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Artificial Intelligence and Evolutionary Algorithms in Engineering Systems pp 285–295Cite as

Object Detection Using Robust Image Features

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Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 324))

Abstract

Object detection is a challenging field of research in computer vision. Research approaches have become increasingly popular in overcoming the challenges of object detection like occlusions, changes in scale, rotation, and illumination. Object detection methods that utilize RGB cameras are used to accurately identify objects in the real world, but they do not consider shape and three-dimensional characteristics of the object. Recognizing the objects in 3D is not an easy task for computers, like as in humans. Robust features like shape, color, size, etc., are necessary for 3D object detection for ensuring accuracy.

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Author information

Authors and Affiliations

  1. Department of Computer Science and Engineering, Amrita School of Engineering, Amrita Vishwa Vidyapeetham, Coimbatore, India

    Khande Bharath Kumar & D. Venkataraman

Authors
  1. Khande Bharath Kumar
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  2. D. Venkataraman
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Corresponding author

Correspondence to Khande Bharath Kumar .

Editor information

Editors and Affiliations

  1. Electrical & Electronics Engineering, Noorul Islam Centre for Higher Education, Kumaracoil, Tamil Nadu, India

    L. Padma Suresh

  2. Electrical and Electronics Engineering, SRM Engineering College, Kattankulathur, Tamil Nadu, India

    Subhransu Sekhar Dash

  3. Electrical Engineering, IIT Delhi, New Delhi, Delhi, India

    Bijaya Ketan Panigrahi

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Kumar, K.B., Venkataraman, D. (2015). Object Detection Using Robust Image Features. In: Suresh, L., Dash, S., Panigrahi, B. (eds) Artificial Intelligence and Evolutionary Algorithms in Engineering Systems. Advances in Intelligent Systems and Computing, vol 324. Springer, New Delhi. https://doi.org/10.1007/978-81-322-2126-5_32

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  • DOI: https://doi.org/10.1007/978-81-322-2126-5_32

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  • Publisher Name: Springer, New Delhi

  • Print ISBN: 978-81-322-2125-8

  • Online ISBN: 978-81-322-2126-5

  • eBook Packages: EngineeringEngineering (R0)

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