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Generation of Future Image Frames for an Image Sequence

  • Nishchal K. Verma
  • Ankan Bansal
  • Shikha Singh
Part of the Communications in Computer and Information Science book series (CCIS, volume 276)

Abstract

A way of generating the future frames of an image sequence is presented. In this paper first we present a way of predicting the future positions of rigid moving objects in a given sequence of images from a static camera.The moving object is first extracted from the images and its centroid is found as a measure of its position. These positions are used to find the future positions of the object using Artificial Neural Network models. This approach is found to predict the positions with very good accuracy. Next we give an algorithm for generating complete future image frames. The optical flow of the images is calculated to find the velocity of each pixel. Time series of the velocities are constructed for each pixel for both dimensions. A separate neural network model is used to predict the future velocities of each pixel and the pixels are then mapped to their new positions. Two different types of neural network models(sigmoidal function networks and radial basis function networks) have been used.

Keywords

Neural Network Image Sequence Test Image Neural Network Model Optical Flow 
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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References

  1. 1.
    Verma, N.K., Pal, N.R.: Prediction of satellite images using fuzzy rule based Gaussian regression. In: 2010 IEEE 39th Applied Imagery Pattern Recognition Workshop (AIPR), pp. 1–8 (October 2010)Google Scholar
  2. 2.
    Verma, N.K., Tamrakar, P., Agrawal, S.: Generating Future Satellite Image Frame using Artificial Neural Network. In: International Conference on Image and Video Processing and Computer Vision (IVPCV 2010), pp. 158–164 (July 2010)Google Scholar
  3. 3.
    Edwards, T., Tansley, D.S.W., Davey, N., Frank, R.J.: Traffic Trends Analysis using Neural Networks. In: Proceedings of the International Workshop on Applications of Neural Networks to Telecommunications 3 (1997)Google Scholar
  4. 4.
    Dorffner, G.: Neural Networks for Time Series Processing. Neural Network World (1996)Google Scholar
  5. 5.
    White, H.: Economic Prediction using Neural Networks: the case of IBM daily stock returns. In: Proceedings of IEEE International Conference on Neural Networks, San Diego, Calif, USA (1998)Google Scholar
  6. 6.
    Paras, S.M., Kumar, A., Chandra, M.: A Feature Based Neural Network Model for Weather Forecasting. In: World Academy of Science, Engineering and Technology (2007)Google Scholar
  7. 7.
    Horn, B.K.P., Schunck, B.G.: Determining optical flow. Artificial Intelligence 17, 185–203 (1981)CrossRefGoogle Scholar
  8. 8.
    Gibson, J.J.: The Perception of the Visual World. Houghton Miffin (1950)Google Scholar
  9. 9.
    Ma, L.L., Xu, X.S.: RBF Network-Based Chaotic Time Series Prediction and its Application in Foreign Exchange Market. In: Proceedings of the International Conference on Intelligent Systems and Knowledge Engineering (ISKE), Chengdu, China (2007)Google Scholar
  10. 10.
    Verma, N.K., Shimaila: Generation of Future Image Frames using Adaptive network based fuzzy Inference System on Spatiotemporal Framework. Accepted in Applied Imagery Pattern Recognition Workshop, AIPR (2012)Google Scholar
  11. 11.
    Verma, N.K.: Future Image Generation Using Artificial Neural Network with Selected Features. Accepted in Applied Imagery Pattern Recognition Workshop, AIPR (2011)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Nishchal K. Verma
    • 1
  • Ankan Bansal
    • 1
  • Shikha Singh
    • 2
  1. 1.Indian Institute of Technology KanpurKanpurIndia
  2. 2.Banasthali UniversityRajasthanIndia

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