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Books on the topic 'Machine vision for robot guidance'

Author: Grafiati
Published: 28 June 2021
Last updated: 10 February 2022

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1

Vernon, David. Machine vision: Automated visual inspection and robot vision. London: Prentice Hall, 1991.

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2

Machine vision: Automated visual inspection and robot vision. New York: Prentice Hall, 1991.

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3

Kanatani, Kenʼichi. Geometric computation for machine vision. Oxford: Clarendon Press, 1993.

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4

Pomerleau, Dean A. Neural Network Perception for Mobile Robot Guidance. Boston, MA: Springer US, 1993.

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5

Miller, Richard Kendall. Color machine vision: A market forecast and applications assessment. Madison, GA: SEAI Technical Publications, 1986.

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6

Heytler, Peter. Machine vision: A Delphi forecast to 1990. Ann Arbor, MI: Automated Vision Association of RIA, 1986.

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7

Heikkilä, Tapio Arturri. A model-based approach to high-level robot control with visual guidance. Espoo, [Finland]: Technical Research Centre of Finland, 1990.

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8

Bajcsy, Ruzena. Assembly via disassembly: A case in machine perceptual development. Philadelphia, PA: Dept. of Computer and Information Science, School of Engineering and Applied Science, University of Pennsylvania, 1989.

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9

Center, Langley Research, ed. A guidance scheme for automated tetrahedral truss structure assembly based on machine vision. Hampton, Va: National Aeronautics and Space Administration, Langley Research Center, 1996.

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10

Takeo, Kanade, ed. Three-dimensional machine vision. Boston: Kluver Academic Publishers, 1987.

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11

Vernon, David. Machine Vision. Prentice Hall Europe (a Pearson Education company), 1991.

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12

Robot and Machine Vision Application Profiles. Robotic Industries Assn, 1992.

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13

AVA machine vision glossary. Dearborn, Mich. (P.O. Box 1366, Dearborn 48121): Automated Vision Association, 1985.

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14

1938-, Zuech Nello, ed. Machine vision: Capabilities for industry. Dearborn, Mich: Machine Vision Association of SME, Publications Development Dept., Marketing Division, 1986.

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15

Brady. Machine Vision: Advent Intelligent Robots. Addison-Wesley Professional, 1986.

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16

Brady. Machine Vision: Advent Intelligent Robots. Addison-Wesley Pub (Sd), 1986.

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17

Report: Machine vision activity in Europe, 1986. Society of Manufacturing Engineers, Marketing Division, 1987.

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18

K, West James, and Society of Manufacturing Engineers, eds. Report: Machine vision activity in Europe, 1986. Dearborn, Mich: Society of Manufacturing Engineers, Marketing Division, 1987.

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19

Robot Learning by Visual Observation. Wiley & Sons, Incorporated, John, 2017.

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20

Janabi-Sharifi, Farrokh, and Aleksandar Vakanski. Robot Learning by Visual Observation. Wiley & Sons, Incorporated, John, 2017.

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21

L, Boyer Kim, Stark Louise, and Bunke Horst, eds. Applications of AI, machine vision and robotics. Singapore: World Scientific, 1993.

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22

Neural Network Perception for Mobile Robot Guidance. Springer My Copy UK, 1993.

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23

(Editor), Kevin W. Bowyer, H. I. Christensen (Editor), and Horst Bunke (Editor), eds. Active Robot Vision: Camera Heads, Model Based Navigation and Reactive Control (Series in Machine Perception and Artificial Intelligence, Vol 6). World Scientific Pub Co Inc, 1993.

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24

(Editor), Bruno Apolloni, Ashish Ghosh (Editor), Ferda Alpaslan (Editor), Lakhmi C. Jain (Editor), and Srikanta Patnaik (Editor), eds. Machine Learning and Robot Perception (Studies in Computational Intelligence) (Studies in Computational Intelligence). Springer, 2005.

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25

J, Tsikos Constantine, and United States. National Aeronautics and Space Administration., eds. Assembly via disassembly: A case in machine perceptual development. Philadelphia, PA: Dept. of Computer and Information Science, School of Engineering and Applied Science, University of Pennsylvania, 1989.

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26

Company, Market Intelligence Research, ed. World machine vision market and competitive strategies: Great opportunities exist for the nimble market participant. Mountain View, CA: MIRC USA, 1991.

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27

Corporation, Market Intelligence Research, ed. World machine vision system, component, and software markets: Innovative, affordable, and reliable solutions lead to billion-dollar industry. Mountain View, CA: Market Intelligence USA, 1992.

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28

Little, Max A. Machine Learning for Signal Processing. Oxford University Press, 2019. http://dx.doi.org/10.1093/oso/9780198714934.001.0001.

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Abstract:
Digital signal processing (DSP) is one of the ‘foundational’ engineering topics of the modern world, without which technologies such the mobile phone, television, CD and MP3 players, WiFi and radar, would not be possible. A relative newcomer by comparison, statistical machine learning is the theoretical backbone of exciting technologies such as automatic techniques for car registration plate recognition, speech recognition, stock market prediction, defect detection on assembly lines, robot guidance and autonomous car navigation. Statistical machine learning exploits the analogy between intelligent information processing in biological brains and sophisticated statistical modelling and inference. DSP and statistical machine learning are of such wide importance to the knowledge economy that both have undergone rapid changes and seen radical improvements in scope and applicability. Both make use of key topics in applied mathematics such as probability and statistics, algebra, calculus, graphs and networks. Intimate formal links between the two subjects exist and because of this many overlaps exist between the two subjects that can be exploited to produce new DSP tools of surprising utility, highly suited to the contemporary world of pervasive digital sensors and high-powered and yet cheap, computing hardware. This book gives a solid mathematical foundation to, and details the key concepts and algorithms in, this important topic.
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