the optical flow field is a method of recovering the scene by using multiple monocular images

    [Hint] Refer to the introduction in the overview of each chapter.

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Zhang, YJ. (2023). Computer Vision Overview. In: 3-D Computer Vision. Springer, Singapore. https://doi.org/10.1007/978-981-19-7580-6_1

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  • DOI: https://doi.org/10.1007/978-981-19-7580-6_1

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  • Print ISBN: 978-981-19-7579-0

  • Online ISBN: 978-981-19-7580-6

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