Improvised Filter Design for Depth Estimation from Single Monocular Images

  • Aniruddha Das
  • Vikas Ramnani
  • Jignesh Bhavsar
  • Suman K. Mitra
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5909)


Depth Estimation poses various challenges and has wide range applications. Techniques for depth prediction from static images include binocular vision, focus-defocus, stereo vision and single monocular images unfortunately not much attention has been paid on depth estimation from single image except [1]. We have proposed a method for depth estimation from single monocular images which is based on filters that are used to extract key image features. The filters used have been applied at multiple scales to take into account local and global features. Here an attempt is made to reduce the dimension of feature vector as proposed in [1]. In this paper we have optimized the filters used for texture gradient extraction. This paper also introduces a prediction algorithm whose parameters are learned by repeated correction. The new methodology proposed provides an equivalent quality of result as in [1].


Feature Vector Stereo Vision Binocular Vision Depth Estimation Texture Gradient 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Aniruddha Das
    • 1
  • Vikas Ramnani
    • 1
  • Jignesh Bhavsar
    • 1
  • Suman K. Mitra
    • 1
  1. 1.Dhirubhai Ambani Institute of Information and Communication TechnologyGandhinagarIndia

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